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Brain-Computer Interface (BCI) is a revolutionary technique that employs wearable electroencephalography (EEG) sensors and artificial intelligence (AI) to monitor and decode brain activity. EEG-based motor imagery (MI) brain signal is widely utilized in various BCI fields including intelligent healthcare, robot control, and smart homes. Yet, the limited capability of decoding brain signals remains a significant obstacle to BCI techniques expansion. In this study, we describe an architecture known as the dual-branch attention temporal convolutional network (DB-ATCNet) for EEG-based MI classification. DB-ATCNet improves MI classification performance with relatively fewer parameters by utilizing a dual-branch convolutional network and channel attention. The DB-ATCNet model consists of two primary modules: attention dual-branch convolution (ADBC) and attention temporal fusion convolution (ATFC). The ADBC module utilizes a dual-branch convolutional network to extract low-level MI-EEG features and incorporates channel attention to improve spatial feature extraction. ATFC employs sliding windows with self-attention to obtain the high-level temporal features, and utilizes feature fusion strategies to minimize information loss. The DB-ATCNet achieved subject-independent accuracies of 87.33% and 69.58% in two-class and four-class classification tasks, respectively, on the PhysioNet dataset. On the BCI Competition IV-2a dataset, it achieved an accuracy of 71.34% and 87.54% for subject-independent and subject-dependent evaluations, respectively, surpassing existing methods. The code is available at https://github.com/zk-xju/DB-ATCNet.

This paper focuses on the power grid oscillation of grid-side converter (GSC) in doubly-fed induction generator (DFIG) caused by grid sub-synchronous oscillation (SSO), and designs a measure to improve the output power quality of GSC. Firstly, the influence mechanism of multipath disturbance of GSC under SSO is sorted out, and the disturbance factors and action modes are clarified. Secondly, the influence of SSO on the output estimation of phase-locked loop (PLL) in the control strategy and power calculation process is analyzed. Furthermore, the mathematical model of GSC output power considering the influence of PLL is established. At the same time, the key factors of SSO for GSC power oscillation suppression strategy are determined by analyzing the oscillation suppression effect of quasi-resonant controller when SSO amplitude, frequency and phase change. Based on the above theoretical analysis and research, the resonant controller is used to eliminate the estimation error of the PLL; at the same time, an adaptive algorithm is designed according to the mechanism analysis of SSO characteristic changes to improve the fixed resonant frequency of the quasi-resonant controller, and a DFIG-GSC sub-synchronous power oscillation suppression strategy based on the adaptive quasi-resonant controller is proposed, thus eliminating the influence of SSO on the multipath disturbance of GSC and improving the power quality of its output. Finally, the effectiveness of the proposed suppression strategy is verified by simulation and experimental results.

The traditional on-board charging system can only realize the one-way transmission of electric energy from the grid to the power battery. To address this issue, this paper proposes a bidirectional on-board charging system based on a three-phase wye-wye connected CLLLC resonant converter. It adopts a two-stage structure, the front-stage is a bidirectional totem pole converter and the rear-stage is a three-phase wye-wye connected CLLLC resonant converter. Based on the grid characteristics, the working principle of the front-stage converter is analyzed, the equivalent circuit model of the rear-stage converter is derived using fundamental wave analysis, and the voltage gain, impedance characteristics and zero voltage turn-on conditions are analyzed. The parameters of input inductor, DC bus capacitance and resonant network are calculated according to the design specifications of the bidirectional on-board charging system. The control strategy of bidirectional vehicle charging system is studied. The input current and input voltage are kept in the same phase by establishing the small signal circuit model of the front-stage bidirectional totem pole converter. The rear-stage three-phase wye-wye connected CLLLC resonant converter adopts pulse frequency modulation to realize constant voltage and constant current output of the charging system. Simulation software was used to simulate the bidirectional on-board charging system, and a 3.3 kW prototype was trial-produced according to the main circuit parameters. Simulation and experimental results verify the correctness of the design of the bidirectional on-board charging system.

