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Abstract—With the aim to promote sustainable environment, many countries have been supporting the use of renewable generation by introducing strict policies and providing subsidies to producers. Power systems, consequently, have been experiencing high penetration of renewable power. There exists a challenge to maintain a desired level of reliability of power systems due to the intermittent nature of renewable power generation. This study aims to evaluate the reliability of power systems with significant penetration of renewable power generation from wind turbines by testing two reliability indices using MATLAB. A case-based analysis is applied to a modified version of the New England power system. Results show that renewable generators can improve the reliability of the power system but not to the extent to which the use of conventional generators can. The results imply that the number of generators available is directly proportional to the reliability of the system; in other words, with higher generation dispersion degree, the power grid would be more reliable.
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Abstract—In recent years, power systems have gradually become electronically powered, the quality of power in the distribution network presents new features. Although power electronics technology brings qualitative leap to renewable energy, microgrid technology, distributed power supplies, electric vehicles, etc,it also affects the power quality of the distribution network. Discussion on the new characteristics of power quality in distribution network, This paper discusses the new characteristics of power quality of distribution network, such as rapid voltage fluctuation, frequent three-phase unbalance, subsynchronous harmonic caused by disturbance frequency variation, and Ultra harmonics. At the same time, the governance recommendations are elaborated. Combined with existing problems, we will look forward to the development direction of power quality in distribution networks in the future.
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Abstract—For system with uncertain noise variances, two kinds of guaranteed cost (GC) robust centralized fusion (CF) and weighted measurement fusion (WMF) Kalman predictors are presented based on minimax robust estimation principle and parameterization of perturbances of uncertain noise variances. Both the maximal lower bound (MLB) and minimal upper bound (MUB) of accuracy deviations are given. Based on the information filter, the equivalence between robust WMF and CF Kalman predictors are proved. Applying the Lyapunov equation (LE) approach, the GC robustness is proved. A simulation example uesed to tracking system shows the effectiveness of the proposed results.
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Abstract—In order to solve the impact of test point offset on the grounding network corrosion fault diagnosis results, Simulate offset conditions using PSCAD, Comparative analysis of fault diagnosis results of fully measurable branches. Find the law that implies it and propose a method of calculating the test points at different offset levels. According to the method, the original large-change sensitivity nonlinear fault diagnosis mathematical model and the hybrid particle swarm optimization algorithm are improved. Simulation experiments show that the improved fault diagnosis algorithm is accurate and effective for solving the grounding grid corrosion diagnosis problem of test point offset.
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Abstract—Different types of lightning protection system (LPS) are being used for wind turbine blades (WTBs). However, still severe damages due to lightning strikes still happen frequently, which costs huge losses. In this paper, a review on experimental study of WTB lightning protection is given. Experiments were performed to study the lightning attachment characteristics of glass fiber reinforced polymer (GFRP) composite wind turbine blade. Experiments using three specimens of WTB the effect of wind turbine blade orientation, lateral distances (LDs) between the wind turbine blade and the lightning downward leader, polarity of lightning strike, wind turbine blade rotation speed, receptor type and marine environment on lightning interception failure of LPS in different situation were studied. It was observed that discharge attachment paths under positive and negative lightning strikes were quite different, and the positive discharges are much more dangerous to wind turbine blade than the negative ones. The LD between downward leader and WTB is a key factor which influences the interception efficiency. The experiment results can be useful to improve the optimal design of LPS of WTBs.
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Abstract—Different type of composite material and metallic mesh are being used for wind turbine blade (WTB). In this paper, experiments were conducted to study the influence of material and metallic mesh on lightning attachment manner. Experiments using two specimens of wind turbine blade under different air gap were conducted to investigate the effect of carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) on the attachment process. Wind turbine blade specimen I is made by CFRP with tip receptor and copper mesh as a lightning protection system (LPS), and wind turbine blade specimen II is made by GFRP has LPS including tip receptor made of aluminum and copper conductor inside WTB. The results between CFRP and GFRP are also compared which will be useful to improve the quality of wind turbine blade material and design of LPS for wind turbine blade.
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Absrtact—The development and application of high-tech equipments, from equipment design, general equipment quality characteristics, aircraft information design to maintenance strategy and supporting resources, have a significant impact on maintenance and support. The improvement of maintenance and support strategy and capability in grass-roots level is an urgent problem to be solved in the battle department. This paper focuses on the maintenance and support simulation modeling of grass-roots level and task division optimization problem, first of all, using the modular modeling ideas to draw the basic-level maintenance and support scene operation flow chart, constructs the basic-level maintenance and support model parameters system, and then establishes the maintenance and support simulation model of grass-roots level based on the CPN. And the optimal method of task division based on genetic algorithm is explored. In order to optimize the process of maintenance and support in the grass-roots level, regard sortie generation rate and operational availability as objective functions. The optimal results can provide the effective theoretical support for the aviation combat department to enhance the operational supporting ability.
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Abstract—Defects will reduce the performance and the quality of the wheels in different degree. Therefore, accurate identification of defects can separate broken wheels and reduce cost. Due to the uneven illumination and the high reflectivity of the wheel, the images of the wheels taken by the camera tend to produce more reflective areas. These reflective areas increase the misclassification rate of the images in the segmentation process. In order to reduce background interference and make the segmentation more accurate, this paper uses the improved Otsu algorithm that limits the gray range in recognition process to segment the defects. The arithmetic in this paper can also speed up the Otsu by the dichotomy. Experimental results show that the improved arithmetic used in this paper can find a better segmentation threshold and has good real-time performance.
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Abstract—The electromagnetic power generated by the control and utilization of the electromagnetic spectrum has become a "high point" for everyone in the field of information technology, and big data is an important means to provide spectrum information. This paper analyzes the Hadoop big data infrastructure and then studies its application in the electromagnetic spectrum situation analysis system. The system provides a basis for the handling of emergencies, multi-source spectrum information fusion, and comprehensive situational decision-making, and lays a foundation for improving the management and control of spectrum resources.
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Abstract—The electric cylinder hexapod robot is simulated to study the movement of the hexapod robot, and to improve the design efficiency and save the design cost. The hexapod robot model is built by 3D modeling software SOLIDWORKS, and it is imported into ADAMS for simulation. The movement and foot force of the hexapod robot are analyzed to verify the feasibility of the design. This simulation provides a theoretical basis for the physical design.
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Abstract—The satisfiability(SAT) problem of large-scale propositional logic formulas has gradually become the mainstream of solutions, and some related algorithms and solvers have emerged. MiniSat is an efficient, open source solver. Its data structure, solution logic and algorithm design are also the basis of current top solvers such as Maple and Glucose. Based on the interpretation of MiniSat, this paper implements the solver bfSAT. bfSAT improves the order of clauses in the process of unit propagation, mainly for prioritizing binary clauses, making it faster to find conflict clauses, get learning clauses and backtrack. bfSAT is a solver based on algorithms and techniques implemented in MiniSat. Therefore, the solver is tested with the SAT competition 2017 benchmark data. Finally, this paper gives some optimization strategies for the world's top solvers.
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Abstract—In the wireless burst communication of Traffic Alert and Collision Avoidance System (TCAS) and mode S airborne transponders, it is necessary to meet the needs of large dynamic range and high sensitivity. In order to meet the above needs, this paper proposes an adaptive truncation signal processing algorithm to solve the problem of dynamic range, sensitivity, and threshold setting. And this method is certified by data simulation analysis. The results show that the error rate of the adaptive truncation algorithm is the same as that of full-precision signal processing when the effective number of bits is greater than 4 bits. However, due to the fixed bit width in the signal processing flow, the adaptive truncation algorithm has an advantage in resource consumption. In summary, this paper adopts adaptive truncation signal processing algorithm.
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Abstract—The satisfiability(SAT) of the propositional logic formula is the most classic NP-hard problem and the earliest proven NP-complete problem, making an invaluable contribution to both industry and research. Based on the interpretation of MiniSat's CDCL solver and related technologies, this paper implements a solver sfSAT for the priority processing of small-scale clauses in the process of unit propagation. Through experiments, this paper analyzed and compared sfSAT, bfSAT(the author's improved solver) and MiniSat data using the SAT competition 2017 benchmark.
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Abstract—Modal Energy Analysis (MODENA) is a new method which focuses on structural-acoustic coupling problems in recent years. It can predict the energy response of the system quickly and accurately. An algorithm that integrates Finite Element (FE) and MODENA is proposed to solve the structural-acoustic coupling analyses of complex dynamic systems when modes information cannot be solved by analytical ways. The proposed algorithm is applied to a complex structural-acoustic coupling system. Results show that the FE-MODENA method gives a fine estimation to the structural-acoustic response analyses compared with Dual Modal Formulation results, especially when considering multi-point random excitation.
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Abstract—The Weighted MAX-SAT problem (WMS) and Non-weighted Partial MAX-SAT problem (PMS) are two important branches of Maximum Satisfiable Problem (MAXSAT). The Weighted Partial MAX-SAT (WPMS) problem is a combination of WMS and PMS, which has more important significance in practical applications. In recent years, with the significant breakthroughs of the research on the Configuration Checking (CC) strategy of WMS and PMS problems by Shaowei Cai and others, some advanced solving algorithms such as CCLS [1] and Dist [2] have emerged. On this basis, the CCEHC [3] solving algorithm with the hard clause weighting scheme and biased random walk strategy that specially designed for WPMS instances have achieved more efficient performance. However, the CCEHC solving strategy overly considers the effect of the hard clauses in the solution process. Although the strategies make the solution result closer to the feasible solution, it is difficult to guarantee the optimality of the solution, i.e., minimize total unsatisfied soft clause weight. So, this paper presents an improved CCEHC-Plus solving algorithm based on CCEHC [3]. CCEHC-Plus reconsiders the effect of soft clauses in the solution process and optimizes the original selection strategy in CCEHC. Secondly, during the search steps, the algorithm adjusts the weight of some highweighted soft clauses which are hard to be satisfied reversibly and optimizes the original weighting scheme. CCEHC-Plus is also optimized for the random walk strategy. A large number of experimental tests show that compared with the CCEHC algorithm, the CCEHC-Plus algorithm can conspicuously improve the quality of the solution in solving industrial and crafted WPMS instances.
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Abstract—While enjoying the convenience brought by mobile phones, users have been exposed to high risk of private information leakage. It is known that many applications on mobile devices read private data and send them to remote servers. However how, when and in what scale the private data are leaked are not investigated systematically in the real-world scenario. In this paper, a framework is proposed to analyze the usage data from mobile devices and the traffic data from the mobile network and make a comprehensive privacy leakage detection and privacy inference mining on a large scale of real-world mobile data. Firstly, this paper sets up a training dataset and trains a privacy detection model on mobile traffic data. Then classical machine learning tools are used to discover private usage patterns. Based on our experiments and data analysis, it is found that i) a large number of private information is transmitted in plaintext, and even passwords are transmitted in plaintext by some applications, ii) more privacy types are leaked in Android than iOS, while GPS location is the most leaked privacy in both Android and iOS system, iii) the usage pattern is related to mobile device price. Through our experiments and analysis, it can be concluded that mobile privacy leakage is pervasive and serious.
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Abstract—In order to realize the close and automatic tracking of ground targets by fixed-wing UAV, a directional overhead tracking ground target guidance method for fixedwing UAV is proposed. By establishing the mathematical model of fixed-wing UAV directional overhead tracking ground target and analyzing its motion relationship, the guidance law of fixed-wing UAV directional overhead tracking ground target is designed based on the Dubins model of UAV, and the simulation validation is carried out for different moving ground targets. The simulation results show that the guidance method can realize the directional overhead tracking of fixed-wing UAV to ground targets in different motion states, and has good tracking performance.
