IET Signal Processing最新文献

Methods of Sparse Measurement Matrix Optimization for Compressed Sensing 压缩感知稀疏测量矩阵优化方法

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2025-04-24 DOI: 10.1049/sil2/1233853

Renjie Yi, Shunan Han, Peng Liu, Bo Zhang, Hang Liu

{"title":"Methods of Sparse Measurement Matrix Optimization for Compressed Sensing","authors":"Renjie Yi, Shunan Han, Peng Liu, Bo Zhang, Hang Liu","doi":"10.1049/sil2/1233853","DOIUrl":"https://doi.org/10.1049/sil2/1233853","url":null,"abstract":"<div>\u0000 <p>In compressed sensing (CS), a sparse measurement matrix with few nonzero entries is more competitive than a dense matrix in reducing the number of multiplication units. Recent studies indicate that an optimized measurement matrix having low coherence with a specified dictionary can significantly improve the reconstruction performance. This paper considers the optimization problem of the sparse measurement matrix. The optimized sparse measurement matrix is formulated by minimizing the Frobenius norm of the difference between the Gram matrix of the sensing matrix and the target Gram matrix. First, the approach for updating the target Gram matrix is designed to reduce the maximal, average, and global coherence simultaneously. Then, an improved momentum gradient algorithm for updating the sparse measurement matrix is derived to accelerate convergence. On the basis of alternating minimization, two optimization algorithms are proposed. The experimental results show that the proposed algorithms outperform several state-of-the-art methods in terms of reconstruction performance.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/1233853","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Accuracy Frequency Detection and Analysis via Adaptive Frequency Standard Tracking 基于自适应频率标准跟踪的高精度频率检测与分析

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2025-04-23 DOI: 10.1049/sil2/8914468

Baoqiang Du, Zhengze Xiao, Lanqin Tan

{"title":"High-Accuracy Frequency Detection and Analysis via Adaptive Frequency Standard Tracking","authors":"Baoqiang Du, Zhengze Xiao, Lanqin Tan","doi":"10.1049/sil2/8914468","DOIUrl":"https://doi.org/10.1049/sil2/8914468","url":null,"abstract":"<div>\u0000 <p>Precise frequency detection is one of the key problems to be solved in a high-accuracy transfer of time and frequency. The solution to this problem is helpful in improving the precision of the phase noise measurement, atomic frequency standard, and time synchronization, which plays a strong role in the whole precision measurement physics fields. A high-accuracy frequency detection and analysis based on adaptive frequency standard tracking are proposed for time–frequency signal processing without frequency normalization. First, an adaptive frequency standard signal is generated by using an FPGA to control the DDS based on the measured signal. This signal can achieve phase comparison with the measured signal under any frequency relationships including complex and large-frequency difference relationships, widening a frequency measurement range. Second, the frequency standard signal is put off by the delay chains. The rough time delaying can generate many phase coincidences, which can shorten the gate switch time to achieve fast time response. The finer delaying can provide a very high measurement resolution without transforming the frequency relationships between the measured and reference signals. And then, a differential synchronization is performed between the measured and reference signals after shaping and conditioning the two signals. The obtained optimal phase coincidences, that is, fuzzy zone edge pulses, are used as the gate signals. A precise frequency measurement for the measured signals can then be realized by counting the measured and reference signals without gap in the gate time. The testing results show that the frequency measurement accuracy of the system can reach 1.7 × 10<sup>−13</sup>/s.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/8914468","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Method of Abnormal Behavior Detection for Safety Site Surveillance 一种用于安全现场监控的异常行为检测方法

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2025-04-08 DOI: 10.1049/sil2/8880932

