IEEE Access最新文献

{"title":"Transient Fluctuation Suppression Strategy for MMC Flexible DC Systems Based on Gain-Varying Extended Disturbance Observer","authors":"Huanruo Qi;Yilong Kang;Xiaojv Lv;Ziwen Liu;Xiangyang Yan;Fang Guo","doi":"10.1109/ACCESS.2024.3474696","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474696","url":null,"abstract":"Modular Multilevel Converter Based High Voltage Direct Current (MMC-HVDC) system is an important technical means to realize grid connection and transmission of new energy. When the external disturbance occurs in the MMC-HVDC system, the submodule capacitance voltages suffer instant impact due to the mutual coupling in the MMC, which causes transient fluctuation and unstable operation of the system. In order to suppress the impact of external disturbance on the internal submodule capacitance voltage transient fluctuations of MMC-HVDC system, the transient fluctuation suppression strategy based on gain-varying extended disturbance observer is proposed in this paper. Firstly, the transient response characteristic equation of the submodule capacitor voltage fluctuation component for MMC-HVDC system is constructed, which reveals the relation between the external output and internal dynamic response of the system. Next, a gain-varying extended disturbance observer is proposed to effectively observe the time-varying disturbance current components in the system, and the corresponding submodule capacitor voltage transient fluctuation suppression strategy is then proposed to improve the dynamic response performance and anti-interference ability of the MMC-HVDC system under different disturbances. The simulation model of MMC-HVDC system is built on PSCAD/EMTDC platform, and the validity and correctness of the proposed method are verified by several simulation tests.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146303-146314"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Tracking Control Under Switched and Fixed Topologies With a Leader’s Unknow and Bounded Inputs for Linear Multi-Agent Systems","authors":"Yandong Li;Yuyi Huang;Ling Zhu;Zehua Zhang;Yuan Guo","doi":"10.1109/ACCESS.2024.3474734","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474734","url":null,"abstract":"This document tackles the issue of distributed adaptive tracking control for generic linear multi-agent systems. Specifically, the focus is on linear systems with non-zero control inputs from the leader, which are not directly accessible to the follower. Unlike existing literature limited to fixed topologies, this study tackles the challenge of switching topologies. A distributed discontinuous adaptive tracking controller uses Riccati inequalities and based on the state errors between neighboring agents. This controller adaptively adjusts the coupling weights between adjacent agents and ensures that switching communication topologies does not affect follower agent communication and remains undirected, allowing the leader to reach all followers via a directed path in the joint graph. This paper also examines stable multi-agent systems using fixed and dynamic network configurations. A communication graph in which the leader is bound by their control inputs is shown to be able to demonstrate such behavior, provided that the graph is jointly connected. A description of the followers’ states will effectively approach and converge on the leader’s state. An additional simulation experiment confirms its effectiveness and robustness of suggested method.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146277-146290"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Brain-Like Characteristics of DNNs With Spatiotemporal Features: A Study Based on the Müller-Lyer Illusion","authors":"Hongtao Zhang;Kiminori Matsuzaki;Shinichi Yoshida","doi":"10.1109/ACCESS.2024.3475478","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3475478","url":null,"abstract":"This study explores the capabilities and limitations of deep neural networks (DNNs) in simulating human visual illusions, particularly the Müller-Lyer illusion. Visual illusions are often overlooked in DNN research, which tends to neglect the inherent temporal dynamics and complex context dependencies of human visual processing. By integrating self-supervised learning and teacher-student network models, we examined the performance of DNNs combining spatiotemporal dynamics on visual illusion phenomena. The study employed various video classification models including ResNet3D(R3D-18), Multiscale Vision Transformers(MViT-V1-B), 3DCNN(S3D), and 3D Swin Transformer(Swin3D-T), as well as PredNet for experiments to explore their perception abilities for the Müller-Lyer illusion. The experimental results were visualized using representational dissimilarity matrices (RDMs) and gradient-weighted class activation mapping (Grad-CAM), showing that DNNs considering spatiotemporal characteristics can simulate perceptual errors similar to those of humans in handling these types of visual illusions. Specifically, R3D-18, MViT-V1-B, and S3D exhibited high similarity on the diagonal in both Type A and Type B RDMs, indicating similar length perception for inward and outward pointing lines within the models. In the control group RDMs, the high similarity distribution slightly shifted upwards from the diagonal, suggesting that outward-pointing lines need to be longer to match inward-pointing lines, mirroring human perception. Additionally, we found significant differences in the sensitivity and response patterns to visual illusions among different model architectures, emphasizing the impact of dataset selection and model structure on the performance of DNNs in visual illusions. On the contrary, while spatiotemporal DNNs showed advantages in RDM analysis, static models like AlexNet, Vgg19, and ResNet101 demonstrated more focused attention on arrows in Grad-CAM analysis, similar to human visual processing. The significant differences in the sensitivity and response patterns to visual illusions among different model architectures emphasize the impact of dataset selection and model structure on the performance of DNNs in visual illusions.