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Abstract—The era of big data brings explosive network traffic. Classification of network traffic can enhance the controllability of the network, help relevant personnel to grasp the distribution of traffic on the network, help network operators optimize service quality and prevent various cybercrime. However, traditional network traffic classification methods cannot satisfy the requirements of large-scale network traffic classification, and the current machine learning-based algorithms for network traffic classification are difficult to complete the high-speed parallelized real-time traffic classification task in the actual network environment. In this paper, a method is proposed to optimize the Convolutional Neural Networks (CNN) model in parallel using the Spark platform, and the Spark Streaming framework is put forward to implement the requirements for real-time classification of network traffic. Besides, the performance of our method is evaluated. The experimental results show that the proposed method has good real-time performance while ensuring high classification accuracy, and it can implement the task of high-speed parallel network flow real-time classification.
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Abstract—In this paper, an efficient algorithm based on kernel semi-supervised discriminant analysis for communication emitter identification is proposed. This algorithm uses square integral bispectra to extract the bispectra features from the communication emitter signal as its fingerprint. Simultaneously, to improve the communication emitter identification results, the kernel semi-supervised discriminant analysis algorithm maps high-dimensional bispectra feature data to a low-dimensional subspace and then identifies this data in that subspace by its nonlinear manifold information and partial label information. Sample data of ten FM radio stations with the same manufacturer, batch and type are used in an identification experiment. The experiment result proves that, despite there being fewer labeled training samples, the proposed method still has high recognition performance.
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Abstract—This paper studied the voice conversion algorithms based on Gaussian mixture model and non-negative matrix factorization, and selected non-negative matrix factorization algorithm to implement voice conversion on embedded system. The whole system is divided into two phases: the training phase and the conversion phase. The training phase is completed on the PC and the conversion phase is completed on the STM32F407 embedded development board. Experiments are conducted with datasets acquired by MEMS microphones and a suitable evaluation system is established. The results show that the embedded voice conversion system works effectively.
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Abstract—The traditional Region Growing (RG) algorithm is a semi-automatic image segmentation algorithm that requires manual selection of seed points, manual setting of thresholds, which may cause the cavity and over-segmentation while deals with the uneven and undivided image for noise and grayscale. Therefore, the classical Otsu method is adopted to find the optimal threshold in HSV space to obtain the initial binarized segmentation image. Then, the Adaptive Region Growing (ARG) algorithm is applied to find the image edge. To be specific, the initial seed point is confirmed automatically through histogram firstly. Secondly, the threshold of growth condition is determined according to the average similarity of image. Finally, the image is grown into the contour edges. This image is mixed together with the initial Otsu segmentation image can obtain the final processed image. Experimental results show that the proposed algorithm has strong anti-interference, which can effectively reduce the mis-segmentation rate.
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Abstract—PMSM has the characteristics of nonlinearity, strong coupling, and time-varying parameters. The parameters of PI controller cannot make self-tuning with the change of system state. This paper proposes a new fuzzy control rule. The proportion factor can be adjusted online. The simulation results show that fuzzy PI controller speed regulation system with improved fuzzy rules has faster response speed, small overshoot and strong anti-interference ability. The control system with improved rules has better control performance.
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Abstract—Traditional vehicle lane change decision model based on the mathematical model method and the logical model method is difficult to accurately describe the complete lane changing process and can’t reflect a series of psychological and physiological reactions of the driver. And the subjective experience of the formulation model leads to the lack of certain rationality of this kind of models. This paper proposes a lane change decision model based on ensemble learning from the perspective of data driving. Based on the analysis of the decision-making factors of discretionary lane change of manual driving vehicles and the behavior characteristics of vehicles in the course of lane-changing execution, the identification method of key point in the process of autonomous lane change is proposed. The vehicle lane change behavior decision model based on random forest is established by selecting appropriate variables as decision factors. Then, the vehicle data of a driving unit is extracted from the NGSIM dataset. In order to correct the errors in the NGSIM data to obtain more accurate vehicle movement data, symmetric exponential moving average algorithm is used to smooth the extracted sample data. Finally, the random forest lane-changing decision-making model is trained and tested with the pre-processed sample data, and good prediction results and fitting degree are obtained.
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Abstract—A relation extraction approach based on sequence labeling has been proposed to extract entities and relation triples jointly. That approach does not take triple overlapping into consideration. In this paper, the approach is improved to become more friendly to overlapped triples. First, the sequence labeling model is extended to make it possible to predict more than one tags for a token. And all gold tags of a token are used for supervision. Then a more effective algorithm is designed to construct triples. Experiments on CoNLL04 dataset show that our approach achieves a much better overall performance than our baselines.
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Abstract—LP norm (P<1) has the feature of sparsity. In view of the sparsity of the characteristic band in the near infrared spectrum, a feature extraction algorithm based on L1/2 regular is proposed in this paper. According to the nonconvexity of L1/2 norm, an iterative algorithm is presented. The L1/2 regular is transformed into a series of L1 regular for iteration calculation. In this paper, based on the near infrared spectral data, L1/2 regular, PCR and PLS algorithm are used to detect oil compositions. And then the PLS algorithm and PCR algorithm are compared, the result of which shows that PCR and PLS algorithm have the similar effect and there will be a phenomenon of over fitting, as the number of characteristic band increase. Besides, the modeling sets error decreases and the prediction sets error increases. With LP algorithm, both modeling sets and predictive sets error can be small, and extract the characteristic band effectively. The experiments show that the L1/2 regular has a good application value for the extraction of characteristic band of spectrum.
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Abstract—The classification of microarray data has positive significance for the judgment of cancer and the determination of clinical programs. However, the high dimensionality and small sample characteristics of the microarray data has brought classification a difficult problem. Aiming at the feature selection problem in microarray data classification, a feature selection algorithm based on artificial bee colony algorithm and genetic algorithm is proposed to solve the dimensionality disaster problem in microarray data classification. Finally, the feature subsets obtained by the algorithm are combined with the SVM(Support Vector Machine)classifier to apply to the six published microarray datasets. The experimental results show that the feature subsets obtained based on the proposed scheme can significantly improve the classification performance.
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Abstract—The warship is a very complicated system with many equipments and many excitation sources which can produce transient noise. Transient noise is mainly transmitted to the hull through elastic installation, pedestal and other supporting structures and non-supporting structures such as pipelines and cables. The vibration energy passes through the hull to the acoustic field and forms the radiated noise. Therefore, how to quickly and effectively find out the excitation source of ship transient noise has important military significance. It is difficult to distinguish the transmission channel of the excitation source of the transient noise because of the variety of ways of generating the transient noise and the interference of the mechanical noise. This makes the problem of excitation source discrimination become one of the important research directions of warship transient noise analysis. This paper aims to solve the problem of identifying the main transmission path of the excitation source. In this paper, through the study of principal component decomposition, instantaneous information similarity and so on, combined with the physical model of vibration transfer path, the main transfer path discrimination method suitable for the warship transient noise excitation source is established, and the validity of the method is verified by theoretical simulation.
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Abstract—In order to make the unmanned drone with energy limitation under the biasing condition still climb to the cruising altitude, a new height controller was designed inspired by the total energy control theory, and the influence of the speed deviation was introduced to adjust the elevator for the total energy distribution, so as to solve the coupling problem between longitudinal trajectory control and velocity control. Taking a certain type of target machine as the object, the simulation is carried out under two kinds of extreme biasing conditions. By comparing the simulation results of the traditional and improved control structure, it shows that the new controller is effective in practical engineering applications.
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Abstract—Electroacupuncture therapy is a kind of acupoint stimulation therapy that combines acupuncture and pulsed electrical stimulation. In this paper, the electroacupuncture treatment system for insomnia was designed on FPGA. The working mechanism of FPGA is using the internal hardware circuit to realize the corresponding functions of peripheral circuits, which means any kinds of the waveforms can be generated by using FPGA without complex peripheral circuits. Particularly, the waveforms with regular modulation signals are better adapted to different insomniacs. The integration and reliability of system are improved by integrating hardware circuit modules that generate electroacupuncture therapy signals on a programmable FPGA chip.
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Abstract—In order to improve the recognition effect of wireless communication signal under non-cooperative conditions, the electromagnetic fingerprint sets representing the differences of wireless signals are constructed by combining high-order cumulants and Wigner-Power Distribution (WVD). Meanwhile the Fisher discriminant criterion method is introduced to evaluate and refine the electromagnetic fingerprint set. The simulation results show that the proposed method can significantly improve the identification effect of wireless communication signals in specific electromagnetic space compared with a single fingerprint feature.
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Abstract—In order to achieve accurate and efficient image segmentation, this paper proposes a Particle swarm optimization (PSO) algorithm and k-means aggregation hybrid image segmentation algorithm, which aims to solve the problem of selecting the initial center of k-means clustering and improve the disadvantages of easily falling into local optimal. Firstly, the image is de-noised and the processed color image is converted into HSV space to improve the color quality. Then, global search ability is improved by adjusting the inertia weight and learning factor in the particle swarm optimization algorithm. Finally, the local search is performed by switching to the k-means algorithm according to the particle fitness, so that the clustering center is constantly updated to achieve rapid convergence. Segmentation results show that the proposed algorithm can segment images accurately, and have a higher segmentation efficiency than k-means algorithm, traditional particle swarm optimization and k-means algorithm (PSOK).
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Abstract—Ant Colony Optimization (ACO) algorithm is a kind of subject-based intelligent algorithm with positive feedback mechanism and strong ability to find and optimize. The travel recommendation system is recognized as one of the technologies that can effectively solve the problem of information overload. This paper introduces an improved ant colony algorithm based on multi-objective decision-making to recommend the travel route to meet the needs of users. In this algorithm, the ant uses the attraction rating information that meets the user's needs and the actual distance of the attraction to find the shortest path. In order to prove the accuracy of the algorithm recommendation, an experiment is conducted on a network of attractions. The experimental results show that the algorithm can provide high quality solutions and can be effectively applied in the travel recommendation system.
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Abstract—With the complexity of traffic avement and the increase of vehicles, the traffic accidents of fatigue driving still account for a large proportion of the total traffic accidents. Drivers will wear glasses that meet their needs in order to reduce the incidence of accidents, but the occlusion of glasses and the changes and jitter of light during driving can significantly affect the accuracy of facial information detection. Aiming at the above problems, in order to extract the facial features accurately, an adaptive compensation infrared acquisition system is used, and a fatigue state detection model of parallel convolution neural network is proposed. Based on the different detection characteristics of the same image, the convolution neural network is used to automatically complete the feature learning, so as to obtain a more comprehensive description of the fatigue driving characteristics. The support vector machine is used to train the characteristics and establish the classifier to judge whether the driver is tired or not. Compared with other existing algorithms, this method obtains higher accuracy, meets the requirements of real-time detection and has high robustness to complex driving environment.
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Abstract—It is difficult to measure the track structure and coverage of the localizer of the instrument landing system by general test instruments, the main way to do that is flight inspection, which collecting and analyzing signals emitted by navigation equipment, during the flight. However, this method of measurement costs too much and must be operated by professional pilots. To realize the dynamic measurement and analysis of track structure and coverage, and accurately describe the dynamic characteristics of its spatial signals, a large amount of data needs to be collected. Navigation equipment test system mainly adopts mature low-speed data acquisition technology, which cannot meet the requirement of high-speed data acquisition. In the market, the single-chip high-speed ADC converter has high price and low resolution, and the application of the single-chip ultra-high-speed ADC converter to achieve data acquisition has posed a severe challenge to the performance of the processing system. This paper proposes a clock multi-phase technique, which can realize multi-channel parallel sampling, improve the rate and sampling precision of analog-to-digital conversion, and track the variation characteristics of spatial signal of the course structure and the coverage more accurately.
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Abstract—In this paper, based on speech multi-modal fusion emotion recognition, the key issues of speech signal preprocessing, feature extraction, fusion strategy, fusion method and emotion recognition classification are studied in depth. In this paper, the construction of emotional recognition theory model and the research of feature fusion and classification recognition algorithms are carried out. Speech-based emotion recognition research processes the speech signal by SVM, and calculates their maximum and minimum values. The specific methods and processes of emotional feature extraction are introduced in detail, and the extracted features are analyzed by emotion classification and recognition. The obtained recognition results verify the validity of the extracted features.
