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A predictive current control with parameter robustness (PR-PCC) method of permanent magnet synchronous motor (PMSM) is proposed in this paper. First, a cost function is designed to calculate the increment of the optimal voltage vector at the current instant, which is based on incremental PMSM model. The incremental PMSM model is used to eliminate the influence of the motor flux parameter. Second, the optimal voltage vector that minimizes the cost function is obtained by combining the increment of optimal voltage vector with the idea of iteration. At the same time, a current observer based on the incremental model is designed to compensate the one-step sampling delay. In this way, the feedback current of dq-axis accurately followed the reference current. In comparison with the conventional pulse width modulation predictive current control (PWM-PCC) method, the proposed method can significantly reduce the steady-state error of dq-axis current caused by inductance, flux and resistance parameters mismatch quickly. Finally, experimental results of the proposed method and the conventional PWM-PCC are presented in this paper to verify the validity of the proposed method.

The model predictive current control (MPCC) technique has been an effective alternative for motor drive applications requiring fast dynamic response. However, the conventional MPCC suffers from high switching losses due to high switching frequency of the inverter. This study proposes a low average switching frequency MPCC method based on boundary‐constrained quadratically extrapolated current trajectory for a permanent magnet synchronous motor drive. First, a quadratic function‐based current prediction model is constructed to obtain the current trajectory in the long‐term future. Second, the boundary constraint is designed to obtain the prediction horizon which is changing in real‐time in order to ensure the validity of the predicted current trajectory. Finally, the cost function established according to the value of the prediction horizon is used to reduce the average switching frequency. In comparison with the conventional MPCC method, the proposed method can significantly reduce the switching frequency of the inverter. In the meantime, fast dynamic response and high steady‐state performance of the conventional MPCC method are preserved. Both simulation and experimental results of the proposed method and the conventional MPCC method are presented to verify the feasibility and advantages of the proposed method.

To improve the safety and economy of aircraft pallet use, an aircraft pallet damage monitoring method based on damage subarea identification and probability-based diagnostic imaging is proposed. In the proposed method, first, the large aircraft pallet monitoring area is divided into rectangular subareas, and a piezoelectric transducer sensor is pasted on each vertex of the rectangular subarea that is used to excitation and sensing the Lamb wave. Second, the damage subarea is identified according to the diagonal damage indexes. Third, the damage position in the damage subarea is calculated using the probability-based diagnostic imaging method and coordinate probability weighted algorithm. Finally, the aircraft pallet damage can be localized based on the damage subarea position. Frequency selection and damage simulation study results show that the Lamb wave is sensitive to aircraft pallet damage whose centre frequency ranges from 50 kHz to 150 kHz, and the damage index of a steel ball is less than that of all real aircraft pallet damage from 95 kHz to 125 kHz. The verification results show that the proposed method can locate aircraft pallet damage with an error of less than 2 cm.

In this paper, by combining the dynamic gain and the self-adaptive neural network, an output feedback fault-tolerant control method was proposed for a class of nonlinear uncertain systems with actuator faults. First, the dynamic gain was introduced and the coordinate transformation of the state variables of the system was performed to design the corresponding state observers. Then, the observer-based output feedback controller was designed through the back-stepping method. The output feedback control method based on the dynamic gain can solve the adaptive fault-tolerant control problem when there are simple nonlinear functions with uncertain parameters in the system. For the more complex uncertain nonlinear functions in the system, in this paper, a single hidden layer neural network was used for compensation and the fault-tolerant control was realized by combining the dynamic gain. Finally, the height and posture control system of the unmanned aerial vehicle with actuator faults was taken as an example to verify the effectiveness of the proposed method.

