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Suspended foreign objects on transmission lines will shorten the discharge distance, easily leading to phase-to-ground or phase-to-phase short circuits, which induces outage accidents. Foreign objects are small and difficult to identify, resulting in low detection accuracy. An improved foreign object detection method based on Swin Transformer V2 and YOLOX (ST2Rep–YOLOX) is proposed. First, the feature extraction layer ST2CSP constructed by Swin Transformer V2 is used in the original backbone network to extract global and local features. Secondly, hybrid spatial pyramid pooling (HSPP) is designed to enlarge the receptive field and retain more feature information. Then, Re-param VGG block (RepVGGBlock) is introduced to replace all 3 × 3 convolutions in the network to deepen the network and improve feature extraction capabilities. Finally, experiments are carried out on the transmission lines foreign object image dataset, which was obtained using unmanned aerial vehicles (UAVs). The experimental results show that the average accuracy of the ST2Rep–YOLOX method can reach 96.7%, which is 4.4% higher than that of YOLOX. The accuracy of the nest, kite, and balloon increased by 9.3%, 15.4%, and 9.6%, and the recall increased by 6.5%, 9.4%, and 2.5%, respectively. This method has high detection accuracy, which provides an important reference for foreign object detection in transmission lines. [ABSTRACT FROM AUTHOR]

The sufficient extraction of high and low frequency features is the key to solving the loss of reconstructed image details and ensuring the maximum restoration of useful information. This needs to fully exploit the effective features in the low resolution image and give play to the complementary advantages of different convolution kernels by fusing different features. Therefore, a multi-scale fusion single image super-resolution reconstruction method Be-MRN is proposed following the eagle vision system. Inspired by the eagle eyes to recognize objects from three directions, we design an eagle-eye feature extraction module using three ways in parallel to extract feature. It uses the principle of cross-field of left and right eyes to fuse information in three channels, and uses dilated convolutions to mine effective features. The feedback module receives effective features, uses the attention mechanism to capture cross channel interactive information and eliminates redundant information, and then completes deep feature extraction through four layers of iteration to ensure the complementary interaction and deep mining between different features. The multi-scale reconstruction module uses different convolutions to extract complementary information of deep features, and uses multi-branch in parallel to complete image reconstruction. Experiments show that, for Urban100 with scale factor × 4, when compared to MSICF, Be-MRN increases the PSNR and SSIM values by 1.8% and 2.1% respectively. For the four datasets with × 2, × 3 and × 4 scale factors, Be-MRN obtains the highest objective index, which can highlight the edge and texture information of the image. [ABSTRACT FROM AUTHOR]

Analysis of the occlusion relationships between different objects in an image is fundamental to computer vision, including both the accurate detection of multiple objects' contours in an image and each pixel's orientation on the contours of objects with occlusion relationships. However, the severe imbalance between the edge pixels of an object in an image and the background pixels complicates occlusion relationship reasoning. Although progress has been made using convolutional neural network (CNN)-based methods, the limited coupling relationship between the detection of object occlusion contours and the prediction of occlusion orientation has not yet been effectively used in a full network architecture. In addition, the prediction of occlusion orientations and the detection of occlusion edges are based on the accurate extraction of the local details of contours. Therefore, we propose an innovative multitask coupling network model (MTCN). To address the abovementioned issues, we also present different submodules. The results of extensive experiments show that the proposed method surpasses state-of-the-art methods by 2.1% and 2.5% in Boundary-AP and by 3.5% and 2.8% in Orientation AP on the PIOD and BSDS datasets, respectively, indicating that the proposed method is more advanced than comparable methods. [ABSTRACT FROM AUTHOR]

RGB‐D salient object detection (SOD) is to detect salient objects from one RGB image and its depth data. Although related networks have achieved appreciable performance, they are not ideal for mobile devices since they are cumbersome and time‐consuming. The existing lightweight networks for RGB‐D SOD use depth information as additional input, and integrate depth information with colour image, which achieve impressive performance. However, the quality of depth information is uneven and the acquisition cost is high. To solve this issue, depth‐aware strategy is first combined to propose a lightweight SOD model, Depth‐Aware Lightweight network (DAL), using only RGB maps as input, which is applied to mobile devices. The DAL's framework is composed of multi‐level feature extraction branch, specially designed channel fusion module (CF) to perceive the depth information, and multi‐modal fusion module (MMF) to fuse the information of multi‐modal feature maps. The proposed DAL is evaluated on five datasets and it is compared with 14 models. Experimental results demonstrate that the proposed DAL outperforms the state‐of‐the‐art lightweight networks. The proposed DAL has only 5.6 M parameters and inference speed of 39 ms. Compared with the best‐performing lightweight method, the proposed DAL has fewer parameters, faster inference speed, and higher accuracy. [ABSTRACT FROM AUTHOR]