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147192-147208"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706901","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Review of Multi-Objective Optimization Algorithms for Design and Safety Optimization in Electric Vehicles","authors":"I Gede S. S. Dharma;Rachman Setiawan","doi":"10.1109/ACCESS.2024.3475032","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3475032","url":null,"abstract":"Despite the widespread use of established optimization algorithms like Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) in real-world engineering optimization problems, newer algorithms such as Two-Stage NSGA-II (TS-NSGA-II), Dynamic Constrained NSGA-III (DCNSGA-III), MOEA/D with Virtual Objective Vectors (MOEA/D-VOV), Large-Scale Evolutionary Multi-Objective Optimization Assisted by Directed Sampling (LMOEA-DS), and Super-Large-Scale Multi-Objective Evolutionary Algorithm (SLMEA) remain underexplored in the context of Battery Electric Vehicle (BEV) safety, particularly in optimizing complex, non-linear, and constrained multi-objective problems like crashworthiness and thermal management. This study addresses this gap by comparing these newer algorithms against traditional methods using a newly introduced benchmark problem focused on BEV battery protection (RWMOP-BEV). The design problem aimed to maximize energy absorption during impact, enhance crash force efficiency, and optimize temperature difference, all while adhering to design space and operational constraints. The comparative results, based on four performance indicators—hypervolume (HV), inverted generational distance (IGD), averaged Hausdorff distance \u0000<inline-formula> <tex-math>$left ({{ Delta _{p} }}right)$ </tex-math></inline-formula>\u0000, and spread—reveal that SLMEA emerged as the best algorithm, not only for RWMOP-BEV but also across other benchmark sets, including DTLZ problems and other real-world multi-objective optimization problems.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146376-146396"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the Classification Accuracy of EEG-Informed Inner Speech Decoder Using Multi-Wavelet Feature and Support Vector Machine","authors":"Mokhles M. Abdulghani;Wilbur L. Walters;H. Khalid Abed","doi":"10.1109/ACCESS.2024.3474854","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474854","url":null,"abstract":"Speech involves the synchronization of the brain and the oral articulators. Inner speech, also known as imagined speech or covert speech, refers to thinking in the form of sound without intentional movement of the lips, tongue, or hands. Decoding human thoughts is a powerful technique that can help individuals who have lost the ability to speak. This paper introduces a high-performance brain wave decoder based on inner speech, using a novel feature extraction method. The approach combined Support Vector Machine (SVM) and multi-wavelet feature extraction techniques to decode two EEG-based inner speech datasets (Data 1 and Data 2) into internally spoken words. The proposed approach achieved an overall classification accuracy of 68.20%, precision of 68.22%, recall of 68.20%, and F1-score of 68.21% for Data 1, and accuracy of 97.5%, precision of 97.73%, recall of 97.50%, and F1-score of 97.61% for Data 2. Additionally, the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) demonstrated the validity of the proposed approach for classifying inner speech commands by achieving a macro-average of 78.76% and 99.32% for Data 1 and Data 2, respectively. The EEG-based inner speech classification method proposed in this research has the potential to improve communication for patients with speech disorders, mutism, cognitive development issues, executive function problems, and mental disorder.