The Modular Multilevel Converter (MMC) is considered one of the most promising approaches in the field of High Voltage Direct Current (HVDC) transmission. Aim at the unbalance of capacitors voltage of sub-modules (SMs) and the defect of conventional sorting capacitors voltage balancing strategy in switching frequency, an optimized sorting capacitors voltage balancing strategy based on dynamic threshold has been proposed in this paper. Firstly, the topology and operation principle of MMC were analyzed. The superiority was shown by comparative analysis of the conventional sorting capacitors voltage balancing strategy and the optimized sorting capacitors voltage balancing strategy based on dynamic threshold next. The dynamic threshold that obtained through the closed-loop control of capacitors voltage unbalance factor can help to reduce the switching loss. Finally, a simulation verification carried out in Matlab/Simulink shown that the proposed optimized strategy can reduce the switching frequency while limiting the capacitor voltage unbalance factor in a feasible range.

In order to improve the cruising range of electric bus, this paper studies the deadbeat current prediction vector control system of axial flux permanent magnet synchronous motor (AFPMSM) for electric bus based on the optimal torque distribution method. Firstly, the mathematical model of the three stators-double rotors AFPMSM is established. Secondly, in order to improve the high efficiency range, the efficiency optimal torque distribution method is proposed based on the average torque distribution method and the back propagation (BP) neural network is used to find the optimal torque distribution method. Then a current control strategy based on deadbeat current prediction control is proposed to improve the torque tracking characteristics. Finally, a drive control system is developed for the proposed control strategy, and experimental research and vehicle testing are carried out. The experimental results show that the BP neural network-based torque distribution method designed in this paper increases the high efficiency range of the drive system and improves the cruising range of the electric bus. The drive system using a current controller based on deadbeat current prediction control exhibits good dynamic and steady state performance.

In this paper, we study distributed-observer-based fault diagnosis and propose a fault-tolerant control approach for a class of discrete interconnected systems. The distributed fault observers are designed to estimate faults based on the improved fast adaptive fault estimation (FAFE) algorithm. As a result of the improved FAFE algorithm, the constraints which are necessary to the general FAFE algorithm can be reduced while the fault estimation accuracy can be maintained. Based on the online fault estimates, the distributed output feedback controllers are developed to accommodate faults. To solve the observers and controllers, the corresponding algorithms are proposed. Finally, various fault situations are considered in detail in a simulation, and the results verify the accuracy of the theory and method.

In this paper, a new sensorless control scheme with the injection of a high-frequency square-wave voltage of an interior permanent-magnet synchronous motor (IPMSM) at low- and zero-speed operation is proposed. Conventional schemes may face the problems of obvious current sampling noise and slow identification in the process of magnetic polarity detection at zero speed operation, and the effects of inverter voltage error on the rotor position estimation accuracy at low speed operation. Based on the principle analysis of d-axis magnetic circuit characteristics, a method for determining the direction of magnetic polarity of d-axis two-opposite DC voltage offset by uninterruptible square-wave injection is proposed, which is fast in convergence rate of magnetic polarity detection and more distinct. In addition, the strategy injects a two-opposite high-frequency square-wave voltage vectors other than the one voltage vector into the estimated synchronous reference frame (SRF), which can reduce the effects of inverter voltage error on the rotor position estimation accuracy. With this approach, low-pass filter (LPF) and band-pass filter (BPF), which are used to obtain the fundamental current component and high-frequency current response with rotor position information respectively in the conventional sensorless control, are removed to simplify the signal process for estimating the rotor position and further improve control bandwidth. Finally, the experimental results on an IPMSM drive platform indicate that the rotor position with good steady state and dynamic performance can be obtained accurately at low-and zero-speed operation with the sensorless control strategy.

Due to the increase of human indoor activity time, indoor positioning technology attracts more and more researchers. This paper presents an orientation-independent indoor magnetic localization methodology for mobile phone. Unlike the existing method, the localization method proposed in this paper is not affected by phone posture or heading. Through coordinate transformation and sensor ellipsoid fitting correction, an invariable horizontal-vertical model fingerprint of specific location can be acquired. With the assistance of particle filter, an indoor positioning method with 0 ~ 1.2 m accuracy can be realized from any start position. Compared with existing methods, this method has higher practicability and accuracy

The comfort of the indoor environment greatly affects the work efficiency of the personnel. Although “sensation” is a fuzzy conceptualization of ‘satisfaction with the thermal environment’, it is frequently used as a measure for evaluating thermal comfort. Therefore, this is an innovation that the fuzzy rule classifier is used for modeling the thermal prediction based on the ambiguity of the data itself. This paper applies the fuzzy rule classifier to model the thermal prediction based on the PR-821 dataset. The simulation results show that the model has over-fitting but it can be solved by processing the data by SMOTEENN algorithm. The prediction accuracy of the improved model is greatly improved. Compared with other algorithms (CART, C4.5, Adaboost), the results show that the fuzzy rule classifier has a better predictive effect on thermal sensation.