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Abstract—With the development of Internet, traditional data mining algorithms have been unable to adapt to information mining under large data volume. This paper combines the latest cloud computing technology to improve the traditional data mining algorithm, and uses Hadoop platform to improve the parallel processing ability of the algorithm. The K-Means algorithm relies on the initial k-value and the initial center point and is combined with the Hadoop platform features. Before the K-Means algorithm clusters, the Hadoop platform is used to sample the initial data, and the neighborhood density is used to determine the initial center point. Then cluster again. Based on the previous analysis of the defect of K-Means algorithm, this paper proposes a sampling-based secret, the improved K-Means algorithm. The initial k value and the center point are determined by the sample and density, and the defect of specifying the k value and the initial center point in the initial stage is solved. The K-Means algorithm will be improved MapReduce, and the ability to process data in parallel using Hadoop will improve the scalability of the K-Means algorithm. Finally, the algorithm proved to be more scalable during the experiment.
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Abstract—In recent years, due to the invasion of virus and loopholes, computer networks in colleges and universities have caused great adverse effects on schools, teachers and students. In order to improve the accuracy of computer network security evaluation, Back Propagation (BP) neural network was trained and built. The evaluation index and target expectations have been determined based on the expert system, with 15 secondary evaluation index values taken as input layer parameters, and the computer network security evaluation level values taken as output layer parameter. All data were divided into learning sample sets and forecasting sample sets. The results showed that the designed BP neural network exhibited a fast convergence speed and the system error was 0.000999654. Furthermore, the predictive values of the network were in good agreement with the experimental results, and the correlation coefficient was 0.98723. These results indicated that the network had an excellent training accuracy and generalization ability, which effectively reflected the performance of the system for the computer network security evaluation.
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Abstract—The optimal design of high-speed 650nm plastic optical fiber (POF) communication system with independent design silicon-based 650nm optical transceiver chips is implemented in this paper. Firstly, the parameters of optical path are obtained in theory by analyzing and calculating of system optical path based on the hemispherical and spherical lens system model. Then, the system optical path is designed and simulated in the software TracePro with the help of the control variables method. Finally, the system experiments of optical power, transient response and transmission characteristic are completed. The test results indicate that the output optical power is increased by 30μW using the lens and polished POF. The system has good transient. And the bandwidth of 180MHz is obtained to satisfy the demand of 100Mbps POF communication system.
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Abstract—Sleep is an important part of life, and sleep position recognition is an important indicator which reflects sleep quality, warns diseases and prevents pressure sores. Actually, sleep quality is usually related to the body pressure, which affects muscle comfort and blood circulation during sleep. However, in order to regulate the body pressure, one vital step is to recognize the sleep position. Body pressure is a common indicator of the recognition of sleep position. This investigation put forward a body pressure recognition method combining with kernel principal component analysis (KPCA) and support vector machine. The feature extraction based on KPCA can well solve the nonlinear separable problems as well as dimension reduction processing of PCA, and reduce the system’s complexity. Compared with the image recognition method and computer vision recognition method, it realized a higher recognition accuracy at a lower cost. Ultimately, the recognition accuracy of six common sleep positions reached 96.5%.
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Abstract—A composite scheme of subset extraction is here proposed upon data correlation for Big Data mining. Two stages of processing are applied to subset extraction. Four operations (conditional storage, data processing, similarity calculation and pattern learning) are designed, each of which corresponds to multiple objects. This paper mainly studies the extraction of closely related sets of Chinese vocabulary semantics. Based on the complexity of Chinese semantics, this paper maps Chinese vocabulary into English, extracts the eigenvalues of English vocabulary through vocabulary vectorization and principal component analysis (PCA). Then, it clusters the set of closely related items of English vocabulary by K-means algorithm. Finally, it builds the set of closely related items of Chinese vocabulary semantics based on this evidence. For the other information, it builds the set of closely related items of Chinese vocabulary semantics. Potential users create a closely related subset of corpus that is readily available. Sample applications are also provided to illustrate the main algorithms. The results show that the algorithm can effectively reduce the size of data and make the information available.
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Abstract—APM, Automated People Mover, is a kind of driverless Automated Guideway Transit (AGT) system, which is usually used to undertake passenger transport tasks in large airports. APM is different from the common urban rail transit system, which uses three-phase AC traction power supply system. In order to provide parameters for the design of traction power supply system in the early stage, the traction calculation method of APM system and the simulation method of traction power supply system are studied. In order to reduce the active power loss of the power supply rail, the minimum power loss model of the power supply rail is studied and an adaptive particle swarm optimization algorithm (APSO) with dynamically changing inertia weight (DCW) is used to optimize the location of the traction substation. The simulation results show that the proposed method can effectively optimize the location of traction substation and improve the utilization of electric energy.
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Abstract—With the emergence and development of data mining and data publishing technology, how to protect private data and prevent sensitive information leakage has become a major challenge. According to the existing privacy protection of social networks, attackers can re-identify private users according to the connection fingerprint information of some known public users in the network, and propose two solutions based on k-anonymity clustering method. However, most anonymization methods ignore the personalized privacy protection preferences of different private users, and k-anonymity clustering model has some subtle but serious privacy problems. In this paper, the original datasets are clustered, according to the needs of different users for personalized privacy protection, to form equivalence classes model, based on the l-diversity, which improved the k-anonymity algorithm for n-range connection fingerprint attacks. At the same time, it also improves the k-anonymity algorithm for n-range fingerprint attacks, and designs the LCFC and LCNC algorithms. Finally, experiments prove that the solution proposed in this paper has very effective running time and good stability.
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Abstract—Measuring retinal oxygen saturation has important reference significance for some systemic diseases. The existing fundus examination equipment in the country is still limited to the acquisition of fundus images. The diagnosis of the disease needs to be judged by the doctor's experience. The error is large. How to quantify the retinal oxygen saturation has become a new hot spot in the field of medical instrument science. Based on the characteristics of blood absorption spectroscopy, a non-invasive dual-wavelength retinal oxygen saturation spectrometer with a working wavelength of 570 nm and 600 nm was designed. The illumination and imaging light paths are designed in zemax software and the illumination light path is simulated in tracepro. Then the OTSU method is used to calculate the image threshold, and the gray value with the largest variance between the classes is found as the segmentation threshold, so that the blood vessel and the background are better scored.
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Abstract—Sleep is an important part of human life. Recognition of sleeping position is an important part of judging the quality of sleep, warning disease and prevention of pressure sores. At present, the body pressure is being used as a commonly method to recognize sleep gesture, and most of it is based on methods of extracting features, mainly based on local signs and global features. This paper proposes a deep learning method based on CNN deep learning method to deal with recognition of body pressure image. Compared with the previous methods, the method based on CNN neural network achieves higher accuracy of recognition at a lower cost and is suitable for complex application environments, which has good practical value.
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Abstract—To improve the wireless communication capability of embedded computing platform, a design method of hardware and software of wireless communication module based on Loongson-2K processor is proposed. In the hardware design part, the control function is realized by the processor in the back end and the wireless communication function is realized by the AR9285 chip in the front end. The AR9285 chip interacts with the processor through PCI-E bus. In the software design part, the wireless communication functions such as initialization, data transmission and reception are realized by adding WiFi function driver in BIOS. The verification results show that the wireless communication module based on Loongson-2K processor works stably, reduces power consumption and effectively improves the wireles
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Abstract—In order to solve the problem that the combatants cannot communicate face to face while performing combat missions, a soldier identification intelligent recognition system was designed. First, the system performs data normalization and endpoint detection on the collected gesture information. Then, feature extraction of the processed data such as mean, peak-to-peak and root mean square values in the time domain. Finally, the dynamic time warping (DTW) algorithm is used to calculate the similarity between the test gesture and the template gesture for the extracted feature parameters, and then the recognition result is obtained. The experimental results show that the system has the characteristics of high recognition accuracy, fine real-time performance and strong adaptability to individual differences. Therefore, it is suitable for gesture recognition and communication when performing combat missions.
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Abstract—Aiming at the problem that the traditional image fusion method is not easy to highlight the target information and the background information is not sufficient, an infrared and visible image fusion algorithm based on robust principal component analysis (RPCA) and non-downsampling Shearlet transform (NSST) is proposed. Firstly, the infrared and visible images are decomposed by RPCA, and the corresponding sparse matrices are obtained. Then, the infrared and visible images are decomposed in multi-scale and multi-direction by NSST transform to obtain the corresponding high-frequency and low-frequency components. The high frequency part obtained above adopts the fusion method of combining large absolute value with sparse matrix, and the low frequency part adopts the fusion method of sparse matrix to guide the low frequency part. Finally, the fused image is obtained by inverse NSST transform of each component. The experimental results show that compared with other image fusion algorithms, the algorithm can obtain fused images with more prominent targets and richer background information.
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Abstract—Cloud computing, is a hot spot in current research and a new information service model. It provides reliable and cheap computing resources to users. The cloud system is built by the Service-Level-Agreement (SLA). Cloud providers must meet SLA performance commitment to their clients. Thus, researchers are concerned about the performance issues of cloud infrastructure. Based on the in-depth analysis of the essential attributes of cloud computing systems, this study introduces a method of service cloud performance analysis by using stochastic processes, queuing networks, time series prediction and other methods and methods. Fine-grained modeling of cloud computing systems in low-reliability environments focuses on describing the probabilistic characteristics of cloud system state transitions and transitions under different task complexity and fault frequency, and establishes a performance model for optimal capacity decision problems. The implementation cost is minimized and the optimization problem is solved numerically by simulated annealing.
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Abstract—Time-frequency analysis is the major method to estimate the central frequency of frequency hopping signals. There is always a trade-off between computational complexity and estimation precision. An approach based on frequency difference and one-dimensional non-linear filter has been proposed. Firstly, the frequency ridge line is extracted base on the phase difference of the adherent complex points. Then, aided by median filter, the robustness against white Gaussian noise has been enhanced. Simulation shows that, the proposed method is 5 dB better than current method in accuracy and precision when SNR is greater than 13 dB. Computational complexity be- tween different method has been analyzed.
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Abstract—For the UAV inertial/GPS integrated navigation system, considering the problem of GPS data interruption in navigation process, this technical paper designs an improved sensors fusion algorithm. Combining the traditional extended Kalman filter (EKF) technology with strong tracking filter, a fuzzy strong tracking extended Kalman filter algorithm is designed by using the membership function of the fuzzy theory. Then the navigation simulation model of UAV is established. The simulation results show that the improved algorithm can quickly adapt to the sudden change of GPS signal, that is, when the GPS signal restores from the fault state to the normal state, the improved algorithm can converge to the stable state more quickly than the EKF algorithm, and complete the estimation of flight state again. At the same time, compared with EKF and strong tracking extended Kalman filter (SKEKF), the improved algorithm in this paper has higher estimation accuracy.
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Abstract—Solid rocket motor (SRM) is the key component of missiles and space rockets, and their sealing performance is an important indicator for assembling solid rocket motor. This paper proposes a new life prediction method that takes the solid rocket motor from the completion of the seal to the ignition emission as a seal lifecycle, considering both working and storage state. By studying the relationship between the compression set ratio of the seal ring and the draw ratio of the seal after aging, the storage aging phenomenon and the work rebound characteristics are combined. Further the seal life prediction model of the solid rocket motor seal ring is obtained, and the relationship between the storage time and the leakage rate of the seal ring is established. To verify our method, thermal aging experiments and leak rate testing experiments are performed on two models of solid rocket motors. Experiments show that the theory of seal life prediction proposed in this paper can effectively predict the storage life of solid rocket motor and it is beneficial to improve the reliability of the prediction of the sealing life of the solid rocket motor.
