Proposed Method Research Articles

Analysis of Rectangular Plate with Opening by Finite Difference Method

Steel plates are commonly used to support lateral or vertical loads. Before the design of such a plate, analysis is performed to check the stability of plate for the proposed load. There are several methods for this analysis. In this research, a finite difference method (FDM) is proposed to analyze a rectangular steel plate. The plate is considered to be subjected to an arbitrary transverse uniformly distributed loading and is considered to be clamped at the two opposite edges and free at the other two edges. At first the plate is analyzed considering it to be completely solid and then it was re-analyzed considering a rectangular opening at its centre. The ordinary finite difference method is used to solve the governing differential equation of the plate deflection. The proposed method can be easily programmed to readily apply on a plate problem. The work covers the determination of displacement components at different points of the plate and checking the result by software (STAAD.Pro) analysis.

Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression Technique

The Internet of Things (IoT) has made it possible to include everyday objects in a connected network, allowing them to intelligently process data and respond to their environment. Thus, it is expected that those objects will gain an intelligent understanding of their environment and be able to process data more efficiently than before. Particularly, such edge computing paradigm has allowed the execution of inference methods on resource-constrained devices such as microcontrollers, significantly changing the way IoT applications have evolved in recent years. However, although this scenario has supported the development of Tiny Machine Learning (TinyML) approaches on such devices, there are still some challenges that require further investigation when optimizing data streaming on the edge. Therefore, this article proposes a new unsupervised TinyML regression technique based on the typicality and eccentricity of the samples to be processed. Moreover, the proposed technique also exploits a Recursive Least Squares (RLS) filter approach. Combining all these features, the proposed method uses similarities between samples to identify patterns when processing data streams, predicting outcomes based on these patterns. The results obtained through the extensive experimentation utilizing vehicular data streams were highly encouraging. The proposed algorithm was meticulously compared with the RLS algorithm and Convolutional Neural Networks (CNN). It exhibited significantly superior performance, with mean squared errors that were 4.68 and 12.02 times lower, respectively, compared to the aforementioned techniques.

End-to-End Target Liveness Detection via mmWave Radar and Vision Fusion for Autonomous Vehicles

The successful operation of autonomous vehicles hinges on their ability to accurately identify objects in their vicinity, particularly living targets such as bikers and pedestrians. However, visual interference inherent in real-world environments, such as omnipresent billboards, poses substantial challenges to extant vision-based detection technologies. These visual interference exhibit similar visual attributes to living targets, leading to erroneous identification. We address this problem by harnessing the capabilities of mmWave radar, a vital sensor in autonomous vehicles, in combination with vision technology, thereby contributing a unique solution for liveness target detection. We propose a methodology that extracts features from the mmWave radar signal to achieve end-to-end liveness target detection by integrating the mmWave radar and vision technology. This proposed methodology is implemented and evaluated on the commodity mmWave radar IWR6843ISK-ODS and vision sensor Logitech camera. Our extensive evaluation reveals that the proposed method accomplishes liveness target detection with a mean average precision of 98.1%, surpassing the performance of existing studies.

Automatically Temporal Labeled Data Generation Using Positional Lexicon Expansion for Focus Time Estimation of News Articles

Many facts change over time, which is a fundamental aspect of our physical environment. In the case of pandemic articles, the user is not interested in the creation date of the document but in the facts and the cause of the last pandemic. Fake news can be better combated by having a document with a temporal focus. Currently, neither the sequence of events nor the temporal focus is considered when obtaining news documents. Despite the limited number of temporal aspects in the available datasets, it is difficult to test and evaluate the temporal conclusions of the model. The goal of this work is to develop a temporal focus news article retrieval model based on co-training to advance research in semi-supervised learning. A mapping of the dataset is performed using (1) the evolving focus time of news articles and (2) the semi-supervised method based on coincidence contexts for learning low-dimensional continuous vectors for learning neural contrast embedding models generating focus time-based query in sequential news articles to facilitate temporal understanding by learning low-dimensional continuous vectors. A diverse dataset of news articles is used to evaluate the effectiveness of the proposed method. With semi-supervised learning and lexicon expansion, the result of the developed model can achieve 89%. The method performed better than previous baselines and traditional machine learning models with improvements of 12.65% and 4.7%, respectively.