The filtering characteristics of the output filter directly determine the grid-connected performance and cost and volume of the grid-connected converter. Based on a deep analysis of harmonic attenuation characteristics of LCL-type and LLCL-type filters, a unified circuit model of the output filter was proposed in this paper. According to this model, the possible structures of filters below the fifth order are deduced, summarized, and compared, and two new filters, LLCLC_A type and LLCLC_B type, are proposed. A comparative study of these filters in terms of filter characteristics, impedance characteristics, parameter design, and resonant spike damping was carried out. The results show that the LLCLC_A-type filter has advantages over the other three filters. The LLCLC_A-type filter is named the LLCLC-type filter. Finally, both simulation and experimental results verify the correctness and effectiveness of the proposed LLCLC-type filter. The research in this paper is a guide to the design of a grid-connected converter output filter. [ABSTRACT FROM AUTHOR]

Since the number of features of the dataset is much higher than the number of patterns, the higher the dimension of the data, the greater the impact on the learning algorithm. Dimension disaster has become an important problem. Feature selection can effectively reduce the dimension of the dataset and improve the performance of the algorithm. Thus, in this paper, A feature selection algorithm based on P systems (P-FS) is proposed to exploit the parallel ability of cell-like P systems and the advantage of evolutionary algorithms in search space to select features and remove redundant information in the data. The proposed P-FS algorithm is tested on five UCI datasets and an edible oil dataset from practical applications. At the same time, the P-FS algorithm and genetic algorithm feature selection (GAFS) are compared and tested on six datasets. The experimental results show that the P-FS algorithm has good performance in classification accuracy, stability, and convergence. Thus, the P-FS algorithm is feasible in feature selection. [ABSTRACT FROM AUTHOR]

In this article, the problem of distributed fault detection and isolation for single fault is considered for multi-agent systems affected by disturbances and communication delays. A bank of unknown input observers based on local information are constructed for distributed fault detection and isolation, and the sufficient conditions are presented to guarantee that the fault-detection residuals generated by unknown input observers satisfy H _ / H ∞. The logic of distributed fault detection is as follows: a agent can be declared as faulty when it's fault residuals exceed the threshold, and the fault residuals of its adjacent nodes are less than the threshold, declaring the existence of the fault. [ABSTRACT FROM AUTHOR]

As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer was increased to improve the network for small target detection accuracy. A receptive field module was designed to replace the original spatial pyramid pooling (SPP) module so that the network can obtain feature information and improve network performance. Finally, experiments were carried out on the insulator image dataset. The experimental results show that the average accuracy of the algorithm is 97.4%, which is 7% higher than that of the original YOLOv5 network, and the detection speed is increased by 10 fps, which improves the accuracy and speed of insulator detection. [ABSTRACT FROM AUTHOR]

Agricultural equipment works poorly under low illumination such as nighttime, and there is more noise in soybean plant images collected under light constraints, and the reconstructed soybean plant model cannot fully and accurately represent its growth condition. In this paper, we propose a low-illumination soybean plant reconstruction and trait perception method. Our method is based on low-illumination enhancement, using the image enhancement algorithm EnlightenGAN to adjust soybean plant images in low-illumination environments to improve the performance of the scale-invariant feature transform (SIFT) algorithm for soybean plant feature detection and matching and using the motion recovery structure (SFM) algorithm to generate the sparse point cloud of soybean plants, and the point cloud of the soybean plants is densified by the face slice-based multi-view stereo (PMVS) algorithm. We demonstrate that the reconstructed soybean plants are close to the growth conditions of real soybean plants by image enhancement in challenging low-illumination environments, expanding the application of three-dimensional reconstruction techniques for soybean plant trait perception, and our approach is aimed toward achieving the accurate perception of current crop growth conditions by agricultural equipment under low illumination. [ABSTRACT FROM AUTHOR]