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Abstract—Satisfiability Modulo Theories (SMT) is an extension of the Boolean Satisfiability problem(SAT). It is a theory for satisfiability judgment of multi-type first-order logic formulas. It is being recognized as increasingly important due to its applications in different communities, particularly in compilation optimization, scheduling, static program analysis, software and hardware verification and other fields. Firstly, according to the VSIDS branch selection strategy, this paper proposes the branch selection algorithm WR-SAT based on the “clause weights-reward mechanism”, which gives priority to selecting variables in short clauses for assignment and reward variables in the conflict learning clauses appropriately, in order to reduce unless search in SAT during branch selection. Then, using OpenSMT, an open-source SMT solver, this paper combines WR-SAT with SMT solving in Lazy framework to form a new SMT solver, Modified-OpenSMT. Finally, by testing some instances in benchmarks of 2017 SMT-COMP, OpenSMT is compared with Modified-OpenSMT on the efficiency of solving and it can be found that Modified-OpenSMT is feasible for solving SMT problems. Meanwhile, Modified-OpenSMT has a good effect in solving SMT, especially on the eq_diamond benchmark family and qg5 benchmark family, which is better than OpenSMT.
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Abstract--Dental students devote several years to the acquisition of sufficient psychomotor skills to prepare them for entry-level dental practice. Traditional methods of dental skills training and assessment are being challenged by intelligent virtual visualization technology such as the subjective nature of surgical skill assessment and the limited availability of expert supervision. In this paper, an intelligent dental training simulator is presented to practice dental surgical skills in the context of a crown preparation procedure. This simulator navigates the training process from pointing the mobile phone device and the dental model based on infrared binocular positioning technology, guides with simple and clear course training simulator virtual visualization model in computer. The accuracy of the skill assessment is evaluated with data collected from novice dental students as well as experienced dentists. The quality of the system’s feedback is evaluated by asking a dental expert for comments. The experimental results show trained with this system, the students can rapidly improve their clinical skills.
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Abstract—Orthogonal frequency division multiplexing with Index Modulation (OFDM-IM) technique has recently attracted numerous research interests by applying the core idea of spatial modulation (SM) technology to the OFDM technology. In this paper, in order to further achieve the spatial and transmit diversity gain, a new scheme of enhanced spatial modulation (ESM) aided OFDM (ESM-OFDM) is proposed. This paper can be summarized as firstly, the ESM scheme is briefly reviewed. Then the technique of ESM-OFDM is elaborately introduced, which can carry more extra information bit compared to the OFDM-IM scheme. Furthermore, with the aid of maximum-likelihood (ML) detector, the asymptotic average bit error probability (ABEP) is presented. Finally, compared to conventional schemes such as OFDM, OFDM-IM, simulation results demonstrate that the spatial and transmit diversity gain is achieved to enhance the reliability over Rayleigh distribution and additive white Gaussian noise (AWGN) in wireless communication network.
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Abstract—In view of the current situation that the heterogeneity of cloud computing cluster is increasing and the load difference of system is obvious, this paper proposes a cloud task scheduling TQ (Three Queues) algorithm based on three queues and dynamic priority. TQ algorithm first puts jobs into the waiting queue according to the priority of jobs, and then divides jobs into job types according to the data input amount of Map phase, data output amount of Map phase, total number of current node running tasks, completion time of Map tasks and disk I/O rate. Jobs are put into corresponding queues to improve hardware utilization. The experimental results show that the algorithm can effectively improve the performance of cloud task scheduling under the coexistence of I/O-intensive jobs and CPU-intensive jobs, and shorten the completion time of the total task.
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Abstract—A base station for BDS/GPS dual-mode pseudorange differential correction based on Cortex-A9 core and a BDS/GPS dual-mode pseudo-range differential correction based on Cortex-M4 core are constructed. This paper discusses and applies the mathematical model of pseudo-range differential correction positioning result and error elimination method. The test results show that under the real-time dynamic conditions, the BDS/GPS dual-mode pseudo-range differential correction algorithm, the plane direction is better than 0.5m and the elevation direction is better than 1.0m under dynamic conditions, and the positioning accuracy is significantly improved.
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Abstract—The precision of Rife interpolation algorithm for frequency estimation of sinusoid signal has a great deviation when the frequency is close to the discrete frequency, and because of the noise, the false of interpolation direction can cause greater deviation, however, the frequency estimate precision is high when the signal frequency is close to the midpoint of two neighboring discrete frequencies. In order to solve the problem, the reason for influencing the precision is analysed in theory, and then anticipated Rife interpolation algorithm is presented, which decides if the direction is right after shifting the frequency. The algorithm can judge the interpolation direction correctly under noise effect, and estimate the frequency of sinusoid signal accurately. The simulation results show that the frequency estimation algorithm based on anticipated Rife interpolation can reduce the restriction that discrete frequency causes. Especially when the signal frequency is close to the discrete frequency, the algorithm can judge the interpolation direction and get the sinusoid signal frequency accurately.
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Abstract—In recent years, morbidity of lung cancer has been constantly rising worldwide, particularly in China. The paper, taking relevant data of 2000 patients of lung cancer as research subjects, preprocesses original data, then, studies frequent item set and association rules of Apriori algorithm, on which relevant factors causing lung cancer is analyzed. According to experiment results, the association factors proposed by the algorithm in this paper has feasibility and effectiveness in some degree.
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Abstract—MEMS gyroscope had the advantages of small volume, light weight, low cost, high reliability, easy digitization, mass production, etc. High-precision MEMS gyroscope should be applied near the resonance frequency in order to overcome the interference of process, environmental noise, etc. on measurement, and it is necessary to design gyro detection circuit and drive circuit. The resonant MEMS gyroscope designed and developed by our research group independently was regarded as the research object in the paper. The MEMS gyroscope closed-loop control digital ASIC circuit based on Crotex-M3 core was designed, thereby achieving small coverage area and excellent algorithm scalability. The overall design and simulation verification of the digital measurement and control circuit were completed.
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Abstract—Deep learning methods, especially convolutional neural network (CNN), which have made breakthroughs in many fields of computer vision, and one of the most prominent features of deep learning is that large-scale dataset with annotations should be used. However, obtaining such a dataset in the medical field is really a challenge due to scarcity of annotated data. In our work, a deep learning based framework is proposed to generate more real data from the existing data with Generative Adversarial Networks (GAN) and classify the suspicious lesions with CNN. The proposed method is applied on the diagnosis of diabetic retinopathy. The experimental results show that our deep learning framework achieve a better classification performance than the traditional deep learning methods.
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Abstract—This paper studied an integrated system to accomplish a real-time remote monitor function, which is based on STM32F407 embedded development board, through the Reduced Media Independent Interface (RMII), Flexible Static Memory Controller (FSMC) interface and web development, etc. The STM32F407 embedded development board is a core controller in the system and it is connected to Internet to realize the function of web server with Light Weight IP (LWIP) communication protocol. The value of various sensors can be transmitted to web server in real time with Server Side Include (SSI) instruction, and any device connected to Wi-Fi can realize the remote monitoring function through the router. Experiments are conducted with DS18B20 temperature sensor and the first channel of Analog-to-Digital Converter (ADC1) in STM32F407 to verify the effectiveness of the system. The results show that the embedded remote monitoring system works effectively. The system is convenient and practical in many industries.
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Abstract—Connected to an electric motor via the switching circuit through the direct current (DC) power supply, a brushless motor drive controls the rotating speed of the motor under the condition of input signals with different duty cycles. This paper focuses on the problem resulting in the burning of the overheating drive. For that, the paper relies on numerical simulation to offer thermal distribution data about the internal components of the device when being used in the extreme condition for satellite models, i.e. with the rated voltage and maximum load current, and then to analyze the reasonableness of their layout; at the same time, the paper gives the minimum dimensions of the heat sink mounted to the brushless motor drive used in the worst condition, followed by a simulation and testing of the temperature distribution in relation to the drive’s case. The simulation and testing results indicate that when used with high temperature, the drive with a built-in heat sink is able to meet the demand for using the device with the case temperature lower than 125℃ and the junction temperature lower than 150℃. The research findings of this paper can provide technical support for the design and use of brushless motor drives.
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Abstract—Path planning is important in robot field and in this field, many researchers have done a lot of work. This paper proposes an improved ant colony algorithm as traditional ones have a shortage of slowly convergence and easily falling into local optimum. On the basis of traditional ones, the dynamic random statistical analysis and extraction of each generation of Ant Colony are performed the optimal, average and worst ant information constitutes an adaptive operator for adaptive updating of local pheromones. Simulation results demonstrate that it is effective in equilibrium increasing convergence rate and getting into the contradiction of local optimal solution.
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Abstract—The information security problem of the industrial control system (ICS) has become prominent increasingly with the advancement of industry 4.0. The programmable logic controller (PLC) is the basic control device in the supervisory control and data acquisition (SCADA) of ICS. The stable operation of ICS is limited by the safety of PLC. In this paper, the PLC attack tree model, which targets the PLC equipment security, is built to analyze and evaluate the safety of PLC systematically. First, the attack path of the PLC has been analyzed. The attack nodes have been defined by employing the attack tree model. Second, the fuzzy analytic hierarchy process has been introduced to calculate the security attribute weight and quantify the attack probability of the leaf nodes. The Shapiro-Wilk test has been employed to ensure the normality of data. Finally, the attack probability of different attack paths has been calculated as the PLC attack tree model. The PLC attack tree model which proposed in this paper is employed to distinguish the probability of different attack paths. The proposed attack tree model provides a basis for decision-makers to take appropriate protective measures.
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Abstract—Moving object detection is one of the important applications of computer vision. How to accurately realize the separation between foreground and background is the research focus. In this paper, aiming at the problem of poor anti-interference ability of traditional codebook algorithm in complex background and the cavity-prone problem of traditional frame difference method, a pedestrian detection algorithm integrating the optimized five-frame difference method and the optimized update rate codebook is proposed. The algorithm designs a kind of double code word structure, in the process of codebook background modeling and testing process to code the code word in the book to join the update mechanism, and based on the amount of foreground points to automatically change background update rate. This design also implements an improved algorithm of double code word book YUV color space. It is used to realize the function of realtime updating background. Then, Canny edge detection operator is added to the traditional five-frame difference method to remove the noise. At the same time, the extracted rectangular area is used for filling operation and morphological processing to obtain the corresponding foreground image. Finally, the foreground image of codebook model and the foreground image of frame difference method are calculated with "and", "different or" and "or" of pixels to obtain the precise region of moving target. The experimental results show that this algorithm combines the advantages of the two methods and abandons the disadvantages of each method in the detection prospect. It can accurately detect moving targets and has good robustness. The effectiveness of this algorithm is also verified by experiments.
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Abstract—Aiming at the Four-rotor aircraft with complex dynamic characteristics, the Kane method is proposed to derive the mechanical model of the aircraft. Kane method is a new engineering method. According to the general method of establishing Kane equation, the aircraft is regarded as a single rigid body, and the translation and rotation kinematics model and dynamic model of the system are established. Through the analysis of the established kinematics equation, it is concluded that one of the main reasons for the non-linearity of aircraft is the non-linearization of kinematics equation. Finally, the validity of Kane's modeling method is verified by the relevant data obtained from actual flight experiments.
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Abstract—As a challenging computer vision task, fine-grained visual categorization (FGVC) has received more and more attention. Most fine-grained classification algorithms are data-hungry and time-consuming, on the contrary, humans have the ability to recognize different species with only a small number of samples. For example, a baby can identity different kinds of dogs with only few images. Therefore, in this paper, a fined-grained classification model which is based on meta learning is proposed, and it can be used for more accurate fined-grained classification with only a small number of data. Meta learning uses meta-learners to learn relative tasks so that they can handle with new tasks faster and better when facing new tasks. Specifically, our model uses a task learner to learn various tasks and update the parameters of meta learner at the same time. When facing a new task, meta learner can learn and adapt to new tasks more quickly. The key novelty of our model is an attention module which can enlarge the local area response of image. This model has achieved great results on both CUB-200-2011 and Stanford dogs, and the accuracy of this model has achieved 81.74% with only one image of CUB-200-2011.
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Abstract—In order to achieve accurate measurement of the radial growth of tree, this paper proposes a tree trunk diameter measurement algorithm based on line structured light, and constructs a tree radial growth measurement system based on line structured light vision. The original image is acquired by using an infrared camera and the original image is preprocessed. The image is segmented by the target contour extraction function findContours and the contour minimum bounding rectangle function boundingRect in OpenCV to extract the area where the tree is located. Line structured light is extracted by using a Hough transform. The distance between the camera and the tree is obtained in real time by using the TOF module, and the diameter of the tree trunk is obtained based on the principle of line structured light stereo measurement. The test results show that the proposed method can quickly and accurately measure the diameter of tree and achieve the automatic monitoring of the radial growth of tree.