Wenjing Wang, Yangyang Zhang, QingE Wu

{"title":"A Method of Abnormal Behavior Detection for Safety Site Surveillance","authors":"Wenjing Wang, Yangyang Zhang, QingE Wu","doi":"10.1049/sil2/8880932","DOIUrl":"https://doi.org/10.1049/sil2/8880932","url":null,"abstract":"<div>\u0000 <p>In order to accurately detect and give alerts to the anomalies in visual images, this paper proposes an image anomaly detection method. For the complex background in the image, a multiframe differential superposition algorithm is proposed to denoise the target image; a feature extraction method is given to extract features for the target image, and then a more complete image with target features is obtained after filtering; a normal behavior model is established to extract the motion information of the target from a single frame of the image; an abnormal detection method is proposed to determine whether it belongs to abnormal behavior. The experimental results show that the accuracy of the abnormal behavior detection method proposed in this paper can better discern the beginning and end of behavior occurrence, abnormal behavior prediction, behavior online detection, and other aspects from the visual image data stream, and the correct detection rate is more than 90%, which reduces the consumption of human resources. At the same time, compared with the existing anomaly detection methods, this anomaly detection presented in this paper not only has higher accuracy, faster speed, and stronger anti-interference ability but also has a better detection effect. These researches advance in this paper can provide a new method and decision support for abnormal behavior detection and identification in a variety of scenarios.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/8880932","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Abnormal Diagnosis Method for Process Parameter Fluctuation Based on Power Spectral Density and Statistical Characteristics 基于功率谱密度和统计特征的过程参数波动异常诊断方法

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2025-03-09 DOI: 10.1049/sil2/8178555

Zhu Wang, Jiale Zhan, Qinghe Zheng, Shaokang Zhang

{"title":"The Abnormal Diagnosis Method for Process Parameter Fluctuation Based on Power Spectral Density and Statistical Characteristics","authors":"Zhu Wang, Jiale Zhan, Qinghe Zheng, Shaokang Zhang","doi":"10.1049/sil2/8178555","DOIUrl":"https://doi.org/10.1049/sil2/8178555","url":null,"abstract":"<div>\u0000 <p>In processes of refining and chemical productions, alarm systems are generally centralized alarm management systems for process parameters. However, in order to address the challenges of advanced manipulation and maintenance during emergencies, there has been limited research on timely alarming for individual critical process parameters. This paper proposes a method based on the combination of power spectral density and statistical characteristics, which can quickly and accurately diagnose large-scale trend changes and short-term nonstationary abnormal trends in process parameters. First, the method employs incremental data from historical records of critical process parameters for volatility analysis. Second, the historical data of critical process parameters are segmented into multiple appropriately sized datasets. We employ a combined analysis of power spectral density and statistical characteristics to extract features from multitude of incremental data. Meanwhile, we have designed a tuning scheme for critical frequencies and their threshold parameters, which can be used for testing and online diagnostics. Experimental validation is performed using actual critical process parameters data from Chinese refineries. The experimental results indicate that the method can detect large-scale trends and short-term nonstationary abnormal trends in process parameters, demonstrating good diagnostic performance.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/8178555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143581365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Allocation of Power and Subcarrier for Low Delay and Stable Power Line Communication 低延迟稳定电力线通信的功率与子载波联合分配

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2025-02-28 DOI: 10.1049/sil2/4485513

Zhixiong Chen, Zhihui Yang, Zeng Dou

{"title":"Joint Allocation of Power and Subcarrier for Low Delay and Stable Power Line Communication","authors":"Zhixiong Chen, Zhihui Yang, Zeng Dou","doi":"10.1049/sil2/4485513","DOIUrl":"https://doi.org/10.1049/sil2/4485513","url":null,"abstract":"<div>\u0000 <p>Power line communication (PLC) can realize low-cost IOT access and is widely used in home and new energy applications. To meet the requirements of low-latency services such as remote control and demand-side response, a joint optimal allocation algorithm of subcarriers and their power based on diversity grouping and channel prediction is proposed. First, considering the influence of channel estimation and prediction errors, a resource allocation model is established with the constraints of subcarrier data volume and transmission power, and the objective is to minimize the total delay of multiple slots. The optimal power allocation under the condition of a single slot is realized by subcarrier diversity grouping and improved genetic algorithm, and then the subcarrier power below the rate threshold is recycled and allocated to the slot with good prediction performance. Finally, the performance of the algorithm is compared and analyzed by simulation. The results show that the proposed algorithm can reduce the rate fluctuation and improve the system delay performance and deterministic transmission ability under the condition of ensuring the average rate optimization.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/4485513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Control Filter Estimation for Multichannel Active Noise Control Using Kronecker Product Decomposition 基于Kronecker积分解的多通道主动噪声控制滤波器估计

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2025-01-10 DOI: 10.1049/sil2/2128989