Insulator devices are important for transmission lines, and defects such as insulator bursting and string loss affect the safety of transmission lines. In this study, we aim to investigate the problems of slow detection speed and low efficiency of traditional insulator defect detection algorithms, and to improve the accuracy of insulator fault identification and the convenience of daily work; therefore, we propose an insulator defect detection algorithm based on an improved MobilenetV1-YOLOv4. First, the backbone feature extraction network of YOLOv4 'Backbone' is replaced with the lightweight module Mobilenet-V1. Second, the scSE attention mechanism is introduced in stages of preliminary feature extraction and enhanced feature extraction, sequentially. Finally, the depthwise separable convolution substitutes the 3 × 3 convolution of the enhanced feature extraction network to reduce the overall number of network parameters. The experimental results show that the weight of the improved algorithm is 57.9 MB, which is 62.6% less than that obtained by the MobilenetV1-YOLOv4 model; the average accuracy of insulator defect detection is improved by 0.26% and reaches 98.81%; and the detection speed reaches 190 frames per second with an increase of 37 frames per second. [ABSTRACT FROM AUTHOR]

Capsule network is a new network structure which can encode the properties and spatial relations of image features. It overcomes the shortcomings of CNN that requiring large number of training samples and parameters, and information loss during the pooling process. Capsule network can only extract shallow features, which makes it perform poorly on complex datasets. In this paper, a new residual capsule network model (Res-CapsNet) is proposed by fusing capsule network, residual network and deconvolution. The Res-CapsNet extracts deep features and sends them to capsule module using the dense connections of residual, which effectively strengthens the feature transfer and feature utilization. Capsule module converts scalar neurons to vector neurons through the main capsule layer, and uses dynamic routing algorithm to selectively activate the high-level capsule in the main capsule layer and the digital capsule layer, and obtains the recognition results. Deconvolution reconstruction module is the last part of Res-CapsNet, responsible for reconstructing recognition results by using 4-layer deconvolution. Res-CapsNet utilizes beta-mish activation function to reduce the "death" of neurons caused by ReLU, thus activating more neurons to further improve the classification accuracy. The experimental results show that Res-CapsNet has better performance on datasets, such as SVHN, FASH-MNIST and CIFAR-10. Compared with the baseline model CapsNet, the model parameters of Res-CapsNet on dataset CIFAR-10 are reduced by 65.73%, while the classification accuracy is significantly improved by 33.66%. [ABSTRACT FROM AUTHOR]

The nondestructive testing of agricultural products has always been a key technology for the modernization of agriculture and food. By applying a sinusoidal voltage (current) excitation signal of variable frequency, the relationship between the amplitude, frequency and phase of the response signal is obtained, and the measured response function in a certain frequency range is obtained, constructing the correlation between impedance spectroscopy and matter properties. Electrical impedance spectroscopy (EIS) is a widely used method for the nondestructive characterization of agricultural products, and its applications in the agricultural field has attracted increasing attention. This paper summarizes the research of electrical impedance spectroscopy (EIS) in the detection of grain quality, fruit and vegetable quality, meat quality and food quality from 2005 to 2022. The potential and development direction of electrical impedance spectroscopy in the nondestructive testing of agricultural product quality are prospected, which provides a reference for scientific researchers who applied electrical impedance spectroscopy in agricultural product quality detection. [ABSTRACT FROM AUTHOR]

Aiming to investigate the disadvantage of the optimization algorithm of membrane computing (a P system) in which it is difficult to take advantage of parallelism in MATLAB, leading to a slow optimization speed, a digital-specific hardware solution (field-programmable gate array, FPGA) is proposed to design and implement the single-cell-membrane algorithm (SCA). Because the SCA achieves extensive global searches by the symmetric processing of the solution set, with independent and symmetrically distributed submembrane structures, the FPGA-hardware-based design of the SCA system includes a control module, an HSP module, an initial value module, a fitness module, a random number module, and multiple submembrane modules with symmetrical structures. This research utilizes the inherent parallel characteristics of the FPGA to achieve parallel computations of multiple submembrane modules with a symmetric structure inside the SCA, and it achieves a high degree of parallelism of rules inside the modules by using a non-blocking allocation. This study uses the benchmark Sphere function to verify the performance of the FPGA-designed SCA system. The experimental results show that, when the FPGA platform and the MATLAB platform obtain a similar calculation accuracy, the average time-consuming of the FPGA is 0.00041 s, and the average time-consuming of MATLAB is 0.0122 s, and the calculation speed is improved by nearly 40 times. This study uses the FPGA design to implement the SCA, and it verifies the advantages of the membrane-computing maximum-parallelism theory and distributed structures in computing speed. The realization platform of membrane computing is expanded, which provides a theoretical basis for further development of the distributed computing model of population cells. [ABSTRACT FROM AUTHOR]