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"147929-147941"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706230","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Image Recognition Using a Novel Neural Network Construction Controlled by a Kernel Method Encoder","authors":"Pengzhi Li;Yan Pei;Jianqiang Li;Haihua Xie","doi":"10.1109/ACCESS.2024.3474742","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474742","url":null,"abstract":"The recognition and diagnosis of medical images is one of the important topics in artificial intelligence, such as colonic polyp and cataract classification. The appearance, shape, and size of pathological features in medical images significantly influence the accuracy of identification. Improving recognition accuracy is crucial due to the difficulty in distinguishing pathological features accurately. This paper presents a medical image recognition method utilizing a neural network controlled by a kernel method encoder, which presents the originality of this research. Medical images are enhanced to extract various features. Using the kernel method encoder, a high-dimensional distinguishable feature map is generated. This mitigates the problem of insufficient features in a single image. Image classification is achieved by optimizing kernel method encoder parameters and training the enhanced neural network classification model. We conducted experiments on colonography computed tomography images and colour fundus images. Initially, we conducted classification experiments on the colonography computed tomography image dataset. The experimental results demonstrate that the proposed model improves classification accuracy and shows good performance. Subsequently, we conducted the same classification experiment on colour fundus images using the proposed method. The experimental results indicate that this method improves the classification accuracy. The accuracy of this method on two medical image datasets is 92.1% and 97.4%, respectively. The proposed method performs well across both medical image datasets. This further demonstrates the robustness of the multi-feature and enhanced neural network model classification method.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146807-146817"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient Scan Diagnosis for Intermittent Faults Using CNN With Multi-Channel Data","authors":"Hyojoon Yun;Hyeonchan Lim;Hayoung Lee;Doohyun Yoon;Sungho Kang","doi":"10.1109/ACCESS.2024.3475229","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3475229","url":null,"abstract":"Scan chains are essential for enhancing the testability of semiconductor circuits. While scan chains enhance the testing capability, defects can also occur in scan chains due to the hardware overhead caused by scan chains. To prevent a decrease in yield due to such defects, scan chain diagnosis is widely used in semiconductor manufacturing as an important system. Particularly, with the increasing complexity of semiconductor circuits, there is a growing necessity for the diagnosis of intermittent faults. Since the distinct patterns are shown in test results for intermittent faults compared to permanent faults, a decrease in diagnostic accuracy is caused by the occurrence of intermittent faults. To address this problem, this paper introduces a new deep-learning-based scan-chain diagnosis method, designed to diagnose both intermittent and permanent faults. The proposed method introduces two new concepts for diagnosing not only permanent but also intermittent faults. One is a CNN optimized for scan chain diagnosis, and the other is newly optimized input data tailored for this CNN architecture. The proposed CNN architecture is composed of multiple layers centered around the modified inception module adapted for scan chain diagnosis, maintaining spatial and local information while enabling additional feature extraction. Furthermore, the new input data is multi-channel data composed of subset failure vectors (SFVs), integer failure vectors (IFVs), and fan-out vectors, allowing for the maximization of CNN characteristics. The experimental results demonstrate that diagnostic accuracy for intermittent faults is significantly improved by the proposed method compared to previous works.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146463-146475"},"PeriodicalIF":3.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Optimization for Mobile Wireless Sensor Networks Routing Protocol Using Adaptive Boosting With Sensitivity Analysis","authors":"Kegomoditswe Boikanyo;Adamu Murtala Zungeru;Abid Yahya;Caspar K. Lebekwe","doi":"10.1109/ACCESS.2024.3474288","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474288","url":null,"abstract":"Mobile Wireless Sensor Networks (MWSNs) are employed in diverse applications, including remote patient monitoring systems (RPMS). In RPMS, biomedical sensors collect physiological data from patients outside clinical settings, and the data is transmitted wirelessly to healthcare providers for informed decisions. However, most routing algorithms focus on optimizing routing in static RPMS, neglecting mobile RPMS. This paper introduces an approach to improving the efficiency of MWSN algorithms, with a focus on the Termite Hill Routing Algorithm (THA) applied in RPMS. The investigation employs methods of sensitivity analysis to reveal how crucial parameters, such as the quantity of nodes, speed of nodes, and distribution of nodes affect the behavior and throughput of the algorithm. The paper introduces a novel methodology, Enhanced Regression-based Gradient Boosting (ERGB), which optimizes the algorithm’s parameters and enhances performance. ERGB is a unique combination of regression-based adaptive gradient boosting with sensitivity analysis and a robust machine-learning algorithm. It identifies and ranks the most critical factors that affect throughput in the constantly changing network environment of mobile RPMS. The study found that the network topology size and the source node speed are the most critical parameters impacting the algorithm, piquing the audience’s interest in this innovative approach. The study compared the optimized THA with default parameters and two other algorithms (AODV and Bee Sensor) used with optimized parameters. The results demonstrate significant improvements in throughput, reaching a maximum of about 2.6 Kb/s compared to 0.3 Kb/s with default parameters.