Hakjun Lee, Youngjin Park

{"title":"Control Filter Estimation for Multichannel Active Noise Control Using Kronecker Product Decomposition","authors":"Hakjun Lee, Youngjin Park","doi":"10.1049/sil2/2128989","DOIUrl":"https://doi.org/10.1049/sil2/2128989","url":null,"abstract":"<div>\u0000 <p>Active noise control (ANC) algorithms have been developed within the adaptive algorithm framework. However, multichannel ANC systems, which include numerous reference sensors, control speakers, and error microphones, require a very long control filter converging time for control filter estimation. Traditional system identification methods, such as the Wiener filter method, are better suited for such systems because of their relatively shorter converging time. However, they require large amounts of data to achieve accurate statistical estimation. Therefore, this article proposes a control filter estimation method that requires only a short length of data. An iterative Wiener filter solution using Kronecker product decomposition for multichannel ANC systems converts the filter estimation process by breaking down the extensive control filter into multiple shorter control filters through Kronecker product decomposition. This decomposition effectively reduces the high-dimensional system identification problem into manageable low-dimensional ones. Numerical simulations demonstrate the superiority of the proposed method over conventional Wiener filter techniques, especially in scenarios when limited data are available for control filter estimation.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/2128989","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Robust Tensor Decomposition Based on MCMC Algorithm for Traffic Data Completion 基于MCMC算法的贝叶斯鲁棒张量分解交通数据补全

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2025-01-07 DOI: 10.1049/sil2/4762771

Longsheng Huang, Yu Zhu, Hanzeng Shao, Lei Tang, Yun Zhu, Gaohang Yu

{"title":"Bayesian Robust Tensor Decomposition Based on MCMC Algorithm for Traffic Data Completion","authors":"Longsheng Huang, Yu Zhu, Hanzeng Shao, Lei Tang, Yun Zhu, Gaohang Yu","doi":"10.1049/sil2/4762771","DOIUrl":"https://doi.org/10.1049/sil2/4762771","url":null,"abstract":"<div>\u0000 <p>Data loss is a common problem in intelligent transportation systems (ITSs). And the tensor-based interpolation algorithm has obvious superiority in multidimensional data interpolation. In this paper, a Bayesian robust tensor decomposition method (MBRTF) based on the Markov chain Monte Carlo (MCMC) algorithm is proposed. The underlying low CANDECOMP/PARAFAC (CP) rank tensor captures the global information, and the sparse tensor captures local information (also regarded as anomalous data), which achieves a reliable prediction of missing terms. The low CP rank tensor is modeled by linear interrelationships among multiple latent factors, and the sparsity of the columns on the latent factors is achieved through a hierarchical prior approach, while the sparse tensor is modeled by a hierarchical view of the Student-<i>t</i> distribution. It is a challenge for traditional tensor-based interpolation methods to maintain a stable performance under different missing rates and nonrandom missing (NM) scenarios. The MBRTF algorithm is an effective multiple interpolation algorithm that not only derives unbiased point estimates but also provides a robust method for the uncertainty measures of these missing values.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2025 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/4762771","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human-Centered UAV–MAV Teaming in Adversarial Scenarios via Target-Aware Intention Prediction and Reinforcement Learning 基于目标感知意图预测和强化学习的对抗场景中以人为中心的UAV-MAV团队

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-12-19 DOI: 10.1049/sil2/7719848

Wei Hao, Huaping Liu, Jia Liu, Wenjie Li, Lijun Chen

{"title":"Human-Centered UAV–MAV Teaming in Adversarial Scenarios via Target-Aware Intention Prediction and Reinforcement Learning","authors":"Wei Hao, Huaping Liu, Jia Liu, Wenjie Li, Lijun Chen","doi":"10.1049/sil2/7719848","DOIUrl":"https://doi.org/10.1049/sil2/7719848","url":null,"abstract":"<div>\u0000 <p>Tacit understanding refers to the ability of team members to work together seamlessly and intuitively without explicitly communicating in detail. This ability is crucial for effective teamwork in complex situations that involve both manned and unmanned aerial vehicles (UAVs). Existing collaborative tasks between manned and unmanned aircraft focus mainly on optimizing communication and the UAVs’ flight paths but neglect the benefits of tacit and intelligent operational cooperation with pilots. To address this limitation, we propose a tacit collaborative attack method that utilizes the UAVs’ capacity for tacit understanding to infer human intent and select the appropriate targets for collaborative attack missions. A learning framework incorporating intention prediction and reinforcement learning paradigms is developed to teach the UAV to generate corresponding collaborative attack actions. Finally, we present results from extensive simulation experiments in a homemade game environment to demonstrate the efficiency and scalability of our method within the proposed framework. The video can be found at https://www.youtube.com/watch?v=CjXhkD7ko14.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/7719848","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Att-U2Net: Using Attention to Enhance Semantic Representation for Salient Object Detection at - u2net：利用注意力增强显著目标检测的语义表示