In this article, a voltage-distortion-based method is presented for robust detection and location of interturn fault (ITF) in permanent magnet synchronous machine (PMSM). The mathematical models of PMSM under healthy and fault conditions are presented. According to the mathematical model, the dq-axis voltage distortions owing to ITF are estimated through Luenberger observer. On the basis of the voltage distortions, a fault characteristic signal (FCS) is constructed for ITF diagnosis. Through analyzing FCS, the second harmonic component in FCS is chosen to construct the fault indicators for fault diagnosis. The simulation and experimental results show that the ITF diagnosis is effectively realized through the presented method. Additionally, the presented method requires no additional hardware and has good robustness against parameter mismatch and rotating speed variation. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) have been gradually used as the driving equipment of coal mine belt conveyors. To ensure safety and stability, it is necessary to carry out real-time and accurate fault diagnosis of PMSM. Therefore, a fault diagnosis method for PMSM based on digital twin and ISSA-RF (Improved Sparrow Search Algorithm Optimized Random Forest) is proposed. Firstly, the multi-strategy hybrid ISSA is used to solve the problems of uneven population distribution, insufficient population diversity, low convergence speed, etc. In addition, the fault diagnosis model of ISSA-RF permanent magnet synchronous motor is constructed based on the optimization of the number of Random Forest decision trees and that of features of each node by ISSA. Secondly, considering the operation mechanism and physical properties of PMSM, the relevant digital twin model is constructed and the real-time mapping of physical entity and virtual model is realized through data interactive transmission. Finally, the simulation and experimental results show that the fault diagnosis accuracy of ISSA-RF, 98.2%, is higher than those of Random Forest (RF), Sparrow Search Algorithm Optimized Random Forest (SSA-RF), BP neural network (BP) and Support Vector Machine (SVM), which verifies the feasibility and ability of the proposed method to realize fault diagnosis and 3D visual monitoring of PMSM together with the digital twin model. [ABSTRACT FROM AUTHOR]

Spatiotemporal modal analysis and prediction of tropospheric atmospheric CO concentration in the world and East Asia from 2002 to 2021 using the inversion data of airs sounder. The results show that: The CO concentration in the northern hemisphere is higher than that in the southern hemisphere; from the upper troposphere to the lower troposphere, the CO concentration changes from " ∧ " to "√"; the fluctuation range of near surface CO concentration in the northern hemisphere is relatively intense, and the fluctuation range in the southern hemisphere is relatively small. Using MK, Sen slope estimation, and EOF analysis, it is found that CO concentration in the convective middle layer tends to decline in more than 90% of the global area, and the decline rate in the northern hemisphere is significantly higher than that in the southern hemisphere. In East Asia, the CO concentration in the lower tropospheric marine area is significantly lower than that in the land area. The average concentration and decline rate of CO in East Asia is always higher than that in the world; the CO concentration in East Asia is the highest in spring and winter in the lower troposphere; and the CO concentration in East Asia is lower in the northeast and higher in the southeast in the upper troposphere in spring, autumn, and winter, and higher in the northeast and Central Plains in summer. Compared with the three-exponential smoothing model, the prediction error of the VMD-LSTM hybrid model for atmospheric CO concentration is significantly reduced, which indicates that the improved neural network prediction model has higher prediction accuracy. The factors affecting the change of tropospheric CO concentration are not only affected by the ground factors, but also related to indirect factors such as water vapor, methane, and atmospheric temperature in the atmosphere. [ABSTRACT FROM AUTHOR]