The Modular Multilevel Converter (MMC) is one of the most attractive converter topologies in the High Voltage Direct Current (HVDC) transmission field. The latest widely used sorting method has a low algorithm complexity. It can effectively balance the sub-module (SM) capacitor voltages, but it would cause relatively high switching frequency and power loss. Aiming at the problem that the sub-module (SM) capacitor voltage sorting algorithm has a large switching loss due to the high switching frequency of the device, this paper proposes an auto-optimized capacitor voltage balancing control strategy. Firstly, the topology and operation principle of MMC are analyzed. Secondly, a SM capacitor voltage control method based on the dynamic deviation threshold is proposed. Considering the switch switching state of the SM and the difference between the voltages of each SM, the algorithm can obtain the dynamic deviation valve using the closed-loop control. The method can avoid the unnecessary repeated switching of the Insulated Gate Bipolar Transistor (IGBT) under the premise of ensuring that the capacitance voltages of the SMs are basically the same, which effectively result in reducing the switching frequency of the MMC SM and reducing the switching loss, thereby improving the operating efficiency of the system. Finally aiming at the proposed control strategy, the simulation and experimental verification are carried out which shows that the proposed algorithm can better control the system voltage deviation, reduce the switching loss of the system and improve the stability of the system.

This paper proposes a fault diagnosis and fault-tolerant control method for a system with a fast time-varying delay and time-varying parameters. A fault observer is designed to estimate faults, and an improved fast adaptive fault estimation (FAFE) algorithm is developed to reduce the relevant constraints in the general form of this algorithm. With newly introduced relaxation matrices, this study estimates faults in a system exhibiting a fast time-varying delay. Based on the estimated faults, an output feedback controller is designed to accommodate the faults. The fault-tolerant control is realized using the introduced relaxation matrices. An algorithm is derived to solve for the observer and controller. Finally, the theory and method are validated using a real example of a helicopter system.

Aiming at the function and technical requirements of high-power photovoltaic cell simulation, high-performance programmable logic power supply and dc motor simulation, a high frequency isolation hybrid topology and control strategy based on current-source/voltage-source converter was studied and proposed. Firstly, according to the performance requirements of photovoltaic cell analog power supply, the control strategy requirements of the high-precision wide-range hybrid topology were proposed. Secondly, the working principle of the new hybrid topology was analyzed. At the same time, the equivalent model of the new hybrid topology was simplified and established, and the overall control strategy of the hybrid topology based on current compensation and sliding mode variable structure was proposed. Finally, simulation and experimental research on the hybrid topology was carried out, and the experimental test of photovoltaic cell simulation was completed. The simulation and experimental results show that the hybrid topology and control strategy proposed in this paper has the characteristics of wide-range output regulation, fast dynamic response, high efficiency and high power factor, and can be used for high performance photovoltaic cell simulation, programmable logic power supply and DC motor simulation.

In this paper, a data-driven power transformer anomaly detection method is presented. For transformers with multiple working states and nonlinearity of data, adaptive kernel fuzzy C-means clustering (KFCM) algorithm is used to cluster sample data, each class corresponds to a working state. Then, the kernel principal component analysis (KPCA) is used to obtain projection matrices and anomaly detection limits of various classes. A method for online update of sample data is designed in this paper. Finally, the measured data of the power transformer is used for experiment, and the results are compared with the results obtained by using the conventional KPCA algorithm. The experimental results prove the correctness and effectiveness of the method in this paper.