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146494-146512"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705357","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Community Detection via Convolutional Neural Network: A Modified Approach Based on MRFasGCN Algorithm","authors":"Puneet Kumar;Dalwinder Singh;Mamoona Humayun;Ali Alqazzaz;Arun Malik;Ibrahim Alrashdi;Isha Batra;Ghadah Naif Alwakid","doi":"10.1109/ACCESS.2024.3474303","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474303","url":null,"abstract":"Community detection is a very important research topic in the field of Social Network Analysis. Lots of researchers are working in this field due to its applications in various fields like medicine, social, Business, Marketing, and research. Researchers are proposing new algorithms to detect the communities having better performance as compared to the existing techniques. Initially, Newman and Girvan proposed traditional algorithms for community detection from social networks in 2004, but with the growth of social networks, Convolutional Neural Network (CNN) based algorithms are proposed by different researchers in recent years due to the inefficiency of traditional methods. After reviewing the state-of-the-art algorithms based on CNN learned that MRFasGCN is having the best performance compared to any other state-of-the-art algorithms for large data sets. In this algorithm, researchers have integrated the technique of Graph Convolutional Neural Network (GCN) with the statistical model Markov Random Field (MRF) to get better results and after implementing it on large datasets comparison is done on its results with other state-of-the-art algorithms and got to know its performance is far better than any other algorithm. While MRFasGCN is performing well on social networks and provides ground truth communities, there is a possibility available for improvement due to the sparsity problem. This paper proposes a new algorithm called Modified MRFasGCN. Two modifications are done to the existing algorithm, 1. In pre-processing, rather than passing the adjacency matrix with the normalized adjacency matrix, it will pass the reconstructed adjacency matrix and normalized reconstructed adjacency matrix, which resolves the sparsity problem, 2. GCN layer output will be fed to the MRF layer and refined results will be passed to the Adam optimizer without subtraction. Our experimental analysis shows that the modified algorithm provides better ground truth communities than the MRFasGCN and solves the problem of sparsity as passes a reconstructed adjacency matrix. In this paper, the proposed algorithm is executed on different datasets having different sizes like CORA (2708 Nodes), Flicker (80513 Nodes) and DBLP (317080 Nodes) and compared on different Community Detection metrics like accuracy, NMI, F1 Score, and execution time with other algorithms. After Experiments NMI value for MRFasGCN on DBLP data set is 0.662 while for Modified MRFasGCN it is 0.672, Modified MRFasGCN algorithm provides significant improvement of 2.9% in performance as compared to MRFasGCN. F1-Score of proposed algorithm is 0.511 on DBLP dataset which is better than MRFasGCN.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146733-146748"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harmonic Distortion Indices for Experimental Characterization of Variable Frequency Drives","authors":"Angel Arranz-Gimon;Daniel Morinigo-Sotelo;Angel Zorita-Lamadrid;Oscar Duque-Perez","doi":"10.1109/ACCESS.2024.3474176","DOIUrl":"https://doi.org/10.1109/ACCESS.2024.3474176","url":null,"abstract":"Frequency converters controlling electric motors are widely used as they increase the functionality of the equipment and improve energy efficiency. However, it is inevitable that the quality of the power delivered to the motor does not have the same characteristics as when it is supplied from the mains. Due to the impact of the power quality on the operation of the motor and even on its possible diagnosis, it is desirable to characterise the power quality, preferably by means of rates or indices that reflect the quality as completely as possible. However, the rates specified in the standards and in the literature are designed to characterise the quality delivered from the grid and are not well adjusted to the features of the signal at the output of the converter with a high interharmonic and harmonic content at high and low frequencies, and with variable fundamental frequencies. For this reason, this work proposes a set of distortion rates and the appropriate procedures for calculating them to reflect as reliably as possible the quality of the energy delivered by the converter to the motor. To verify the validity of the proposal, several practical examples are presented with induction motors fed from different frequency converters.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"12 ","pages":"146343-146358"},"PeriodicalIF":3.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705286","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}