To improve the positioning accuracy and reliability of autonomous navigation agricultural machinery and reduce the cost of high-precision positioning, an integrated navigation system based on Real-Time Dynamic Kinematic BeiDou Navigation Satellite System (RTK-BDS) and Inertial Navigation System (INS) is designed in this study. On the one hand, an autonomous navigation control board is designed and made in the system, which integrates BDS high-precision analysis module, Inertial Measurement Unit (IMU) module, and radio module, and realizes the integrated navigation algorithm on the control board. On the other hand, low-cost RTK technology is realized by building differential reference stations and vehicle-mounted mobile stations. Experiments are carried out on actual farm machinery under different road conditions including open road, signal-shielded road, and urban congested road. According to the angular velocity and acceleration information from INS and the position and velocity information from the BDS high-precision analysis module, the system uses Kalman filter algorithm for data fusion to calculate the precise position, velocity, and attitude information of agricultural machinery in real time. The experimental results show that the position error of the integrated navigation system on the open road is within 3 cm, the azimuth error is within 0.6°, and the inclination error is within 1°, all of which converge rapidly when encountering bad road conditions. It can be known from the experimental results that the RTK-BDS/INS integrated navigation system has high positioning accuracy, strong adaptive anti-interference ability, and low implementation cost of RTK technology, which provides a reliable way for automatic navigation control of agricultural machinery. [ABSTRACT FROM AUTHOR]

This study addresses the output impedance model of the LCL-type grid-connected converter considering the dead-time effects and the digital control delay. The model shows that the digital control delay will affect the accuracy of the output impedance of the grid-connected converter, and the dead-time effects are only equivalent to superimposing a disturbance voltage on the original output impedance model. The derived output impedance model is verified by comparing it with the switching model in the PSIM simulation environment. The harmonic interaction between converter cluster and utility grid is modeled and analyzed based on the output impedance model. A combined harmonic suppression strategy is incorporated into every converter control scheme to suppress the harmonic interaction. Simulation results are presented to demonstrate the correctness of harmonic interaction analysis and the effectiveness of the proposed suppression strategy. [ABSTRACT FROM AUTHOR]

For the trajectory planning problem under the nonlinear and strongly coupled characteristics of unmanned helicopters, membrane computing with distributed parallel processing capability is introduced for unmanned helicopter trajectory planning. The global and local spatial information is temporally characterized; the temporal characterization algorithm under mapping information is designed; the hierarchical discriminant regression algorithm is designed based on incremental principal component analysis to realize the process of building and identifying trees in trajectory planning; and the pulsed neural membrane system (PNMS) with spatio-temporal coding function under membrane computing is constructed. Compared with the RRT algorithm in two experimental environments, the original path length, the trimmed path length, the time used to plan the trajectory, and the number of search nodes have different levels of improvement; the feasibility and effectiveness of the PNMS in unmanned helicopter trajectory planning are verified. It expands the theoretical research of membrane computing in the field of optimal control and provides theoretical support for the subsequent application practice. [ABSTRACT FROM AUTHOR]

For the trajectory planning problem under the nonlinear and strongly coupled characteristics of unmanned helicopters, membrane computing with distributed parallel processing capability is introduced for unmanned helicopter trajectory planning. The global and local spatial information is temporally characterized; the temporal characterization algorithm under mapping information is designed; the hierarchical discriminant regression algorithm is designed based on incremental principal component analysis to realize the process of building and identifying trees in trajectory planning; and the pulsed neural membrane system (PNMS) with spatio-temporal coding function under membrane computing is constructed. Compared with the RRT algorithm in two experimental environments, the original path length, the trimmed path length, the time used to plan the trajectory, and the number of search nodes have different levels of improvement; the feasibility and effectiveness of the PNMS in unmanned helicopter trajectory planning are verified. It expands the theoretical research of membrane computing in the field of optimal control and provides theoretical support for the subsequent application practice. [ABSTRACT FROM AUTHOR]

The pooling layer has achieved good results in reducing the feature dimension and parameters of convolution neural network (CNN), but it will cause different degrees of information loss. In order to retain as much feature information as possible, we design a pooling method based on Principal Component Analysis (PCA)-PCAPool. Firstly, all feature maps are traversed with the pooling window in which the data is extracted and stretched into row vectors. With the sliding of the pooling window, all row vectors are arranged in the matrix to form the sample matrix. Then all eigenvectors are extracted from the sample matrix by PCA algorithm to form the eigenvector matrix, which right multiplies the sample matrix to get the principal component matrix. Thirdly, each column of the principal component matrix is weighted with information coefficient which is determined by training to get the pooling vector. Finally, PCAPool result is obtained by blocks arrangement of pooling vector. PCAPool is tested with CNN-Quick, NIN, WRN-SAM, GP-CNN on datasets MNIST, CIFAR10/100 and SVHN. We also used AlexNet on Imagenet2012 to test PCAPool. The experiment results show that compared with traditional pooling methods, PCAPool could retain information in the pooling window better and improve the image classification accuracy. [ABSTRACT FROM AUTHOR]