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-11-29 DOI: 10.1049/sil2/6606572

Chenzhe Jiang, Banglian Xu, Qinghe Zheng, Zhengtao Li, Leihong Zhang, Zimin Shen, Quan Sun, Dawei Zhang

{"title":"Att-U2Net: Using Attention to Enhance Semantic Representation for Salient Object Detection","authors":"Chenzhe Jiang, Banglian Xu, Qinghe Zheng, Zhengtao Li, Leihong Zhang, Zimin Shen, Quan Sun, Dawei Zhang","doi":"10.1049/sil2/6606572","DOIUrl":"https://doi.org/10.1049/sil2/6606572","url":null,"abstract":"<div>\u0000 <p>Saliency object detection has been widely used in computer vision tasks such as image understanding, semantic segmentation, and target tracking by mimicking the human visual perceptual system to find the most visually appealing object. The U2Net model has shown good performance in salient object detection (SOD) because of its unique U-shaped residual structure and the U-shaped structural backbone incorporating feature information of different scales. However, in the U-shaped structure, the global semantic information computed from the topmost layer may be gradually interfered by the large amount of local information dilution in the top-down path, and the U-shaped residual structure has insufficient attention to the features in the salient target region of the image and will pass redundant features to the next stage. To address these two shortcomings in the U2Net model, this paper proposes improvements in two aspects: to address the situation that the global semantic information is diluted by local semantic information and the residual U-block (RSU) module pays insufficient attention to the salient regions and redundant features. An attentional gating mechanism is added to filter redundant features in the U-structure backbone. A channel attention (CA) mechanism is introduced to capture important features in the RSU module. The experimental results prove that the method proposed in this paper has higher accuracy compared to the U2Net model.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2/6606572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142749280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Reinforcement Learning Explores EH-RIS for Spectrum-Efficient Drone Communication in 6G 深度强化学习探索 6G 频谱高效无人机通信的 EH-RIS

IF 1.1 4区工程技术

IET Signal Processing Pub Date : 2024-11-22 DOI: 10.1049/2024/9548468

Farhan M. Nashwan, Amr A. Alammari, Abdu saif, Saeed Hamood Alsamhi

{"title":"Deep Reinforcement Learning Explores EH-RIS for Spectrum-Efficient Drone Communication in 6G","authors":"Farhan M. Nashwan, Amr A. Alammari, Abdu saif, Saeed Hamood Alsamhi","doi":"10.1049/2024/9548468","DOIUrl":"https://doi.org/10.1049/2024/9548468","url":null,"abstract":"<div>\u0000 <p>Reconfigurable intelligent surfaces (RISs) have emerged as a groundbreaking technology, revolutionizing wireless networks with enhanced spectrum and energy efficiency (EE). When integrated with drones, the combination offers ubiquitous deployment services in communication-constrained areas. However, the limited battery life of drones hampers their performance. To address this, we introduce an innovative energy harvesting (EH), that is, EH-RIS. EH-RIS strategically divides passive reflection arrays across geometric space, improving EH and information transformation (IT). Employing a meticulous, exhaustive search algorithm, the resources of the drone-RIS system are dynamically allocated across time and space to maximize harvested energy while ensuring optimal communication quality. Deep reinforcement learning (DRL) is employed to investigate drone-RIS performance by intelligently allocating resources for EH and signal reflection. The results demonstrate the effectiveness of the DRL-based EH-RIS simultaneous wireless information and power transfer (SWIPT) system, demonstrating enhanced drone-RIS spectrum-efficient communication capabilities. Our investigation is summarized in unleashing potential, which shows how DRL and EH-RIS work together to optimize drone-RIS for next-generation wireless networks.</p>\u0000 </div>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":"2024 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/9548468","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0