Motor driving technology and battery charging technology are the two core technologies of electric vehicles (EVs). They have a profound impact on the performance and mileage of EVs. Based on the topology of a traditional EV motor controller, this study employs the time-sharing multiplexing insulated gate bipolar transistor (IGBT) power module form to expand the functions of the motor controller. This paper proposes an integrated driving–charging controller structure that enables the controller to realize the motor driving and on-board charging simultaneously. A decoupling analysis was performed at the topology level, mathematical models of two bidirectional converters in the integrated topology were established, and a reasonable control strategy for the double-closed-loop control system was designed. The simulation system was built in Simulink, and the simulation results verified the effectiveness of the system topology and control strategy. We made an integrated controller experimental platform based on TMS320F28335. A resistive load simulated the power battery to verify the charging mode, and a 5 kW permanent magnet synchronous motor (PMSM) was used to verify the driving mode. The experimental results demonstrated the driving–charging integrated controller’s feasibility for electric vehicles.

During the power transmission of doubly-fed induction generator (DFIG), due to the influence of series compensating capacitance and long-distance transmission, DFIG is prone to sub-synchronous oscillation, which damages the stability of the system. By establishing the mathematical model of DFIG system, the cause of sub-synchronous oscillation and its influence on the control strategy of DFIG system are discussed. In order to solve the problem of performance degradation of traditional phase-locked loop (PLL) under sub-synchronous oscillation, an improved PLL is proposed to replace the traditional PLL. Aiming at the problem that the control of rotor side converter(RSC) and grid side converter(GSC) in doubly-fed wind power generation system under sub-synchronous oscillation is disturbed by harmonic signals, a control method of adding a quasi resonant controller in the control link of RSC and GSC to suppress sub-synchronous oscillation is proposed, and the feasibility of the method is verified by simulation and experiment. Finally, based on the research process of RSC direct resonance control, the sub-synchronous oscillation suppression strategy based on harmonic current extraction is proposed for the frequency adaptability of the quasi resonant controller. The actual performance of the sub-synchronous oscillation suppression strategy is verified through simulation and experiment. The experimental results show that the strategy is effective.

Unmanned aerial vehicles (UAVs) have played an important role in recent high-tech local wars. Seizing air control rights with UAVs will undoubtedly be a popular topic in future military development. Autonomous air combat is complex, antagonistic and mutable, and consequently, the decision-making that depends on unmanned systems is extremely challenging with very little research having been conducted on it. An intelligent air combat learning system inspired by the learning mechanisms of the brain is proposed in this paper. In accordance with research on learning, knowledge and memory, we constructed a cognitive mechanism model of the brain. Based on this model and the inferential abilities of humans, a long short-term hierarchical multi-line learning system is established. Then, the bio-inspired architecture and the basic learning principle of the system are clarified. Taking advantage of the conclusions of studies on information theory, the relationship between the knowledge updating cycle and the system learning performance is analysed. The updating cycle length adjustment problem is transformed into an optimization problem optimization problem, and system performance improvement is guaranteed. Experiments show that the system designed in this paper can acquire confrontation abilities through self-learning without prior rules; the parallel universe mechanism can significantly improve the system's learning speed when the number of parallels is within 40, and the performance of the system improves gradually and continuously. The system can master actions similar to classical tactical manoeuvres such as the high yo-yo and the barrel-roll-attack without prior knowledge. Compared with the Bayesian inference and moving horizon optimization (BI&MHO) method, the learning system proposed in this paper is more flexible in situation assessment and in the prediction of opponents' actions. Although it cannot be deployed quickly, it has a continuous learning ability.

Information security is a key issue involved in the utilization of different technological innovations, such as the Internet of Things (IoT). This study proposes a new covert communication scheme in which the transmitted waveform modulated by the covert bit is statistically similar to the background noise to achieve information security. The impulsive noise in many communication fields can be well modeled with a symmetrical α -stable distribution. Thus, the proposed scheme exploits skewed α -stable distributions to conduct the covert bit. The polarity transition of the skewness parameter within the first and second halves of a bit period represents a covert bit. The resultant signal exhibits a near-symmetrical behavior similar to ambient interference to achieve concealment. The sample skewness estimator based on the signed fractional lower order moment is used to determine the binary message at the receiver. Then, the expression of the skewness estimator and its probability density function are derived. With the aid of the hard decision, the theoretical expression of the bit error rate of the proposed communication system is obtained. The proposed scheme works at the physical layer with the advantages of simple structure, easy implementation, and extreme suitability with low-cost, resource-limited IoT devices exhibiting low-rate transmission.