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Abstract—A sea cucumbers detection method based on SSD and depthwise separable convolution is proposed. Firstly, sea cucumbers images taken by underwater robot are enhanced by Multi Scale Retinex, which can make underwater images clearer. MobileNet-SSD which uses a 13-layer depthwise separable convolution as basic feature extractor is trained to detect the sea cucumbers and model quantification is applied to the model to further reduce the model size and improve detection speed. Experimental results demonstrate that the mean average precision of improved SSD reaches 89.41%, the detection precision for underwater sea cucumbers is 93.76% and the recall ratio is 91.17%, the detection speed can reach 19.8f/s in CPU mode which is no longer limited by the performance of computer. Compared with other methods for object detection, the improved SSD can reach a high level of both detection precision and speed, which can realize the rapid and accurate detection of sea cucumbers.
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Abstract—The analysis of travelling characteristics is a crucial factor to smart city traffic management. It can help solve problems such as vehicle scheduling, resource allocation, etc. effectively. Traditional logistic regression analysis methods have low accuracy, cannot feedback optimization, so it’s not practical. This paper proposes an analysis method of urban citizens’ travel characteristics based on artificial neural network, theoretically proves the viability of neural network, and solves the problem of multi-parameter optimization of travel characteristics. The accuracy is high compared to the traditional logistic regression, and with more hidden layers added, the estimation accuracy will gradually increase, but the amount of calculation increases too.
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Abstract—With the rapid development of mobile communication technology and mobile Internet technology, mobile terminals are becoming more and more irreplaceable in people's life and work. In the present power industry, Android-based smart mobile terminals are widely used to carry out various kinds of operations. Compared with traditional desktop computers, smart mobile terminals provide users with convenience and are more vulnerable to attack and control. Considering the security protection of the core layer of smart mobile terminal, this paper designs an Android lightweight kernel layer mandatory access control framework, deeply analyses and discusses the necessity of terminal kernel layer security protection, proposes a verifiable lightweight kernel layer access control model, and formalizes the model. This paper describes and uses FVT method to discuss security policy conflict detection technology. Finally, the availability and shortcomings of the model on three typical smart mobile terminals are verified by experiments.
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Abstract—In order to ensure the security of the Internet of Things, it is necessary to authenticate the nodes in the architecture of the Internet of Things to prevent illegal node intrusion, illegal node data eavesdropping and other network attacks. In order to overcome this security threat and reduce the computing cost and other resource consumption of identity authentication in the Internet of Things, this paper designs a secure identity authentication method in the Internet of Things based on contact signals, which includes two parts: secure two-party identity authentication strategy and secure multi-party identity authentication strategy. The multi-party contact signal negotiation is completed by using the secure multi-party calculation method. In the process of secure multi-party identity authentication, an illegal node filtering mechanism is designed to improve the computing performance of secure multi-party identity authentication under the condition of illegal node attack.
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Abstract—With the explosive growth of population and the development of technology, face recognition has been widely applied to various fields. At the same time, there are problems of low recognition speed and accuracy. Therefore, this paper proposes a real-time face recognition system based on edge computing, and implements this system on the development board. The system has excellent performance, high recognition accuracy and low price, which fully exploits the advantages and characteristics of edge computing. This is a good solution for real-time face recognition systems.
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Abstract—Clothing sales forecast can help clothing producers and sellers to make reasonable plans. Considering that there are linear and non-linear factors in the clothing sales data, it is inevitable to lose part of the data information if a single prediction model is used, resulting in a large error in the prediction results. Among the prediction methods, ARIMA model has an ideal prediction effect on linear time series, while BP neural network has a good prediction effect on nonlinear time series. In order to predict clothing sales accurately and efficiently, a prediction model based on the weighted combination of ARIAM-BP neural network is proposed. By using MATLAB software to simulate the clothing sales data, the results show that the prediction accuracy of ARIMA-BP neural network combination model is better than that of a single model.
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Abstract—To dig the essential reason why bottlenose dolphin click signal has excellent ability of detecting target, this paper analyzes bottlenose dolphin click signal and its detection performance according to theoretical derivation and simulation analysis from the aspect of time and frequency domain, wideband ambiguity function and reverberation suppression, respectively. The analysis results show that the spectrum of click signal contain two Gaussian functions and cross-term’s function, and cross-term lead to the phenomena that low frequency component rising, high frequency component rising and new frequency component arising. The wideband ambiguity function of click signal is nail plate shape, as the sub-pulse’s frequency overlap region decrease gradually, the energy of ambiguity function’s main-lobe decrease, the velocity-distance resolution increase and the reverberation suppression performance for target sunk at sea bottom decrease gradually. These study results can provide reference for bio-inspired detecting signal designing in radar and sonar system.
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Abstract—Pantograph’s contact plate, drag link and upper arm work in high voltage environment. Contact physical sensors are incapable of safely and accurately inspecting these components. Among all inspection methods, image processing and pattern recognition based methods are widely acknowledged and applied thanks to the development of high performance visual sensors and algorithms. This paper discusses the possibility of establishing a visual tracking system to analyze the movement of high voltage components on a pantograph. Some specific accuracy and robustness criteria are proposed, and different algorithms are tested and evaluated. An optimized tracking solution is to put into experiments. Finally, some dynamic properties of contact plate, drag link and upper arm are obtained with this visual tracking system.
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Abstract—A air quality monitoring system based on the NB-IoT was designed for wireless monitoring of special field environment. The STM32F103RCT6 microprocessor unit was the core processor and the data of gas pollutants was collected by electrochemical gas sensor through serial port. The switching mechanism of wireless network and the data storage mechanism of disconnected network was designed. The network communication based on the TCP/IP protocol was realized through NB-IoT module or GPRS module. The experimental results show that the data transmission of NB-IoT network is stable. The system can automatically switch to GPRS network as the NB-IoT network is abnormal. In the absence of network, the data is stored in SPI Flash. The collected data can be reloaded as the disconnected network is recovered.
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Abstract—A discontinuous Galerkin approach was presented for the numerical simulation of acoustic waves propagation in transit-time ultrasonic flowmeters. The wave propagation in the fluid part was described by the linearized Euler equations and the equations of linear elasticity was adopted in the solid part. Stationary background flow field of ultrasonic propagation was calculated by solving the k-ɛ turbulent flow model. By linearizing the acoustic variables, the governing equation of linear sound propagation in the adiabatic state was obtained. By defining the normal velocity on the surface of transducers, the average sound pressure on the surface of receiver under downstream and upstream propagation could be generated. The simulated time difference was smaller than that of the experimental results when comparing the average sound pressure for downstream and upstream propagation. But the error of time difference between the experimental and simulation was within 5%.
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Abstract—Hot spot temperature of oil-immersed power transformer winding is an important parameter affecting the insulation life-span of transformer, which is closely related to the load coefficient of transformer. In this paper, the finite element simulation method is used to simulate and analyze the hot spot temperature of a 220 kV oil-immersed three-phase power transformer, and the temperature distribution of the transformer winding under rated load is obtained. The relationship between different load coefficients and winding hot spot temperature is researched by changing the load coefficient of transformer.
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Abstract—Grid harmonics generated additional loss in the transformer, which would lead the transformer winding to overheat. The influence of harmonics on the temperature field of transformer was observed by considering harmonic loss in the transformer flow-heat coupling calculation. This paper took the hysteresis loss, eddy current loss and winding harmonic loss under the influence of harmonics into consideration. The transformer loss calculation formula was derived. Therefore, the temperature distribution of transformer under harmonic condition was obtained through flow-thermal coupling calculation. The hot spot temperature was 73.4℃, which was within safe range. The calculation results of the fluid field showed that the asymmetric structure of the transformer lead to the asymmetry of the heat dissipation path of the oil flow, which brought about the asymmetry of the final temperature distribution result. This paper improved the flow-thermal coupling calculation method because of considering harmonic loss, which provided a reference for improving the calculation accuracy in running transformer.
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Abstract—As an essential element of the integrated energy system (IES), the output of coal-fired units still occupies a high proportion in the power system, resulting in high CO2 emissions. This paper proposes to transform the coal-fired unit into a carbon capture power plant (CCPP) and combined the power-to-gas (P2G) device to form the model of integrated energy system containing CCPP-P2G. Take the minimum of the operation cost and carbon trading cost as the objective function. The nonlinear part of the model is piecewise linearized and the example is solved with the CPLEX solver. Finally, the comprehensive cost, carbon emissions and wind consumption of the system in three scenes are compared and analyzed, the impact on the operational stability of the natural gas network is further studied as well. The simulation results verify the effectiveness of the proposed model.
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Abstract—In recent years, renewable energy distributed generation technology is introduced into power system through micro grid. As a rapid developing direction in the field of smart grid, the short-term load forecasting of micro grid is a very important work. More accurate short-term load forecasting can strengthen the energy management of renewable energy in micro grid. However, due to the non-smoothness and non-linearity of the load, the short-term load forecasting of the microgrid is a very complicated task. Taking a university office building microgrid system as an example, this paper expounds the difference between microgrid and traditional power in system load time series, and proposes a prediction strategy based on feature selection technology and prediction engine (including neural network and evolutionary algorithm) for short-term load prediction of microgrid.
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Abstract—Aiming at the problem of large overshoot and long response time of bidirectional DC/DC in the energy storage part of DC micro-grid, a fuzzy self-tuning PI controller is designed in this paper to realize online setting of control parameters and achieve the purpose of optimizing traditional PI control. The results show that fuzzy self-tuning PI control can effectively solve the problem of slow tracking voltage and fluctuation in dc micro-grid.
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Abstract—To improve the navigation accuracy of mobile robots, a grid map construction based on multi-sensor fusion is proposed from the perspective of map construction. And a path planning algorithm based on improved intelligent water droplet algorithm is proposed. Firstly, the observation model and information fusion framework of radar and depth vision sensor are established to construct the grid map. Secondly, the intelligent water droplet algorithm is introduced into the path planning model. The neighborhood perturbation strategy is used to optimize the evolution process of intelligent water droplets. Finally, through the verification of map construction on the mobile robot, as well as the experimental simulation of robot path planning, the improvement of map accuracy by multi-sensor fusion and the effective improvement of path convergence speed and accuracy by the intelligent drop algorithm are verified.
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Abstract—Drones play a very important role in disaster relief. This paper describes how to design a DroneGo disaster response system to quickly deliver drones and medical kits to the disaster area to support the hurricane disaster in Puerto Rico. First, the improved entropy method is used to find the proportion of various drones and determine the proportion of the three medical kits according to the selected location requirements, and TRUCKFILL is used to simulate the loading of drones and medical kits in the container. In addition, based on the local road network and population distribution, the optimal location for building the UAV base is determined, and the number of cluster centres is discussed to find the optimal container distribution. The results show that it is reasonable to allocate one and two containers in the two cluster centres in the west and east respectively. Finally, based on the selected location and considering the load of the drone, the best path is determined through graph theory. Referring to the method of finding the Euler cycle and integer programming, the drone flight plan is successfully developed.
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Abstract—In order to solve some shortcomings of the teaching method of maintenance operation of a certain type of equipment, a virtual maintenance training system based on Unity3D engine is designed in this paper. In this paper, 3DS Max and Solidworks modeling software are used to profile the tools and components of equipment installation and maintenance, and the virtual maintenance operation interface is constructed by using FairyGUI to realize human-computer interaction. In this paper, using key technologies such as collision detection, Animation and particle system, the virtual maintenance system of a certain type of equipment is constructed by scripting with C #. The system solves the need of virtual use, maintenance and teaching of equipment.