Gradient descent optimization algorithm is very important in deep learning. In order to obtain a more stable convergence process and reduce overfitting in multiple epochs, we propose an improved Adagrad gradient descent optimization algorithm in this paper. Our approach is tested both on the Reuters dataset and the IMDB dataset with many gradient descent optimization algorithms. The results show that our approach has a more stable convergence process and can reduce overfitting in multiple epochs.

LED visible light communication indoor positioning method based on the mobile phone at any horizontal orientation angle. The system includes a single LED downlight, a mobile phone orientation sensor, a mobile phone front single camera and a positioning system. The steps include: the orientation sensor of the handset is used to measure the orientation of the handset in the horizontal direction; the developed camera is used to call the front camera of the handset to record the LED downlight image; and the image is processed and decoded to obtain the ID information of the LED downlight, which is the three-dimensional position coordinates information of the downlight in the indoor space. Take the previously obtained information including the handset horizontal orientation and downlight ID information into the developed indoor positioning method to obtain the real-time accurate position of the mobile phone. The positioning method is simple and easy to operate. The mobile phone can be accurately positioned in real time at any horizontal orientation angle just by using the front camera of mobile phone in the case of just a single LED downlight. This method overcomes the problem that even if the position of the camera is kept unchanged, the positioning result of the mobile phone is different due to the difference of the horizontal orientation of the mobile phone. This method is more feasible and accurate.

As technology advances, wireless charging system of electric vehicles(EVs) based on magnetic resonance becomes prevalent. A novel parameter identification method of primary side compensation capacitance and coil inductance in wireless charging system is proposed. According to the typical resonance equivalent circuit of wireless charging system, current effective value equation of primary side is established. And then, based on the current effective value equation, input signal frequency constraint equation of current extremum is established. By detecting primary side real-time current and using Fourier transform on primary side transient current waveform, two characteristic quantities are extracted. Finally, unknown parameters (primary side capacitance and inductance) in both equations are identified utilizing the adaptive particle swarm optimization. The simulation results show that the method proposed is feasible and the identification accuracy is high.

Most state-of-the-art anomaly detection methods are specific to detecting anomaly for pedestrians and cannot work without adequate normal training videos. Recently, there is a growing demand for detecting anomalous vehicles in traffic surveillance videos. However, the biggest challenge in this task is the lack of labeled datasets for training supervised models. By examining the resemblances of anomalous vehicles, we find it reasonable to label a vehicle as anomaly if it stays still in the video for a relatively long time. Utilizing this property, in this paper we introduce a novel unsupervised anomaly detection method for traffic surveilliance based on background modeling, which shows great potentials in handling heterogeneous scenes as well as extremely low resolution videos recordings without the dependence on labeled data. In the proposed system, we first employ background modeling using MOG2 to remove the moving vehicles as foreground while keeping the stopped vehicles as part of the background. Then we use Faster R-CNN to detect vehicles in the extracted background and decide if they are new anomalies under certain conditions. All information is updated on a frame basis until the end of the video which contains the final results. In this way, we make full use of the characteristics that abnormal vehicles stay in the scene for a relatively long time and reduce the difficulty of vehicle anomaly detection. Eventually, we can detect almost every anomaly in the NVIDIA AI CITY CHALLENGE track-2 dataset except for several extremely complex cases with a 81.08% F1-score and 10.2369 RMSE.

The three-phase current source inverter has the characteristics of accurate current control, high dynamic response speed and strong current limiting ability, and the DC side voltage can be adjusted from zero. In this paper, the three-phase current source inverter topology is studied, the mathematical model of the converter is established in the three-phase static coordinate system and two-phase rotating coordinate system, and get the equivalent circuit in two coordinates. The current modulation method including SPWM and SVPWM is studied. A new type of system control strategy based on two-phase rotating coordinate system is proposed. The simulation model of the whole system is established by PSIM. The results show that the proposed new control strategy is correct and feasible.