IoT device type identification using training deep quantum neural networks optimized with a chimp optimization algorithm for enhancing IoT security

IoT networks can be defined as groups of physically connected things and devices that can connect to the Internet and exchange data with one another. Since enabling an increasing number of internets of things devices to connect with their networks, organizations have become more vulnerable to safety issues and attacks. A major drawback of previous research is that it can find out prior seen types only, also any new device types are considered anomalous. In this manuscript, IoT device type detection utilizing Training deep quantum neural networks optimized with a Chimp optimization algorithm for enhancing IOT security (IOT-DTI-TDQNN-COA-ES) is proposed. The proposed method entails three phases namely data collection, feature extraction and detection. For Data collection phase, real network traffic dataset from different IoT device types are collected. For feature mining phase, the internet traffic features are extracted through automated building extraction (ABE) method. IoT device type identification phase, Training deep quantum neural networks (TDQNN) optimized with Chimp optimization algorithm (COA) is utilized to detect the category of IoT devices as known and unknown device. IoT network is implemented in Python. Then the simulation performance of the proposed IOT-DTI-TDQNN-COA-ES method attains higher accuracy as26.82% and 23.48% respectively, when compared with the existing methods.

Multichannel deconvolution with a high-frequency structural regularization

The resolution of seismic data determines the ability to characterize stratigraphic features from observed seismic records. Sparse spike inversion, as an important processing method, can effectively improve the band-limited property of the seismic data. However, this approach ignores the spatial information along seismic traces, which causes the unreliability of the reconstructed high-resolution data. In this paper, we develop a high-frequency structure-constrained multichannel deconvolution (HFSC-MD) to alleviate this issue. This method allows the cost function to incorporate high-frequency spatial information in the form of a prediction-error filter (PEF) to regularize the components of the result beyond the original frequency. The PEF, also called a high-frequency reflection structural characterization operator, is estimated from the mapping relationship of low- and high-frequency components. We adopt the alternating direction method of multipliers to solve the cost function in HFSC-MD. Synthetic and field data demonstrate that our method recovers more reliable high-resolution data and enriches reflective structures.

Weight Decay With Tailored Adam on Scale-Invariant Weights for Better Generalization.

Weight decay (WD) is a fundamental and practical regularization technique in improving generalization of current deep learning models. However, it is observed that the WD does not work effectively for an adaptive optimization algorithm (such as Adam), as it works for SGD. Specifically, the solution found by Adam with the WD often generalizes unsatisfactorily. Though efforts have been made to mitigate this issue, the reason for such deficiency is still vague. In this article, we first show that when using the Adam optimizer, the weight norm increases very fast along with the training procedure, which is in contrast to SGD where the weight norm increases relatively slower and tends to converge. The fast increase of weight norm is adverse to WD; in consequence, the Adam optimizer will lose efficacy in finding solution that generalizes well. To resolve this problem, we propose to tailor Adam by introducing a regularization term on the adaptive learning rate, such that it is friendly to WD. Meanwhile, we introduce first moment on the WD to further enhance the regularization effect. We show that the proposed method is able to find solution with small norm and generalizes better than SGD. We test the proposed method on general image classification and fine-grained image classification tasks with different networks. Experimental results on all these cases substantiate the effectiveness of the proposed method in help improving the generalization. Specifically, the proposed method improves the test accuracy of Adam by a large margin and even improves the performance of SGD by 0.84% on CIFAR 10 and 1.03 % on CIFAR 100 with ResNet-50. The code of this article is public available at xxx.

Memory Shapelet Learning for Early Classification of Streaming Time Series.