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Abstract—Temperature and humidity control are the key factors for safe and effective storage of grain in granary. In order to improve the efficiency of grain storage, it is necessary to effectively control temperature and humidity in granary. Aiming at the traditional way of monitoring and processing or PID tuning methods cannot deal with the complex process of non-linearity, time-varying, coupling and uncertainty of parameters and structure in time, an improved genetic algorithm PID control is proposed. The shortcomings of genetic algorithm premature maturity and random roaming are improved by niche technology, and then the PID parameters are optimized to achieve global optimum and effectively control of temperature and humidity in granary. The results show that the improved genetic algorithm PID can better control the temperature and humidity in granary.
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Abstract—Micro-inspection technology has played an important role in image inspection of hair forensic evidence. The use of convolutional neural network to automatically classify and identify the microscopic images of hair evidence will further enhance the automation of microscopic technology and test efficiency. Microscopic image acquisition of human hair with different dyeing conditions as well as hair of cats and dogs is performed by light microscope. The sample image data set is preprocessed by Matlab and converted into LMDB format. The convolutional neural network model XI-Net, which is suitable for this experiment, is used to conduct sample training and test comparison of experiment results with AlexNet, and center-loss function is adopted to improve classification accuracy and network generalization ability. The results show that XI-Net can achieve higher classification accuracy of 98.96 % after adding the center-loss function of 0.007 weight coefficient. This method can realize automatic classification and identification of microscopic image of hair evidence and provides a more efficient and accurate examination method.
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Abstract—This paper aims to improve the reliability of the company's communication network. By combing the key factors affecting the reliability of the communication network, the reliability evaluation index system of the power communication network is established, and the weights of each index are determined. Based on the big data platform, the basic data of production management of the power communication network is integrated, and the reliability evaluation index is calculated online. Based on the results of reliability evaluation, the weak link analysis of communication network is carried out, which provides quantitative decision-making tools for communication network management, promotes the transformation of communication professional management to lean direction, and continuously meets the new requirements for the reliability level of communication network under the new situation.
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Abstract—A new disturbance rejection control scheme is proposed for a class of controlled dynamic nonlinear systems in this paper. As is well known, the linear controller is valid to stabilize dynamic nonlinear systems even when these systems are affected by small amplitude disturbances. But, unfortunately, it may be unstable when the amplitude of the disturbance increases, which is undesirable. To overcome this undesired phenomenon, a compensation system (sensitive system) is introduced into the linear controller in this paper, by which the controlled dynamic nonlinear systems can be approximately linearized and the disturbance amplitude can be rejected. Our new disturbance rejection control scheme is described. First, the proposed sensitive system is connected to the controlled dynamic nonlinear system to form an interconnection system. Then, an adaptive state feedback controller is synthesized for the sensitive system to asymptotically guarantee the original stability of the controlled dynamic nonlinear system with large disturbance amplitude. Finally, the effectiveness of our new disturbance rejection control scheme is verified by the simulation example.
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Abstract—In this paper, the concept of the state domain of the hidden layer neuron is introduced for the three-layer BP (Back Propagation) neural network. By employing the state domain and the partition of unity method to modularize the hidden layer, and a new artificial network called Nested BP neural network is constructed to reduce the computational complexity and improve the learning quality. The Nested BP network is employed to approximate the unknown nonlinear items in controlled systems. Compared to the usual BP network, the control effect in this paper is greatly improved. Finally, an example shows that the proposed method is superior to the usual BP neural network in learning quality and control effect.
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Abstract—Density-based clustering method DBSCAN is one of the most widely used clustering methods, this algorithm does not need to input the number of clustering, and can find clusters of any shape. However, DBSCAN algorithm requires two parameters, with different parameters, the clustering results are quite different. This paper proposes a density-based clustering algorithm using adaptive parameter k-reverse nearest neighbor: ARKNN-DBSCAN, which can effectively identify and separate clusters with different densities by analyzing the correlation through the reverse k-nearest neighbor of the observation in the dataset without input parameters. Our results show that this algorithm is more accurate than other density-based clustering algorithms using adaptive parameter.
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Abstract—Millimeter wave radar has the advantage of short wavelength, high guidance precision and low atmospheric attenuation. The application of millimeter wave radar seeker in millimeter wave guidance system has great anti-jamming ability, all-weather combat ability and battlefield survivability. A millimeter wave microstrip antenna element for radar guidance is designed in this paper, and is simulated by CST software, the impedance bandwidth of the element is widened by means of coaxial back-feeding and U-shape on the surface of rectangular patch antenna. The simulation results show that the impedance bandwidth of the millimeter wave microstrip antenna element is 5.3 times of the traditional microstrip antenna element, that can be 17%(31-37GHz). The antenna element has the advantages of wide bandwidth, uniform pattern, small backward radiation, low cross polarization level, compact structure and is suitable for constructing phased array radar antenna
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Abstract—Diabetic retinopathy (DR) is one of the major causes of blindness in the western world. Effective treatment of DR is available, when detected early enough, which makes this a vital process. Computers are able to obtain much quicker classifications once trained, giving the ability to aid clinicians in real-time classification. This work employed a deep convolutional neural network (CNN) based method for diabetic retinopathy classification. Three independent CNNs were employed for the classification of DR grade, macular edema risk and multi-label, which included the combination of both grade and risk classes. A fusion method was used to combine all features extracted by the CNNs and make the final classification result. The classification accuracy of the grade and risk were 0.65 and 0.72, respectively. The classification results showed the proposed network fusion method can improve the performances on both task – DR grading and macular edema risk.
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Abstract—It is a critical issue of glass refractive index measurement that enhancing low contrast slit image. In order to solve this problem, the paper proposes a solution that is feasible and adaptive to enhancing low contrast image in engineering. The CLAHE algorithm is used to globally preprocess the slit image acquired by CCD in the meantime and adaptive smoothing filtering is used for filtering. The improved stretching window is used to suppress the brightness of the single line in the slit, and enhance the brightness of the slit image to improve the extraction accuracy of the single line. The experimental results show that the accuracy of glass refractive index measurement can be improved to ±5×10-6 in the case of low contrast image, which provides a new method of low brightness and low contrast for improving the measurement accuracy of V-prism refractometer and has a significant in its high precision measurement.
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Abstract—Image fusion is the method of merging information from many images of the same scene taken from various sensors. In this paper, a fusion method based on CLAHE and sparse representation is proposed, which can effectively extract the texture detail information of visible image and infrared image. Firstly, CLAHE and adaptive smoothing filtering is aimed to improve the contrast of the image. Sparse representation is aimed to fuse visible and infrared image. Experimental results show that the proposed algorithm can obtain state-of-the-art performance than DWT and NSCT algorithm.
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Abstract—The effect of traditional wavelet denoising algorithms is not very good and the detail precision of the image isn’t high enough. What is worse, it will damage the edge and corner information of the image, and lose texture details. To solve problems above, a new method based on adaptive morphological edge detection and wavelet fusion is proposed. Firstly, the noisy image is decomposed with two wavelet bases. Then we divide the wavelet coefficients into two parts by using the adaptive morphological edge detection method. Secondly, we deal the wavelet coefficients of the edge by using the improved threshold and the hard threshold function. Thirdly, we deal the others by using the improved wavelet threshold and the improved threshold function. At last, we obtain the denoising image by using the wavelet fusion algorithm. Results of the experiment show that the new method can not only highlight the characteristics of the image texture, but also can remove the noise without hurting the important characteristics and the texture edges at the same time. So the new method has great application value.
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Abstract—The wire patrolling for the transmission and distribution line plays an important role in maintaining the stable, safe and reliable operation of the power system. In order to complete the onerous patrolling task, the patrol chart should be divided into grids after information processing. Then, the inspectors are determined in the first place. Thus, the specific positional relation will be analyzed and a series of methods that will improve efficiency, for example, optimal path will be provided. This paper presents a matching algorithm for the inspector and the wire on the basis of GPS. By acquiring the coordinate, the position of the inspector and the cable being inspected will be matched, and the attitude while he is working will be analyzed at the level of position. In addition, the path can be fitted through the coordinate point column. It will provide a foundation for further path planning and information management, and evaluate the completeness and efficiency of the patrolling work.
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Abstract—Outlier detection is an important aspect in the field of data mining. In order to solve the problem of outlier detection in high-dimensional datasets, an outlier detection algorithm based on Gaussian mixture model is proposed. First of all, for the data set to be tested, the global optimization expectation maximization algorithm is used to fit a Gaussian mixture model, and then the three-time standard deviation principle is introduced on each Gaussian component, the outlier is the data point outside the range of the mean deviation of the mean value of three times the standard deviation. Through the experiments on the simulation dataset and the real data set, the effectiveness of the algorithm on the outlier detection of high-dimensional data sets is verified.
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Abstract—In order to improve the characterization ability of speech signal and recognition accuracy of speech emotion recognition, a speech emotion recognition model based on improved Deep Belief Network (DBN) is proposed. The method is to replace the traditional DBN activation function with a Rectified Linear Unit(Relu). And the reconstruction error is used to determine the depth of the DBN network. The shorttime energy, short-time zero crossing rate, the fundamental frequency, formants and 24 dimensional MFCC parameters of emotional speech signal are extracted as the basic features. Using these basic features as input to the DBN, automatic recognition of the 6 emotions, anger, fear, joy, calmness, sadness and surprise can be achieved. Compared with the traditional DBN model and the BP model, a better recognition result is achieved by using the improved DBN discussed in this paper, and the recognition rate can reach 84.94%.
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Abstract—In order to meet the need of water quality in nuclear power plants, an intelligent water quality monitoring system for the water intake of nuclear power plants was designed. The system is a diversified intelligent water quality monitoring system with parameters of main monitoring temperature, ammonia nitrogen content, chlorophyll content and other parameters and underwater camera conditions as well. The paper used STC89C52RC microcontroller and the corresponding sensor underwater to collect water-quality data and sent them to the water surface by wired transmission. It built a wireless Ad hoc network through the nRF24L01 modules to accomplish the data transmission. And the LabVIEW platform is adopted as the monitoring center to achieve data monitoring, early warning and analysis. The test results show that the system is suitable for water quality monitoring of nuclear power plant water intake.
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Abstract—Road Detection Algorithm for Vision Based Intelligent Vehicles is proposed, by considering road surface as reference object and Sub Region Hough Transform for road edge detection. The features of structured pavement are extracted, and a mathematical model that describes the relationship between target image coordinates, world coordinates and camera parameters is established. The solution is based on partition, which traverses the total number of segments in each sub region and finally, by using the traversal partition window an optimal solution that is straight line information of the road edge is found. Simulation results show that, adoptive path detection Algorithm for subsection Hough Transform improves the reliability of road detection.
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Abstract—The traditional PI control of the VIENNA rectifier is difficult to track zero-error of the AC side and it is slowly to track the dynamic response of the DC side. In order to achieve zero-error control of the AC side, the proportional resonant (PR) controller is applied in the Vienna rectifier current loop, and the system's anti-jamming capability and fast performance are improved by introducing load current feed-forward control. The analytic expression of feed-forward function is derived, the influence of feed-forward factor on the dynamic response of the system is analyzed, and the selection basis of feed-forward factor is given. The simulation and experiment results show that the scheme boasts validity and correctness.
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Abstract—This paper presents a local positioning system based on vehicle motion model. The system is designed by 2D dead reckoning principle, which uses previous state information, speed and heading outputs of vehicle sensors to estimate real-time pose information. A Kalman filter based on speed-acceleration model is introduced to eliminate error of the odometer. And a linear error model is introduced to compensate the accumulative error of gyroscope. By preprocessing the speed and heading outputs, the precision of local positioning system is improved.
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Abstract—To solve the problem of blind equalization for nonlinear underwater acoustic channel, the kernel method was adopted and a variable step size algorithm was proposed to further improve the performance. The kernel least mean square (KLMS) algorithm can obtain good performance for nonlinear filter with simple computational complexity. The step size is an important parameter of KLMS. Hereby a variable step size algorithm based on the estimation of instantaneous gradient error by sliding window method was proposed. The step size keep big value when the estimation value larger than the pre-set threshold, otherwise the step size switch to small value. The proposed algorithm can take full advantage of big value to obtain fast convergence rate and small value to obtain high convergence precision. The simulation results under nonlinear underwater acoustic channel show the effectiveness of the variable step size KLMS blind equalization.