Taking double stage type three phase photovoltaic grid-connected generation system as the research object, the equivalent model of photovoltaic cells, the maximum power point tracking (MPPT) technology and the control strategy of three-phase grid connected inverter are analyzed in this paper. On the basis of the theoretical analysis, the double closed-loop vector control of the double stage type three-phase grid-connected inverter and the MPPT method based on T-S fuzzy neural network are studied deeply, and the relevant simulation and experimental research are carried out. The experimental results show that the new grid-connected control strategy can make the grid-connected system run with high power factor, high efficiency and low total harmonic distortion, and the grid-to-network voltage and current distortion is low, which proves the effectiveness and feasibility of the control strategy.

With the popularity of the concept of green energy, reducing the harmonic pollution of power equipment to power grid has become an important research topic in the field of power electronics. This paper takes three-phase current-source PWM rectifier system as the research object, and it presents a current-source PWM pulse signal tri-logic control method, and puts forward a pre-modulation method. Then the topology of the system is studied and a new equivalent model is proposed. After that the closed-loop control strategy of the system is studied, and a PI tuning method is obtained. Finally, the control strategy is simulated by PSIM and the experimental platform is set up for experimental verification.

The visualization of large networks is a challenging problem due to the limitation of human visual perception. Sampling technology is an essential strategy in most of large network visualization and analysis. Moreover, when we consider large heterogeneous networks consisting various types of nodes and links, which has attracted much attention from multiple disciplines, it is much more difficult for users to explore and mine some useful information in such complex networks. Thus to design sampling methods for large heterogeneous network visualization has become a challenge. However, most of existing sampling methods ignore the types of nodes and links, hence they could not be used for heterogeneous network directly. In this paper, we propose an eigenvector centrality based sampling method considering both multiple types of nodes and links, supporting sampling the heterogeneous networks directly and efficiently. Furthermore, we develop HeteVis, a prototype visualization system with the proposed sampling algorithm built-in. The parameters of HeteVis can be dynamically personalized through user interactions and such system can help users to explore real large heterogeneous networks easily and flexibly. Extensive case study using the UCI KDD Movie network demonstrates how users can apply HeteVis to explore the heterogeneous networks.

In this paper, an adaptive robust drive control system for an axial flux permanent magnet synchronous motor of an electric medium-sized bus based on the optimal torque distribution method is studied. The drive control system is mainly divided into two parts. First, a torque distribution method is proposed. The optimal torque distribution method based on particle swarm optimization algorithm is used to increase the high efficiency interval of the system and apply it to the energy feedback braking. Secondly, in order to reduce the nonlinear disturbance of the system and improve the accuracy of the unified control, this paper models and studies the vector system based on adaptive robust control. Finally, the whole drive control system is modeled, simulated and experimented. The simulation and experimental results show that the torque distribution method proposed in this paper can effectively increase the high-efficiency running time of the electric medium bus, and improve the shortcomings of insufficient mileage of the electric medium-sized bus. The use of a current controller based on adaptive robust control improves the control accuracy of the drive system and can effectively suppress the disturbances generated by it.

In this paper, a sliding mode vector control system based on collaborative optimization of an axial flux permanent magnet synchronous motor (AFPMSM) for an electric vehicle is proposed. In order to increase the high efficiency range of electric vehicles and improve the cruising range, a collaborative optimization control strategy is firstly proposed. Due to the use of a dual stator-single rotor AFPMSM, the multi-motor efficiency optimization map and torque cooperative control are used to realize the working mode conversion of single stator and double stator, and the torque ripple caused by the working mode conversion is improved by fuzzy control. In order to improve the torque tracking capability, speed limiting characteristics, and operating characteristics, a speed limit and current vector control strategy based on a sliding mode controller is proposed and studied. The dynamic performance of electric vehicles is improved by a sliding mode vector control. Finally, a drive control system was developed for the proposed control strategy, and the complete vehicle test was carried out. The collaborative optimization control experiment and torque tracking and speed limiting experiments verify the correctness and effectiveness of the proposed control strategy. The acceleration performance and endurance experiments show that the proposed control strategy can effectively improve the cruising range and the acceleration performance of electric vehicles.