Early classification predicts the class of the incoming sequences before it is completely observed. How to quickly classify streaming time series without losing interpretability through early classification method is a challenging problem. A novel memory shapelet learning framework for early classification is proposed in this article. First, a memory distance matrix is introduced to store the historical characteristics of streaming time series, which can alleviate repetitive calculations caused by the growing length of time series. Second, early interpretable shapelets are extracted in the proposed method by optimizing both accuracy objective and earliness objective simultaneously. The proposed method employs end-to-end learning, which allows the model to directly learn early shapelets without the necessity of searching for numerous candidate shapelets. Third, an objective function of memory shapelet learning is proposed by overall considering accuracy and earliness, which can be optimized by gradient descent algorithm. Finally, experiments are conducted on benchmark dataset UCR, Tennessee Eastman process, and real-world aluminum electrolysis process in China. Comparable results with other state-of-the-art methods demonstrate the superior performance of the proposed method in interpretability, accuracy, earliness, and time complexity.

Online Muscle Activation Onset Detection Using Likelihood of Conditional Heteroskedasticity of Electromyography Signals.

The real-time, accurate detection of muscle activation onset (MAO) is significant for EMG-triggered control strategies in embedded applications like prostheses and exoskeletons. This paper investigates sEMG signals using the generalized autoregressive conditional heteroskedasticity (GARCH) model, focusing on variance. A novel feature, the likelihood of conditional heteroskedasticity (LCH) extracted from the maximum likelihood estimation of GARCH parameters, is proposed. This feature effectively distinguishes signal from noise based on heteroskedasticity, allowing for the detection of MAO through the LCH feature and a basic threshold classifier. For online calculation, the model parameter estimation is simplified, enabling direct calculation of the LCH value using fixed parameters. The proposed method was validated on two open-source datasets and demonstrated superior performance over existing methods. The mean absolute error of onset detection, compared with visual detection results, is approximately 65 ms under online conditions, showcasing high accuracy, universality, and noise insensitivity. The results indicate that the proposed method using the LCH feature from the GARCH model is highly effective for real-time detection of muscle activation onset in sEMG signals. This novel approach shows great potential and possibility for real-world applications, reflecting its superior performance in accuracy, universality, and insensitivity to noise.

Fast Attack Detection for Cyber-Physical Systems Using Dynamic Data Encryption.

To defend the cyber-physical system (CPSs) from cyber-attacks, this work proposes an unified intrusion detection mechanism which is capable to fast hunt various types of attacks. Focusing on securing the data transmission, a novel dynamic data encryption scheme is developed and historical system data is used to dynamically update a secret key involved in the encryption. The core idea of the dynamic data encryption scheme is to establish a dynamic relationship between original data, secret key, ciphertext and its decrypted value, and in particular, this dynamic relationship will be destroyed once an attack occurs, which can be used to detect attacks. Then, based on dynamic data encryption, a unified fast attack detection method is proposed to detect different attacks, including replay, false data injection (FDI), zero-dynamics, and setpoint attacks. Extensive comparison studies are conducted by using the power system and flight control system. It is verified that the proposed method can immediately trigger the alarm as soon as attacks are launched while the conventional χ2 detection could only capture the attacks after the estimation residual goes over the predetermined threshold. Furthermore, the proposed method does not degrade the system performance. Last but not the least, the proposed dynamic encryption scheme turns to normal operation mode as the attacks stop.

Local Means Binary Networks for Image Super-Resolution.

The success of modern single image super-resolution (SISR) algorithms is inspired by the development of deep convolutional neural networks (CNNs). However, these CNN-based methods require considerable computation and complexity, making it impossible for these methods to perform real-time calculations in edge devices. Thus, lightweight model design has become a development trend in the super-resolution field, including pruning, quantization, and other methods. The 1-bit quantization is an extreme lightweight method which can reduce the calculation amount of the model in an extreme manner and is friendly to hardware such as edge devices. Most existing binary quantization approaches lead to a large information loss during forward propagation, especially in detailed color information (e.g., edge, texture, and contrast). The loss of color information makes modern binary methods unsuitable for SISR tasks. We think the loss occurs because these methods typically utilize a uniform threshold to quantize the weights and activations. Thus, in this article, we thoroughly analyze the difference between normal classification tasks and SISR tasks, and present a binarization scheme based on local means. The proposed method can maintain more detailed information in feature maps using dynamic thresholds during quantization. Specifically, each value in the full precision activations has a corresponding threshold during the quantization process, and those thresholds are determined by the full precision values of the surroundings. In addition, a gradient approximator is introduced to adaptively optimize the gradient for updating binary weights. We then verify the effectiveness of our method for training binary networks on several SISR benchmarks including VDSR and SRResNet. Experimental results show that the proposed method can outperform the state-of-the-art algorithms to obtain binary networks for image super-resolution with better peak signal-to-noise ratio (PSNR) values and visual quality.