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Abstract—A local mean decomposition (LMD) improved by energy layer extraction algorithm was proposed to overcome the mode mixing of LMD. The signal is decomposed by LMD to obtain a series product functions (PF). Integral operation is done for the energy of each PF, and the degree of energy leakage caused by mode mixing is determined according to the integral ratio. A new PF can be obtained by removing the next order PF from the high order PF with mode mixing. Subtracting the new PF from the signal, the new signal to be processed is got and LMD is carried on until all the frequency of PFs is independent each other. The simulation results show the effectiveness of the proposed algorithm.
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Abstract—Probabilistic and Parallel Planning (PPP) is a hot spot in AI planning in recent years. Relational Dynamic Influence Diagram Language (RDDL) is a new language used in PPP. For PPP problems, Real-Time Dynamic Programming (RTDP) and Upper Confidence Bounds (UCB1) Applied to Trees (UCT) algorithm are few approaches to find near-optimal solutions. However, there is no evaluation of the performance of these algorithms under the same platform. Different planners based on RTDP or UCT have different performance. It is hard to answer which algorithm is better. Fortunately, RDDLSim is a platform which implements a parser, simulator, and client/server evaluation architecture for RDDL. Empirically testing these algorithms on the same platform is necessary. In this paper, we evaluate the performance of RTDP and UCT in RDDLSim. Experimental results show that the performances of these two algorithms are domain specific. In several domains, RTDP is significantly more efficient than its opponent. While in other domains, UCT performs better.
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Abstract—Aiming at the problem of incomplete battery charging and lower energy output in the use of power aircraft lithium battery pack, a set of power equalization strategies are introduced. Firstly, the OCV-SOC (Open Circuit Voltage-State of Charge) curve of battery is established through discharge experiments. The effect of various factors on SOC is also obtained. Then the accurate estimation model of the battery pack’s SOC is built. With the SOC as the equilibrium variable and the battery management chip LTC6804 as the core, the bidirectional equalization management circuit of the battery pack is designed. Finally, the effectiveness of the equalization strategy is verified through the simulation experiment and the actual test of the power aircraft lithium battery pack.
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Abstract—A table usually contains numerical and symbolic data, so a method is proposed for clustering the records of the table. First, by using the improved function (TF-IWF) of the term frequency-inverse document frequency (TF-IDF), the symbols are transformed into the corresponding numerical values, and the remaining numerical values remain unchanged. On the basis of data transformation, the principal factor iteration method and maximum variance rotation method are used to discover the key factors that affect the clustering of the records. And the scores of the key factor are obtained by the least square method. Then k-means algorithm is used to cluster the scores, and finally the clustering results of the records are obtained. experimental results show that the evaluation indexes of the proposed method are better than those of other clustering methods, and the proposed method is helpful to dimensionality reduction and extract the latent important features of different clusters, so that each cluster can be labeled precisely.
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Abstract—Based on the leading power factor test guidelines of synchronous generator, the calculation method of leading power factor depth limit constrained by generator power angle is presented, and the impact of system reactance on system static stability with power angle limited is studied. Meanwhile, the values of terminal voltage and stator current are checked and the modification of leading power factor depth limit is given if these values are over-limit. Taking #1 generator unit of Ruijin thermal power plant for example, the PQ curves of leading power factor depth limit changing with active power are showed under different system voltage conditions. The results considering constraints of leading power factor test can provide references for experimenters to control leading power factor depth so as to ensure the safety and stability of generator in the tests.
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Abstract—In order to improve the efficiency and accuracy of obstacle avoidance path planning, the characteristics of obstacle avoidance path planning are analyzed. Based on the traditional ant colony algorithm, sliding window and forgetting factor are introduced. By adjusting the correlation parameters and pheromone rules, the fixed value ant colony parameters cannot meet the performance of the whole calculation process. Through the cooperation of ants, an optimal path was established to avoid obstacles. An optimal moving path of manipulator based on dynamic recursive ant colony algorithm was proposed, and the practicability and validity of the method were verified by an example. It provides a reference for finding the optimal solution to the manipulator path planning in the shortest time.
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Abstract—Intermittent Sampling Repeater Jamming (ISRJ) is an advanced coherent interference method, by using the matched filtering characteristics and under sampling technology of pulse pressure radar, to generate false target jamming which poses a great threat to modern radars. Based on the research and analysis of the principle of intermittent sampling repeater jamming, aiming at the intermittent sampling characteristics of interference, an in-pulse quadrature phase-encoded signal is designed by using the radar signal which is not intercepted by the jammer. In the process of waveform design, an immune genetic algorithm based on DNA coding is used to make the orthogonal waveforms have lower autocorrelation sidelobe and cross correlation peak. The simulation verifies that the orthogonal waveform signal in the pulse can effectively suppress the intermittent sampling repeater jamming, and analyzes the anti-interference performance under different signal-to-noise ratio conditions.
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Abstract—This paper studies the reliability and system cost based on the independent microgrid that includes wind turbine, photovoltaic panels, diesel generator, energy storage system, and analyzes its operation results with different operating strategies. Firstly, consider the randomness of wind speed and light intensity, the mathematical models of wind turbine, photovoltaic panels, diesel generator and energy storage system are established respectively. Secondly, consider the equipment investment, operation, maintenance, fuel, and replacement cost, power supply economy and reliability as optimization target. Finally, establish an optimal model that includes equipment investment operation cost and system reliability index, the optimization of the capacity of the microgrid is solved by using the Non-Dominated Sorting Genetic Algorithm II. Programming in MATLAB to achieve the optimal microgrid configuration scheme, and conduct economic and reliability analysis, the results show that different control strategies have an effect on optimal configuration, and economics and reliability will also be different, thus it provides the necessary basis for user optimization design.
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Abstract—This paper introduces the principle of series compensation technology in distribution network, and analyzes the topology structure and application advantages of fixed series capacitance compensation device. Taking the design of the series compensation device for 35kV in Linlang station as an example, the capacity selection of the series compensation device are introduced. The voltage, line loss and voltage fluctuation rate with and without series compensation device are compared. Besides, the economic benefits of the series compensation device are described. The engineering example further validates that the series compensation technology is an effective method to improve the transmission power of the line, enhance the stability of the system, and improve the voltage quality of the line. Therefore, the series compensation technology has great advantages in the 35kV distribution network.
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Abstract—The purpose of this article is the CRM Boost PFC circuit, which analyzes the conducted electromagnetic interference of the converter. It mainly introduces: the converter's conducted EMI noise source, including MOS switch drain source voltage VDS, gate drive signal voltage overshoot, rectifier bridge noise generated by diode and transformer magnetics. An equivalent model of the gate drive circuit is established. The cause of the noise is analyzed and the corresponding suppression measures are proposed. This article builds a low power 100W CRM Boost PFC prototype and uses the R&S EMI receiver ESU26 to perform EMI noise tests on prototypes. EMI caused by overshoot of the drive voltage is analyzed and the voltage overshoot is suppressed by adding capacitance between the power supply terminals of the DC power supply of the control chip.
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Abstract—It is considered to be one of the most promising large-scale energy storage technologies by applying compressed air energy storage (CAES). But there is no method to calculate the maximum speed after load rejection. By researching on the process of load rejection, the over-speed process is divided into three phases. The method to calculate the maximum speed is also worked out. And it supports the manufacture and the operation of CAES.
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Abstract—With the construction of the smart grid with UHV as the backbone grid, the requirements for the “three lines of defense” security defense function of the power system are more stringent. The communication system of the real-time wide-area stable control business currently under construction is large in scale and complicated in structure, the primary and secondary equipment involved in the power increase, and the probability of failure of the power communication network carrying the business increases due to various unexpected factors. When the communication network node or link is interrupted, how to restore the stable control business of the bearer needs to construct a complete rerouting mechanism. This paper proposes a re-routing mechanism based on the business risk for the power communication transmission network. According to the requirements of the grid business importance and the delay constraints, the optimization model of the network risk minimization is constructed, and the Yen algorithm and the genetic algorithm are integrated to realize the business routing method. Through the simulation analysis, the proposed method can quickly realize the fast switching and recovery of the stable control business for the high-risk scenarios such as business interruption caused by different incentives.
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Abstract—Based on the circuit modeling and simulation technology, a new method was proposed to select the optimal health monitoring point with the Rough Set attribute reduction algorithm. In allusion to the degeneration failure in electronic circuits, the influence of the relevant monitoring points on the output results was analyzed. Moreover, the simulation tool PSPICE was used to perform degradation simulation on key components, and the characteristic parameters were used to describe the degradation process. Finally, the parameters were optimized according to the Neighborhood Rough Set attribute reduction algorithm, and the effectiveness of the method was verified by the degradation simulation experiment of MOSFET.
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Abstract—At present, the state evaluation of the vacuum circuit breaker mostly relies on a single sensor. For a complex circuit breaker, the characteristic quantity of the single cannot fully stand for the state of the circuit breaker. In this paper, multi-sensor is used to analyze comprehensively the status of the circuit breaker. The temperature sensor uses self-powered mode to monitor the temperature of contact arm of the circuit breaker and the consequences of evaluation of the contact arm are obtained according to the dynamic temperature rise model. The signals of vibration are collected by the vibration sensor and are extracted the feature quantity based on the algorithm of complete empirical mode decomposition of adaptive noise and sample entropy of intrinsic mode functions. The signals of angle are collected by the angle sensor and can be converted into travel by using a calculation formula, which can extract feature quantity of travel. Then combined with these two characteristics, the transmission mechanism of circuit breaker is evaluated by using the neural network. The hall sensor monitors the current of the coil, then the consequences of evaluation of the control mechanism of the circuit breaker are obtained by a nonlinear state estimation technology. Finally, through the synthesis rule of DS evidence theory, the above preliminary evaluation results are combined to obtain the consequences of final evaluation.
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Abstract—The line-commutated converter-voltage source converter (LCC-VSC) hybrid direct current grid becomes a new form, which is applied to high-voltage and long-distance power transportation. In this paper, a transient stability analysis method for LCC-VSC hybrid transmission systems is proposed, which considers the characteristics of AC/DC hybrid system transient stability analysis and calculates it through probability-based transient stability evaluation. Firstly, the stability analysis characteristics of the hybrid power transmission system are studied, and the hybrid HVDC transmission model at both ends of the LCC-VSC and the influence of the control mode on the transient stability are presented. Then the transient stability of AC/DC hybrid system is analyzed, and the selection method of system state before failure, the probabilistic stability fault model based on Monte-Carlo, and the probabilistic stability index solving method of power system are given. Finally, the rationality and effectiveness of the proposed method in the application of transient stability assessment of large power grid are verified through the analysis of a practical example of the power grid in China's "west-east power transmission" project.
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Abstract—The laboratory simulation environment of the low-voltage station area is set up, and various experimental conditions such as adjustable line parameters, variable load, and operating conditions can be simulated to simulate various types of on-site environment and operation status of the actual station area. This paper proposes the electrical characteristics analysis and parameter extraction method based on the user's electricity behavior. Firstly, through deep mining of massive low-voltage station area power data, based on hierarchical analysis, cluster analysis and other algorithms, extract the typical electrical characteristics of the station area and the user's electricity behavior characteristics, build a typical low-voltage station area model, and develop the laboratory environment. Then, a method for extracting the electrical characteristics of the station based on the user's electricity consumption behavior is proposed. Finally, the simulation of hardware equipment and numerical model are modified to fully simulate the running state of typical station. The experimental results show that the simulation platform is helpful for the verification of the research results of low-voltage platform area, such as the operation error check, the relationship between household and transformer, and the line loss.
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Abstract—Aiming at the failure that caused by the change of shaft resistance of large synchronous motor starting-up with load-commutated inverter during in a low speed, this paper makes a further investigation on the rotor position detection technology and Low frequency pulse commutation control technology based on motor voltage, flux, torque equation of ideal synchronous motor and power station operation data. And then an optimal control method for the low frequency phase of a large synchronous motor at the low frequency phase of a large synchronous motor is proposed. Finally, based on the real-time data simulation platform, a simulation model of large synchronous motor's soft start control system is established, and the starting failure problems and optimal control algorithm of the load-commutated inverter in low frequency stage are simulated. The experimental results show that the changes of motor shaft resistance may cause the failure of the detection of the rotor position, which will lead to the fault of the inverter. By lengthening the working time of the first torque and the number of estimated pulses at a low speed, the resistance moment of the unit can be overcome effectively and the success rate of the unit can be improved.