The doubly fed induction generator with super capacitor is helpful to enhance the system inertia. At the same time, its structural characteristics are more suitable for the power grid topology without communication lines. However, the change of its topology also brings some problems. In this article, when SCESS-DFIG is participating in frequency regulation, DC bus voltage fluctuation caused by power imbalance of multiple converters is studied. The dynamic power response of super capacitor with DC-DC converter is a step load for grid side converter. The load of DC bus is nonlinear and random, so the balance and stability of the original system will change accordingly. Because of the complex topology and system composition of the doubly fed induction generator with super capacitor, the conventional feed-forward compensation control strategy can not be used to improve its dynamic response speed. Therefore, this article designs a control strategy which is applied for DC bus voltage fluctuation of the doubly fed induction generator with super capacitor. The theory proposed in this article is verified by simulation model and experiment.

In this article, real-time detection of moving targets is performed, and an improved moving target detection algorithm combining background modeling and three-frame difference method is studied. An improved anisotropic differential filtering algorithm is proposed. The algorithm mainly compares the gradient difference between the target and the background in eight nearby directions. Choose the average of the three directions with the smallest spread function value to filter the space, which will effectively highlight the difference between the targets. so that the target signal is well preserved in the differential space domain; then based on obtaining the difference map. The average gray level of the target and the local signal-to-noise ratio of the spatial domain obtained by the energy enhancement algorithm combining the spatial and temporal motion characteristics have reached 278 and 14.84 dB, which further improves the target signal. Strict registration of heterogeneous spatial domains is completed by affine transformation and bilinear interpolation, and in many cases, the invariance and robustness of the method are verified. Focus on the proposed color space fusion method based on spatial frequency and fuzzy transformation. The superiority of the methods is evaluated and compared from both subjective and objective aspects. Finally, it lays a good foundation for moving target detection and better dealing with complex scenes. The improved inter-frame difference method combined with background subtraction validates and compares the method in different scenarios. It shows the advanced nature of the method in both qualitative and quantitative aspects. And effectively overcome the shortcomings of the particle filter tracking algorithm based on a single feature.

In this paper, an accurate off-line map-matching (OM2) system is designed for complex trajectory networks. It is difficult to complex trajectories input into the hidden Markov model (HMM) directly. OM2 includes three key modules that are pre-processing, map-matching based on weight adaptation HMM (WA-HMM), and post-processing. The pre-processing module divides complex multi-trajectory into single-trajectory sets based on the self-defined trajectory division model (TDM) of crossroads. Another core module is the WA-HMM based on Boxplot, which is used to balance efficiency and accuracy of off-line map-matching. The post-processing is used to map the points of the crossroads so as to further improve the map-matching accuracy. OM2 employs the actual GPS trajectories of the internet company and road map of Sichuan province police GIS (PGIS). Our evaluation results show that the accuracy is about 98%, which is suitable for off-line map-matching and solves the problem of complex trajectory network matching being difficult and time-consuming.

Watermarking is an effective solution for copyright protection and forensics tracking via hiding information into the image signal. Recently, spatial-embedding watermarking methods from different transform domain have been proposed rapidly and effectively protect the copyright of the color image. Compared to the individual frequency domain method, it has both advantages of spatial domain and frequency domain. Here, we proposed a novel spatial-embedding watermarking method based on an attended just noticeable difference (JND) model with color complexity, to achieve a good tradeoff between robustness and perceptual quality. In particular, at the spatial embedding level, the direct current (DC) coefficients are selected for embedding and the perceptual JND model is used to guide the amount of pixel modification to improve visual quality. Different from the previous JND model, we proposed an attended JND model that considers color complexity, which is more consistent of the human visual perception model. Compared with the other JND models, the proposed JND model is more suitable for the watermarking framework. Experimental results show that the proposed watermarking scheme has better performance than other existing watermarking schemes. This greatly benefits the practical implementations of the spatial-embedding watermarking methods.