JITL-MBN: A Real-Time Causality Representation Learning for Sensor Fault Diagnosis of Traction Drive System in High-Speed Trains.

A traction drive system (TDS) in high-speed trains is composed of various modules including rectifier, intermediate dc link, inverter, and others; the sensor fault of one module will lead to abnormal measurement of sensor in other modules. At the same time, the fault diagnosis methods based on single-operating condition are unsuitable to the TDS under multi-operating conditions, because a fault appears various in different conditions. To this end, a real-time causality representation learning based on just-in-time learning (JITL) and modular Bayesian network (MBN) is proposed to diagnose its sensor faults. In specific, the proposed method tracks the change of operating conditions and learns potential features in real time by JITL. Then, the MBN learns causality representation between faults and features to diagnose sensor faults. Due to the reduction of the nodes number, the MBN alleviates the problem of slow real-time modeling speed. To verity the effectiveness of the proposed method, experiments are carried out. The results show that the proposed method has the best performance than several traditional methods in the term of fault diagnosis accuracy.

Representation-Learning-Based CNN for Intelligent Attack Localization and Recovery of Cyber-Physical Power Systems.

Enabled by the advances in communication networks, computational units, and control systems, cyber-physical power systems (CPPSs) are anticipated to be complex and smart systems in which a large amount of data are generated, exchanged, and processed for various purposes. Due to these strong interactions, CPPSs will introduce new security vulnerabilities. To ensure secure operation and control of CPPSs, it is essential to detect the locations of the attacked measurements and remove the state bias caused by malicious cyber-attacks such as false data inject attack, jamming attack, denial of service attack, or hybrid attack. Accordingly, this article makes the first contribution concerning the representation-learning-based convolutional neural network (RL-CNN) for intelligent attack localization and system recovery of CPPSs. In the proposed method, the cyber-attacks' locational detection problem is formulated as a multilabel classification problem for CPPSs. An RL-CNN is originally adopted as the multilabel classifier to explore and exploit the implicit information of measurements. By comparing with previous multilabel classifiers, the RL-CNN improves the performance of attack localization for complex CPPSs. Then, to automatically filter out the cyber-attacks for system recovery, a mean-squared estimator is used to handle the difficulty in state estimation with the removal of contaminated measurements. In this scheme, prior knowledge of the system state is obtained based on the outputs of the stochastic power flow or historical measurements. The extensive simulation results in three IEEE bus systems show that the proposed method is able to provide high accuracy for attack localization and perform automatic attack filtering for system recovery under various cyber-attacks.

An Estimation Method of High-Order LC Circuits in Power Electronic Converters

This paper proposes a parameter estimation method for high-order LC circuits conducted during the power-OFF of power electronic converters. It is based on a battery-powered compact signal injection unit and a circuit modeling method to estimate the LC circuit parameters. The proposed method aims for LC component condition monitoring in applications with routine-based maintenance or frequent ON/OFF operation (e.g., wind power, photovoltaic, traction systems). Compared to LCR meters and impedance analyzers, the proposed method has a significantly lower implementation cost and does not require disassembly of components. Moreover, it is a converter-level method in which the signal injection unit only needs to connect the converter inputs or outputs. Compared to power-ON LC parameter estimation methods, the proposed one is non-invasive without adding any hardware or software algorithm that may introduce new risks to converter operation. This method can handle the converters with high-order LC structures (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{nd}$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4^{th}$</tex-math></inline-formula> -order) in converter power-OFF state, which has not been achieved by existing power-OFF solutions. Therefore, the proposed method is promising for the specific applications of interest mentioned above. The parameter estimation algorithm and signal injection unit design are presented. The experimental results indicate that the estimation errors of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{nd}$</tex-math></inline-formula> -order equivalent circuit, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3^{rd}$</tex-math></inline-formula> -order equivalent circuit, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4^{th}$</tex-math></inline-formula> -order equivalent circuit are lower than 1.6%, 2.5%, and 3%, respectively. The estimated LC parameters can be further used as inputs for operation optimization or condition monitoring of the converters.