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Abstract—In this paper, a novel piezoelectric buoy energy harvester composed by a buoyant sphere shell and a flexible composite piezoelectric disk is proposed to obtain the energy from ocean waves. The flexible composite piezoelectric disk fixed inside the buoyant sphere shell consists of a proof mass and a two-tier plate, i.e., a PVDF layer and a base layer. The simulation results showed that the output power of the harvester can reach up to 1mW, which is capable to drive the wireless sensors. Moreover, the harvester can generate considerable output power in a wide frequency range of 4Hz – 50Hz. In a further analysis, the orthogonal experimental design method is adopted to get the comprehensive design and optimizing rules and meanwhile reduce the needless simulations. Depending on 49 representative simulations, the effects of the structural and material properties of the harvester on its performance were systematically investigated and some valuable rules about designing and optimizing such kind of harvester were obtained.
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Abstract—In view of how to effectively detect a complete pulse period and the echo signal without triggering information and the fixed pulse cycle, this paper proposed a real-time detection algorithm of echo signal and developed a detection software of echo signal on the platform of Visual C++. First of all, the relative frequency domain energy detection algorithm was adopted to detect a pulse cycle through the adaptive threshold. Then, the echo signal was detected by the copy correlation algorithm. At last, the results were visualized in real time for the users. The experiment of the algorithm showed that the software can detect the complete pulse cycle and echo signal, and the performance of the system is quite stable.
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Abstract—The flashover phenomenon of insulator is the main cause for the insulating failure of GIS. One of the most critical factors that impacts the surface flashover voltage is the accumulation of surface charge which can even severely restrict the performance of insulators. Therefore, it is very necessary to find a way to restrain the accumulation of surface charge on the insulators. The common methods to restrain the surface charge accumulation on the insulators were reviewed in this paper. Through the reasonable comparison and analysis for those methods, the way of nano-coatings for insulators was selected to restrain the accumulation of surface charge on the basin insulators of GIS. The raw materials and formula of nano-coating materials were developed in this paper. The effective experiments were also carried out to test the suppression of nano-coatings for the surface charge accumulation on the GIS basin insulators. The experimental results indicated that, the nano-coatings can effectively restrain the accumulation of surface charge and improve the distribution of electric field on the insulator under DC and AC voltage, the maximum degree of the suppression for the surface charge under DC voltage can reach 51.2%, while under AC voltage it is 9.3%.
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Abstract—With the continuous innovation and development of technology, communication transmission mode has been gradually deployed from solidified wired mode to flexible wireless fusion mode. Wireless private network has special advantages that wired can not achieve. Through comprehensive evaluation, the power grid decides to construct a power wireless private network within the whole network, carrying the last kilometer of service of the power grid. During the initial operation and testing of the experimental network, it was found that many terminals were deployed in the basement, weak electric well and other complex and closed environments. Because of the signal loss of the multi-layer shield, the wireless signal reached the coverage area. In this paper, the realization of LTE230MHz power wireless private network depth coverage is studied in detail. The technology and network deployment are analyzed in depth. Some innovative ideas are put forward in traditional technology. The application effect is analyzed with the existing network test examples, and the feasibility and reliability of the method are verified. The research and experiment in this paper can be used for reference in the construction and optimization of power wireless private network system.
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Abstract—By the big data experiments, it has been achieved to analyze the relationship between the occurrence of accidents and various influencing factors. Through the regular analysis and situation prediction, it is possible to provide traffic management departments and participants with effective information to reduce traffic accidents.
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Abstract—In the simultaneous wireless information and energy power transmission system, the system uses the same radio frequency signal to transmit information and energy simultaneously, which can be effectively used for self-sustaining operation of wireless devices. In practical applications, the power splitting method can realize simultaneous wireless information and energy transmission between base station and multi-user. Different user systems have different energy consumption, so users with insufficient power need more energy transmission for power supplement. At present, the beamforming strategy of simultaneous wireless information and energy transmission does not consider the problem of insufficient power at the user end. This paper presents a simultaneous wireless energy and information transmission system based on Reinforcement learning (RL) beamforming mechanism for power splitting. The reinforcement learning (Q-learning) algorithm adopts the e-greedy strategy. The beamforming design is optimized by feedback of power information at the user end to increase energy transmission for users with insufficient power. The transmission power is optimized by minimizing the signal-to-interference-noise ratio (SINR) and energy harvesting constraints using channel state information and node power information of each node, and the simulation analysis is carried out.
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Abstract—The architecture of the basic communication equipment of the distributed dual-active data center is designed to achieve network reliability and availability through technical means such as line redundancy. In order to solve the short-board effect of the traditional data center, we use the switch cluster virtualization technology to ensure the availability of the entire architecture;Smart DNS technology (BIND 9) and Eginx reverse proxy technology are used to select the nearest data center in different regions to reduce the data transmission delay caused by physical distance. At the same time, a special disaster-tolerant area has been set up to prevent data from being restored immediately after the accidental damage of the data. The design is based on ENSP simulation and has useful value. After modifying its model, it can theoretically be used for actual production deployment. Experiments show that the entire network topology model basically meets the requirements, and each subnet can be unblocked.
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Abstract—With rapid growth in the size of software, applications’ code structure becomes more complex, attacks against vulnerable points can cause huge damage. To solve this problem, this paper analyses code structure of classes, builds cooperative network at class level to describe information propagation. On the basis of class cooperative network, this paper designs a method to exclude wrong information propagation paths, uses WS small-world network to build software model, analyses data association, finds network critical paths and vulnerability points. Experiments are launched to verify the results.
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Abstract—At present, with the rapid development of society, it brings convenience to people's work and life, but also brings many potential safety hazards. Fire is the most common and frequent occurrence of these hazards. Therefore, this paper designs and implements a fire alarm device based on singlechip. When a fire occurs, a large number of smokes and higher temperatures will be generated. The device can detect smoke signals and temperature signals through smoke sensor detection module and temperature sensor detection module, and send them to singlechip to decide whether to alarm. In the specific implementation process, it is divided into two parts: The first part is the hardware design, including singlechip module, sensor module, display module, alarm module and key module. The second part is the software design, which uses C language to develop the alarm system through Keil uVision 4 software. At the same time, in order to make it more convenient for users to use, emergency alarm key and threshold adjustment key are designed, which can realize emergency alarm function and threshold parameter preset function. The experimental results show that the device has stable structure, good performance and practical value.
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Abstract—Aiming at the problem of waste of manpower and material resources, serious waste of water resources and low utilization rate in traditional farmland irrigation, a low-cost intelligent irrigation system is designed by using sensor technology and wireless data communication technology. The system can monitor the amount of irrigation water and receive the feedback information of the field controller, remotely and control of the irrigation system solenoid valve on or off achieving timely and appropriate precision irrigation. It has been verified by the Institute of Water Resources and Hydropower Research. The designed system can effectively perform real-time monitoring, data feedback and analysis, and has high stability and reliability, and application value.
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Abstract—Based on the two-degree-of-freedom planar link topology, this paper deduces that five-bar mechanism is the simplest structure to realize the closed chain mechanism. After discussing the five-bar mechanism, it was found that a variant of the RPRPR structure could be used as the basic linkage group of the new closed chain leg mechanism and named as collinear driver mechanism. This paper establishes 3D model of the hexapod robot equipped with the collinear driver leg mechanism and the ordinary distributed leg mechanism, and imports them into the ADAMS for dynamic simulation. The collinear driver mechanism is used as a design scheme, and the distributed driver is used as a comparison scheme in order to verify whether the collinear driver mechanism is superior to the existing distributed driver mechanism in terms of energy consumption and dynamic parameters. From the comparison of the simulation results, it is found that the energy consumption of the collinear driver mechanism is significantly smaller than that of the distributed driver mechanism, which proves that it is a new design with low energy consumption in the closed chain leg mechanism. In addition, the distributed driver decoupling mechanism has high requirements on the peak power of the driver, and set the higher requirement of the joint drivers. The collinear driver decoupling mechanism allows the two drivers to work together.
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Abstract—This paper proposes a method for acquiring target color parameters based on digital cameras in the field. A preliminary model of color reduction is proposed by using the acquired digital images to achieve fast and accurate real parameters of color, which provides a reference for camouflage design. The research starts from the imaging principle of digital camera and the conversion of color space. By designing the experimental scheme and conducting a large number of experiments according to the scheme, the digital image obtained is analyzed and processed, and a digital image color restoration model is proposed.
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Abstract—It is impossible to obtain the detailed distribution of the potential and current density of the ship hull during the actual measurement because of the complex form of the underwater electric field source and the numerous influencing parameters. Therefore, it is difficult to accurately and completely grasp the parameters of the field source. According to the characteristics of field source and the distribution of electric field in sea water, the complex field source is simplified and abstracted into dipole and quadrupole sources commonly used in electromagnetic, and the characterization of different types of electric field sources for ships is completed
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Abstract—The electric conductivity of seawater directly affects the attenuation law of electric field source in seawater. With the increase of conductivity in seawater, the attenuation of electric field in seawater increases, which leads to different characteristics of electric field sources in different sea areas. The magnitude and distribution characteristics of the ship's electrostatic field source are closely related to the conductivity of the sea area. Because of the limitation of the test conditions, it is difficult to obtain the electrostatic field sources under different sea water conductivity environments. In this paper, a method of deducing the field source of ship electrostatic field under different sea water conductivity is used to deduce the field source of ship electrostatic field inversion from the measured data at sea to the converted sea area, which improves the applicability and utilization value of space conversion.
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Abstract—When solving the electromagnetic compatibility problem of low-voltage distribution system in thermal power plants, it is found that the current protection specifications for secondary cables in some conventional substations and common civil buildings are not suitable for thermal power plants, and there is a lack of reference standards for lightning protection data of secondary cables in thermal power plants. Therefore, this paper takes Xishui Substation as the research object, uses CDEGS software to build a three-phase low-voltage distribution system combined with the structural characteristics of large-scale ground screen, overhead cable bridge, building group and others of thermal power plants, and completes the modeling of its buildings, grounding grid, transformer and secondary overhead cable bridge. According to the layout of two buildings in the low-voltage distribution system, the overvoltage of the secondary power cable generated when the buildings are struck by lightning is calculated. The results show that the lightning strike will cause great electromagnetic influence on the secondary equipment. This provides data support for the layout design and protection of secondary cables in thermal power plants, and is of great significance for improving the insulation coordination capability of secondary systems.
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Abstract—The low-voltage distribution system of thermal power plants has a large auxiliary power load and is vulnerable to electromagnetic influence, so its lightning protection is relatively complex. This paper takes Xishui Substation as the research object, and uses CDEGS software to build its buildings and grounding grid model, and calculates the shunt of lightning current in branch conductors, indoor magnetic field distribution and grounding grid potential distribution of thermal power plants when lightning strikes buildings. The results show that the shunt coefficient of the thermal power plant buildings basically meets the specification requirements. The lightning that strikes buildings will not cause harm to indoor equipment near the middle. Lightning flowing through buildings and leaking into the ground will threaten its low-voltage distribution system with ground potential counterattack. This provides data basis for electromagnetic safety assessment of low-voltage distribution system in thermal power plants, and is of great significance for improving reliable operation of low-voltage distribution system in thermal power plants.
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Abstract—We are in the era of information explosion, and everyday a large amount of data are generated from all kinds of sources such as social media, biology, online purchase, etc. It is crucial that we acquire data mining tools to efficiently and effectively analyze the data and gather useful information. There are various aspects of data mining approaches such as clustering, outlier detection and dimension reduction. In this paper we propose an approach to selecting dimensions based on the information of outliers on different dimensions, along with the information of clusters. We also conduct experiments to demonstrate the performance of our algorithm.
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