At present, the sorting of agricultural products in China mainly relies on manual labour, which results in low efficiency, and the development of corresponding automatic equipment lags behind. The grasping method based on machine vision has been widely used in industry, and can provide a reference for the automatic sorting of agricultural products. In this paper, an automatic sorting device for agricultural products was designed. The grasping mechanism adopted a 4-degree-of-freedom (4-DOF) manipulator, and the machine vision control system adopted a monocular camera, which can realize the positioning and classification of the grasp-target. First, the geometric model of the manipulator was established, and the kinematics model of the manipulator was established via the Denavit-Hartenberg (D-H) parameter method. Next, the kinematics analysis and verification were carried out. Then, Zhang Zhengyou calibration method was used to calibrate the camera. An image processing method based on histogram correction was proposed. Based on this, a target positioning algorithm based on the pinhole imaging principle and a target classification algorithm based on the area threshold were established. Finally, an automatic sorting test platform for agricultural products using a visual servo was built. Target classification, positioning and sorting tests were conducted using tomatoes and oranges as the test objects. The test results show that the success rate of the target positioning is close to 98%, that of the target classification is close to 98% and that of the grasping is close to 95%. Furthermore, the sorting time of a single target object can be as fast as 1 second, which can meet the requirements of automatic sorting for common agricultural products. The automatic sorting device for agricultural products has a simple structure, reliable performance and low costs. The structure and algorithms proposed in this paper are simple, reliable, and highly efficient and thus can easily realize technology transplantation. These relevant methods provide a theoretical reference for the development of an automatic sorting device for agricultural products.

The partial discharge (PD) in inverter-fed motors generated from high frequency and short rise time impulsive voltage are more complex than traditional sinusoidal voltage operated motors due to the noise initiated from fast switching power electronics devices. Available PD sensors and related technology for DC and sinusoidal voltage cannot be used at repetitive impulsive voltage conditions. This work aims to report the design of antenna with optimized geometry and satisfied parameters to detect PD for impulsive voltage. The antenna is verified and the influence of both impulse voltage parameters and test configuration on PD features are discussed. The envelope detection technique after the filter in the frequency domain was proposed to reduce the complexity and cost of PD test hardware under impulsive voltages with fast rise times. The frequency domain energy of PD from motor insulation is mainly distributed in the range of 0.6-1.8 GHz. By increasing the amplitude and frequency of input impulsive voltage, the range of PD energy spectrum is enlarged to (1.5-1.8 GHz) and by decreasing input amplitude the PD signal strength decays abruptly. Moreover, the high-frequency distribution of PD energy attenuates more severely with the distance and the electromagnetic energy of PD decays nonlinearly with the increase of propagation distance. Therefore, under the premise of satisfying the safety test, selecting the test distance below 15 cm can ensure that the test results have a high signal-to-noise ratio and signal integrity. The experience reported in this study could provide suggestions for off-line PD measurements for inverter-fed motor insulation evaluations for manufacturers.

Lithium-ion battery refers to a complex nonlinear system. Real-time diagnosis and accurate prediction of battery state of charge(SOC) parameters are hotspots and critical issues in battery research. To reduce the dependence of state of charge prediction on battery model accuracy and speed, and achieve real-time online estimation, a SOC prediction model of lithium-ion battery system is developed based on the model of support vector machine (SVM). SVM parameter is optimized using an algorithm of particle swarm optimization, and the performance of prediction model is assessed using cross-validation. The obtained experimental data is simulated, involving the comparison with the support vector machine model, and the prediction simulation of the battery in the state of fault. The results reveal that this model with a better performance than that of the support vector machine exhibits high accuracy and generalization ability.

To solve problem of the reliability and consistency of silver-zinc batteries after being sorted into groups, a proposed classification strategy of zinc-silver battery based on least squares support vector machine with PSO (PSO-LSSVM) was proposed in this paper. Sample data was extracted from the charging curve of silver-zinc batteries to pre-sort training samples using FCM clustering. The least squares support vector machine model parameters were optimized and improved using particle swarm optimization algorithm. The method breaks the limitation of building battery classification model based on prior knowledge, reduces the dependence on parameter selection, and enhances model training speed and accuracy. In the end, experimental data was used for battery classification model training and testing. Test results show that the battery pack obtained by the group strategy has good dynamic consistency, the rate of capacity decay is significantly reduced. The rate of capacity decay is no more than 10% after 30 cycles of life test. The silver-zinc battery group classification strategy proposed to this paper improves the consistency and reliability of the battery and the life of battery packs.