Deep Learning Models and Fusion Classification Technique for Accurate Diagnosis of Retinopathy of Prematurity in Preterm Newborn

اعتلال الشبكية الخداجي (ROP) هو السبب الأكثر شيوعًا لعمى الأطفال الذي لا رجعة فيه، ويعتمد تشخيصه وعلاجه على الدرجات الذاتية بناءً على سمات الأوعية الدموية في شبكية العين. ومع ذلك، فإن هذه الطريقة شاقة وعرضة للخطأ، لذا فإن الأساليب الآلية مرغوبة لمزيد من الدقة والإنتاجية. تهدف هذه الدراسة إلى تطوير نهج قائم على التعلم العميق للتشخيص الدقيق لمرض اعتلال الشبكية الخداجي الزائد عند الأطفال الخدج باستخدام نماذج التعلم التحويلية وتقنية تصنيف الاندماج. زودنا مركز الأمل للعيون في العيادة الخاصة في بغداد، العراق، بـ 2776 صورة لقاعدة فحص مرضى اعتلال الشبكية الخداجي بين عامي 2015 و2020، واستخدمنا هذه الصور لتدريب ثلاثة نماذج للشبكات العصبية التلافيفية العميقة (ResNet50 وDensenet161 وEfficientNetB5). تم استخدام نهج مصنف الاندماج لدمج النماذج الثلاثة من أجل تشخيص شامل ودقيق. تتميز النماذج الثلاثة بمعدلات دقة نسبية تبلغ 69.78٪ و80.57٪ و81.29٪ في تصنيفاتها الخاصة. ومع ذلك، زادت الدقة الإجمالية إلى 90.28 في المائة عند استخدام مصنف الاندماج. هذا يدل على أن الطريقة المقترحة مفيدة لتحديد اعتلال الشبكية الخداجي عند الخدج. تشير نتائج الدراسة إلى أن الطريقة المقترحة لديها القدرة على تعزيز الدقة والسرعة التي يتم بها تشخيص اعتلال الشبكية الخداجي بشكل كبير، مما قد يؤدي بدوره إلى الكشف المبكر عن المرض وعلاجه وتقليل احتمالية الإصابة بعمى الأطفال.

Rapid Adaptation for Active Pantograph Control in High-Speed Railway via Deep Meta Reinforcement Learning.

Active pantograph control is the most promising technique for reducing contact force (CF) fluctuation and improving the train's current collection quality. Existing solutions, however, suffer from two significant limitations: 1) they are incapable of dealing with the various pantograph types, catenary line operating conditions, changing operating speeds, and contingencies well and 2) it is challenging to implement in practical systems due to the lack of rapid adaptability to a new pantograph-catenary system (PCS) operating conditions and environmental disturbances. In this work, we alleviate these problems by developing a revolutionary context-based deep meta-reinforcement learning (CB-DMRL) algorithm. The proposed CB-DMRL algorithm combines Bayesian optimization (BO) with deep reinforcement learning (DRL), allowing the general agent to adapt to new tasks quickly and efficiently. We evaluated the CB-DMRL algorithm's performance on a proven PCS model. The experimental results demonstrate that meta-training DRL policies with latent space swiftly adapt to new operating conditions and unknown perturbations. The meta-agent adapts quickly after two iterations with a high reward, which require only ten spans, approximately equal to 0.5 km of PCS interaction data. Compared with state-of-the-art DRL algorithms and traditional solutions, the proposed method can promptly traverse scenario changes and reduce CF fluctuations, resulting in an excellent performance.