A hybrid method which combines time series model and artificial intelligence method is proposed in this paper to improve the prediction accuracy of building thermal load. Firstly, a simple auto regressive (AR) model is utilized to predict present load using previous loads, the order and the parameters of AR model are identified by the data produced by DeST. Then, a 3-layer back-propagation neural network optimized by particle swarm optimization (PSO) neural network (PNN) is set up to predict the error which is derived by comparing the precious AR predicting load. The error and its corresponding meteorological data generate the training sample data. At last, the hybrid model, named autoregressive and particle swarm neural network (APNN), is obtained. It uses historical load information and real-time meteorological data as input to predict a refined real-time load by adding error to preparative load. To evaluate the prediction accuracy, this hybrid model APNN is compared with several common methods via different statistical indicators, the result show the APNN hybrid method has higher accuracy in thermal load forecasting.

Due to the curse of the dimensionality, high-dimensional data visualization has always been a difficult and hot problem in the field of visualization. Some of the existing works mainly use dimensionality reduction methods to generate latent dimensions and visualize the transformed data. However, these latent dimensions often have no good interpretability with user knowledge. Therefore, this paper introduces a high-dimensional data visualization method based on user knowledge. This method can derive dimensions aligned with user knowledge to reorganize data, then it uses scatter-pie plot matrix, an extension of scatter plot matrix, to visualize the reorganized data. This method enables users to explore the relationship between the known and unknown data as well as the relationship between the unknown data and the derived dimensions. The effectiveness of the method is validated by the experiments.

In contrast to conventional radars, phased array radars have the capability to switch the direction of the radar beam very quickly without inertia. The measurements from phased array radar can contribute to many application fields such as data and intelligence process and radar performance evaluation. However, it often costs more than we can bear to obtain phased array radar measurements. It is necessary to model and simulate the phased array radar, especially for missile-borne phased array radar. This chapter lays a strong emphasis on the search and simulation technology of missile-borne phased array radar. According to operational theory of phased array radar, this chapter focuses on the functional modeling and simulation techniques. It contains three parts: beam arrangement of phased array radar in sin coordinate, parameter optimization of missile-borne phased array radar, and a function simulation model of missile-borne phased array radar.

According to the good visualization interface and WiFi wireless function of the Android, We put forward a kind of pressure strength training system for medical students teaching, formulating the basic standards of the pressing position and the strength on the acupoint by experienced doctors, aims to training qualified students. Firstly using a low-power STM32 family of processors to complete data collection and AD conversion, and then develop Android mobile terminal software, as a visual teaching interface. We get the medical teaching purposes by interactive feedback wireless communication.

This paper proposes a new phase noise compensation scheme for 60 GHz OFDM system employing optimal blind data detection based on quantum genetic algorithm to alleviate performance degradation due to the phase noise. High data-rate OFDM system in 60-GHz millimeter-wave band have been extensively studied, but the relatively large phase noise in the phase locked loop (PLL) synthesizer severely degrades transmission performance. This proposed scheme employ an optimal (maximum a posteriori) data estimator, utilizing prior statistical knowledge of PHN. In addition, quantum genetic algorithm is used to further reduce the computational complexity. Simulation results under 802.15.3C system show that the proposed scheme mitigates the degradation due to PHN effectively over both AWGN channel and frequency selective fading channel.

BikeNet is a class of mobile sensing system for cyclists characterised by limited bandwidth capacity, small size and high mobility. Thus, the data propagation process follows the principles and techniques of opportunistic networks. However, as the mobile devices embedded in the bicycle are managed by rational individuals, the selfish behaviours that attempt to conserve their resources can significantly degrade the network performance. In this paper, a secure social-aware credit-based stimulating mechanism for BikeNet, called SOS, is proposed to encourage the cooperation among nodes while resisting the malicious attacks. The social context information is utilised to formulate the routing and data forwarding strategy. Moreover, to guarantee the security of nodes and enhance the confidentiality and integrity of messages, we propose identity-based cryptosystems based on social attributes for BikeNet. Security analysis and performance evaluation demonstrate that SOS can effectively stimulate the cooperation of nodes while keeping the confidentiality and integrity of messages.