Recent years, many scientists address the research on text sentiment analysis of social media due to the exponential growth of social multimedia content. Natural language ambiguities and indirect sentiments within the social media text have made it hard to classify by using traditional machine learning approaches, such as support vector machines, naive Bayes, hybrid models and so on. This article aims to investigate the sentiment analysis of social media Chinese text by combining Bidirectional Long-Short Term Memory (BiLSTM) networks with a Multi-head Attention (MHAT) mechanism in order to overcome the deficiency of Sentiment Analysis that is performed with traditional machine learning. BiLSTM networks, not only solve the long-term dependency problem, but they also capture the actual context of the text. Due to the fact that the MHAT mechanism can learn the relevant information from a different representation subspace by using multiple distributed calculations, the purpose is to add influence weights to the constructed text sequence. The results of the numerical experiments show that the proposed model achieves better performance than the existing well-established methods.

To improve the performance of a current loop, this paper presents a novel current control scheme for an interior permanent magnet synchronous motor (IPMSM) based on the model predictive control (MPC) algorithm in a synchronous rotating frame (dq-frame). The recently developed explicit MPC (EMPC) is introduced to ensure the feasibility of real-time implementation in the control hardware. To achieve feasible reformulation of the current control problem using EMPC, a coupled nonlinear IPMSM mathematical model is linearized using an augmented model with disturbance. Furthermore, to approximate the related quadratic stator current and voltage constraints in the dq-frame, they are also transformed into a series of linear inequalities. We propose an improved disturbance observer based on the augmented model, in conjunction with the concept of offset-free MPC, to estimate both the disturbance terms and the state variables from the predicted and measured outputs. All the influences of plant/model mismatches and un-modeled nonlinear terms are removed by the estimated total disturbance within the closed-loop framework of EMPC. The proposed EMPC scheme not only improves both the dynamic performance and the steady-state precision of the current loop, but it also exhibits robustness against parameter uncertainties. The proposed method has been proven and verified successfully in both simulation and experiment.

The enhanced phase-locked loop (EPLL) adds an amplitude estimate to the conventional phase-locked loop (PLL), which solves the problem of double-frequency disturbance in the Phase-Locked Loop mathematical model at steady state. However, there are few researches on disturbance resistance design of EPLL in complicated grid environment. Therefore, periodic disturbances of different frequencies are introduced into frequency estimation and amplitude estimation of EPLL when the grid voltage has DC offset, three-phase unbalance, high-order harmonic disturbance and sub-synchronous oscillation (SSO). This will interfere with the frequency and amplitude estimation performance of the EPLL, and then affect the closed-loop control and frequency tracking of the converter. In order to improve disturbance rejection of the EPLL. Firstly, the mathematical model of the disturbance in the frequency and amplitude loop is deduced and analyzed. Then the active disturbance rejection control (ADRC) is used to improve the EPLL. Through the simulation model of MATLAB/Simulink, the parameters of the ADRC and the ESO are compared and validated. The proposed EPLL based on ADRC is verified experimentally for the tracking performance under the frequency stability or drop in complicated power grid environment.

Camera pose estimation from the image of a planar object has important applications in photogrammetry and computer vision. In this paper, an efficient approach to find the initial solutions for iterative camera pose estimation using coplanar points is proposed. Starting with homography, the proposed approach provides a least-squares solution for absolute orientation, which has a relatively high accuracy and can be easily refined into one optimal pose that locates local minima of the according error function by using Gauss-Newton scheme or Lu's orthogonal iteration algorithm. In response to ambiguities that exist in pose estimation from planar objects, we propose a novel method to find initial approximation of the second pose, which is different from existing methods in its concise form and clear geometric interpretation. Thorough testing on synthetic data shows that combined with currently employed iterative optimization algorithm, the two initial solutions proposed in this paper can achieve the same accuracy and robustness as the best state-of-the-art pose estimation algorithms, while with a significant decrease in computational cost. Real experiment is also employed to demonstrate its performance.