This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms. [ABSTRACT FROM AUTHOR]

Patrol unmanned aerial vehicles (UAVs) in coal mines have high requirements for environmental perception. Because there are no GPS signals in a mine, it is necessary to use simultaneous localization and mapping (SLAM) to realize environmental perception for UAVs. Combined with complex coal mine environments, an integrated navigation algorithm for unmanned helicopter inertial measurement units (IMUs), light detection and ranging (LiDAR) systems, and depth cameras based on probabilistic membrane computing-based SLAM (PMC-SLAM) is proposed. First, based on an analysis of the working principle of each sensor, the mathematical models for the corresponding sensors are given. Second, an algorithm is designed for the membrane, and a probabilistic membrane system is constructed. The probabilistic SLAM map is constructed by sparse filtering. The experimental results show that PMC can further improve the accuracy of map construction. While adapting to the trend of intelligent precision mining in coal mines, this approach provides theoretical support and application practice for coal mine disaster prevention and control. [ABSTRACT FROM AUTHOR]

This article proposes an integration method for interturn fault diagnosis and torque ripple minimization control of direct-torque-controlled surface-mounted permanent magnet synchronous machine (SPMSM) drive system without requiring the specific fault severity (short-circuited turn ratio, current). In this method, the interturn fault is detected and located based on the amplitude and initial phase angle of the zero-sequence voltage component (ZSVC). Both the torque injection and the improved flux observer are applied to reduce the torque ripple caused by the interturn fault. Furthermore, the torque injection and the improved flux observer are directly calculated by using the already obtained amplitude and initial phase angle of the ZSVC during the fault diagnosis process, without requiring the specific values of short-circuited turn ratio and fault current. The experimental results validate the effectiveness of the proposed integration method for the direct-torque-controlled SPMSM drive system. [ABSTRACT FROM AUTHOR]

Open-phase fault (OPF) in the permanent magnet synchronous machine (PMSM) drive system is one of the most prevalent failures due to the resulting large electromagnetic torque ripples and serious mechanical vibration. To improve the reliability of the PMSM drive system, several OPF diagnosis methods were studied and were mainly focused on vector control system. Recently, model predictive control (MPC) has been emerging as a popular control method for the PMSM drive system owing to its simple structure and excellent dynamic performance. However, so far, there are no special OPF diagnosis methods for the model predictive controlled PMSM drive system. Hence, this article first proposes a cost function-based OPF diagnosis method for the model predictive controlled PMSM drive system, considering the characteristics of MPC system. The dc component and second harmonic component in the cost function are used for the OPF diagnosis, and the initial phase angle differences are defined to locate the faulty phase. The experimental results show that, by the proposed fault diagnosis method, not only the OPF can be rapidly detected, but also the faulty phase can be effectively located. Moreover, the proposed method can be implemented simply and is robust against the operating point and parameter variations of the PMSM drive system. This article proposes an OPF diagnosis method for the model predictive controlled PMSM drive system based on cost function, taking into account the characteristics of the MPCC system. The experimental results verify the effectiveness of the proposed method. From the theoretical analysis and experimental results, the following works and conclusion are drawn. [ABSTRACT FROM AUTHOR]