A Simple Magnetic-Flux-Based Method for Localizing High-Resistance Stator Ground Faults in Synchronous Machines

This paper presents a new offline method based on the analysis of the magnetic flux to determine the location of stator ground faults in synchronous machines. First, the defective phase is identified via the insulation resistance (Megger) test, and then the longitudinal axis of the rotor is aligned with the faulty phase winding. During two measuring modes, a voltage is applied separately to each end of the faulty phase while the other end is floating and the core is grounded. The application of the voltage between each terminal of the faulty phase and ground will lead to the circulation of a current through the stator winding to the fault point, being its path closed by the grounded core. A magnetic flux will then be created, which can be sensed indirectly by measuring the induced voltage in the rotor field winding. The fault point can be identified by measuring the applied current or voltage and the magnetic flux created in both measuring modes. Since stator ground faults usually occur via high resistances, the method is extended for the detection of such faults by processing the data using the stationary wavelet transform. The experimental results show that the proposed method is very sensitive and is able to identify the ground fault location for fault resistances up to a few hundred of k-Ohms.

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary characteristics and the individual difference between subjects, how to obtain the effective signal features of the EEG for practical application is still a challenging task. In this article, we investigate the EEG feature learning problem and propose a novel temporal feature learning method based on amplitude-time dual-view fusion for automatic sleep staging. First, we explore the feature extraction ability of convolutional neural networks for the EEG signal from the perspective of interpretability and construct two new representation signals for the raw EEG from the views of amplitude and time. Then, we extract the amplitude-time signal features that reflect the transformation between different sleep stages from the obtained representation signals by using conventional 1-D CNNs. Furthermore, a hybrid dilation convolution module is used to learn the long-term temporal dependency features of EEG signals, which can overcome the shortcoming that the small-scale convolution kernel can only learn the local signal variation information. Finally, we conduct attention-based feature fusion for the learned dual-view signal features to further improve sleep staging performance. To evaluate the performance of the proposed method, we test 30-s-epoch EEG signal samples for healthy subjects and subjects with mild sleep disorders. The experimental results from the most commonly used datasets show that the proposed method has better sleep staging performance and has the potential for the development and application of an EEG-based automatic sleep staging system.

Deep Spectral Clustering With Constrained Laplacian Rank.

Spectral clustering (SC) is a well-performed and prevalent technique for data processing and analysis, which has attracted significant attention in the field of clustering. While the scalability and generalization ability of this method make it prohibitive for the large-scale dataset and the out-of-sample-extension problem. In this work, we propose a new efficient deep clustering architecture based on SC, named deep SC (DSC) with constrained Laplacian rank (DSCCLR). DSCCLR develops a self-adaptive affinity matrix with a clustering-friendly structure by constraining the Laplacian rank, which greatly mines the intrinsic relationships. Meanwhile, by introducing a simple fully connected network with an orthogonality constraint on the last layer, DSCCLR learns discriminative representations in a short training time. The proposed method has the following salient properties: 1) it overcomes limited generalization ability and scalability of the existing DSC methods; 2) it explores the intrinsic relationship between samples in the affinity matrix, which maintains the latent manifold of data as much as possible; and 3) it alleviates the complexity of eigendecomposition via a simple but effective fully connected network. The extensive empirical results demonstrate the superiorities of DSCCLR over other 17 clustering methods.

Active-Control-Based Three-Phase Reclosing Scheme for Single Transmission Line With PMSGs

Electric devices may be damaged due to the strong secondary impact when the circuit breaker recloses on permanent faults, so it is significant to identify the fault nature to reduce the harm of grid faults. To achieve this point, a new control scheme is designed to make permanent magnet synchronous generator (PMSG) based wind turbines behave as a three-phase symmetrical voltage source, so the overcurrent can be detected for permanent faults when the circuit breaker on the PMSG side is reclosed, but only the tiny capacitance current is present for temporary faults. Therefore, the fault nature can be identified by detecting the current amplitude. To accurately extract the power-frequency phasors, a fast matrix pencil extraction method is adopted. This method solves the difficulty in fault nature detection caused by the rapid disappearance of current and voltage after a three-phase trip using the controllability of power electronics. Meanwhile, the severe secondary impact can be reduced since the injected current is small even for permanent faults. Additionally, the proposed method can ensure the fault current at the inverter terminal is within the limiting value and operate properly for remote high-resistance faults. Extensive simulations and experiments are performed to verify this method.