This article proposes an equivalent conversion-based improved loss minimization control method for interior permanent magnet synchronous motor (IPMSM). In the proposed control method, both the copper loss and iron loss are studied and the efficiency optimization problem due to the unequal inductance parameters of IPMSM is considered and solved, thus achieving the optimal efficiency. First, according to the mathematical model of the IPMSM considering iron loss resistance, the equivalent conversion method is presented to derive the relationship between electromagnetic torque, d-axis and q-axis currents. Then, by the polynomial fitting method, the d-axis and q-axis currents which minimize the electrical loss (copper loss and iron loss) are calculated. Additionally, to further show the performance of the proposed control method, the maximum torque per ampere control method and the approximate processing control method are studied for the comparative analysis. Both the simulation and experimental results show that the proposed control method can comprehensively reduce the electrical loss of the IPMSM, showing the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This article proposes a model-based strategy with robust parameter mismatch for online high resistance connection (HRC) diagnosis and localization in surface-mounted permanent magnet synchronous machine (PMSM) drive system. The mathematical mode of the PMSM with the HRC is established in the abc stationary frame and dq rotating frame. Based on the model, the voltage distortion estimator is applied to estimate the voltage distortions caused by the HRC. According to the voltage distortions, a new fault characteristic signal is defined and is analyzed under different fault conditions. Based on the defined signal, the fault indicators being insensitive to the parameter mismatch are defined to diagnose the fault and locate the faulty phases. By the proposed method, not only the HRC can be detected, but also the fault severity can be assessed and the faulty phases can be located, where no additional equipment or measurement devices are needed. Both the simulation and experimental results verify that the proposed method can effectively achieve online HRC diagnosis and location in the PMSM drive system. [ABSTRACT FROM AUTHOR]

To protect edge and texture information, when removing salt‐and‐pepper (SP) noise in grayscale images, a support vector machine (SVM) denoising method is employed. First, a mapping relation between the neighborhood signal pixels and the central pixel is designed. The size of the neighborhood is a 5 × 5 region, with a signal pixel in the center. In this region, a 25‐dimensional input sample is constructed using the correlation between the neighborhood pixels and the eight‐direction fractional integral operators. The center signal pixel acts as the corresponding output sample to provide a training sample. Then, the SVM is trained with all training samples, and the SVM denoising model is obtained. Next, the center pixel value is estimated using the SVM denoising model in every 5 × 5 region with a noise pixel in the center. Finally, the noise pixel values are replaced with the estimated values of the SVM. The experiments demonstrate that the best denoising effect is obtained when the fractional integral order is in the range of 1.8 ± 0.1. The proposed method produces a visually pleasing denoised image and obtains superior image quality assessment indicators. Our method has significant advantages compared with state‐of‐the‐art denoisers when a low level of noise is present. [ABSTRACT FROM AUTHOR]

Image denoising based on a convolution neural network (CNN) can be described as the problem of learning a mapping function from a noisy image to a clean image through an end-to-end training. We propose a multiscale parallel feature extraction module (MPFE) for CNN denoising, which integrates residual learning and dense connection. The MPFE uses convolution kernels of different sizes to adaptively extract multiple features in different scales from the input image. We introduce dense connection to connect each MPFE, which can make different features interact with each other and concatenate together, so as to fully exploit the image features. The dense connection can pass the features that carry many image details, which help reduce image distortion. Meanwhile, it can also reduce gradient disappearance and improve convergence speed. The MPFE uses residual learning to resolve the gradient loss caused by high network depth while still ensuring that the network learns the details of the noisy image. Simulation experiments show that our denoising method has the ability of suppressing Gaussian noises with different noise levels, it performs superior performance over the state-of-the-art denoising methods. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.6.063031] [ABSTRACT FROM AUTHOR]

The energy supply for sensors in wireless sensor and actuator networks (WSANs) is usually limited, we therefore focus on how to design the adjacency weights for achieving second-order consensus with less communication energy consumption in a distributed way, and investigate this issue from both the viewpoints of the rate of convergence and the sparsity of the network. We also derived the conditions on the consensus control parameter and the updating period for the proposed consensus protocol to ensure the convergence. It is shown that, by removing some properly chosen links, the sparsity of the resulting network can produce a positive effect on the energy efficiency of WSANs. Finally, simulation results are presented to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The spiking neural P systems (SN P systems, for short) refer to the parallel-distributed biocomputing models, which have currently become research hotspots in the biocomputing field. In computing systems, logical operations and arithmetic operations are the most important parts, while the decoders composed of logical circuits are their indispensable parts. In this paper, considering the characteristics of SN P systems, a computing model for the general single-input single-output n-2^n decoder is proposed. The decoding results of the $n$ -bit input binary sequence can be derived on the MeCoSim platform. The computing steps of a 3-8 decoder and its simulations on the MeCoSim platform are also described in detail. Simulation results show the effectiveness of the proposed computing model for decoders. [ABSTRACT FROM PUBLISHER]