Iet Science Measurement & Technology最新文献

{"title":"Frequency Response Analysis-Based Transformer Condition Monitoring Supported by Artificial Intelligence—A Review","authors":"Jawad Faiz,&nbsp;Hanieh Naseri,&nbsp;Hossein Tayyari Ilaghi,&nbsp;Mohammad Hamed Samimi","doi":"10.1049/smt2.70014","DOIUrl":"https://doi.org/10.1049/smt2.70014","url":null,"abstract":"<p>Among the most expensive assets in power grids, power transformers are essential for the reliability of the power supply chain and the overall stability of the grid. Due to their permanent connection to the network, this equipment is exposed to all kinds of faults and phenomena, including short-circuit faults and overvoltages caused by lightning and switching. Hence, ongoing monitoring of the transformer's condition is essential to prevent breakdowns and damage to the transformer. Among the different condition monitoring methods, the frequency response analysis (FRA) method is sensitive to the smallest functional changes of the transformer, as it is completely related to the physics and geometry of the transformer. This method stands out as one of the most effective and efficient approaches to transformer monitoring, especially for detecting mechanical faults. However, the FRA method faces an important challenge of interpretation: the correlation between the type of fault that occurred and the way the transformer's function changes is still not well-known, and studies in this field are ongoing. One of the most widely used methods of interpreting frequency response results is the use of numerical indices, coil modelling, transformer function estimation, and artificial intelligence algorithms. This paper introduces these methods, and their advantages and disadvantages are discussed. Then, the most widely used artificial intelligence algorithms in transformer condition monitoring are presented and compared. Finally, future research directions are anticipated.</p>","PeriodicalId":54999,"journal":{"name":"Iet Science Measurement & Technology","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smt2.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171842","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}

{"title":"Research on Converter Valve Discharge Localisation Technology Based on IVMD-MUSIC Algorithm","authors":"Shaotong Pei,&nbsp;Mianxiao Wu,&nbsp;DeWang Liu,&nbsp;Weiqi Wang,&nbsp;Haichao Sun,&nbsp;Bo Lan","doi":"10.1049/smt2.70012","DOIUrl":"https://doi.org/10.1049/smt2.70012","url":null,"abstract":"<p>Regarding the surface discharge phenomenon of the converter valve, this paper proposes a localisation method based on variational modal decomposition (VMD) and multiple signal classification (MUSIC) to achieve accurate positioning of discharge in the current converter valve. First, a solution for determining the number of modes K in VMD and a new threshold for IMF selection are proposed to enhance the noise removal capability in the valve hall. Second, the improved variational modal decomposition (IVMD) and MUSIC algorithms are combined to accurately identify the discharge phenomenon of the converter valve. This approach addresses the issue of inaccurate localization and failure in conventional methods due to strong noise interference in the converter valve hall. Simulation results indicate that when the signal-to-noise ratio (SNR) is higher than -20 dB, the root mean square error (RMSE) is less than 1.6. Localisation experiments were conducted on three types of discharges—needle-plate, cone-plate and sphere-plate discharges—showing an average angular error of less than 1.8°. Both simulation and experimental results demonstrate that the proposed algorithm exhibits higher localisation accuracy and strong applicability when the SNR is lower than -5 dB. Finally, a partial discharge localisation device was developed based on the proposed algorithm. This device utilises an eight-element cross sensor array and applies the IVMD-MUSIC algorithm for localisation, meeting operational and maintenance requirements and providing convenience for inspection personnel.</p>","PeriodicalId":54999,"journal":{"name":"Iet Science Measurement & Technology","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smt2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896865","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}

{"title":"An Integrated Sensor Array for Water Quality Monitoring","authors":"Hooman Abolfathi,&nbsp;Alireza Nikfarjam,&nbsp;Bahareh Abbaspour","doi":"10.1049/smt2.70013","DOIUrl":"https://doi.org/10.1049/smt2.70013","url":null,"abstract":"&lt;p&gt;Four important quantities in water quality monitoring are temperature, specific electrical conductivity (EC), total dissolved solids (TDS), and pH. In this paper, three sensors for precisely detecting these parameters were designed and fabricated in one structure. Spiral electrodes were made as temperature sensors and circular toothed electrodes were made as EC sensors. The pH sensor comprises two electrodes: the reference electrode (Ag/AgCl) and the working electrode (carbon black/highly porous polyaniline). The response time of the temperature sensor is 13.2 s, and the stability of the sensor is −0.031&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mi&gt;Ω&lt;/mi&gt;\u0000 &lt;msup&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mo&gt;∘&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$;Omega ;^circ {{mathrm{C}}^{ - 1}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, and the sensitivity of the sensor is 0.003 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;Δ&lt;/mi&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;msup&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mo&gt;∘&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$Delta {mathrm{R}};{{mathrm{R}}^{ - 1}}; ^circ {{mathrm{C}}^{ - 1}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. The response time of the pH sensor was reported as 136.2 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;s&lt;/mi&gt;\u0000 &lt;annotation&gt;${mathrm{s}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and the sensor's sensitivity was 8.8 &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;mV&lt;/mi&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;H&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 ","PeriodicalId":54999,"journal":{"name":"Iet Science Measurement & Technology","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smt2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896866","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}

{"title":"Robust Sensor Selection for Reconstructing Thermal Properties in Electromagnetic Devices","authors":"Seyede Faezeh Hosseini,&nbsp;Guillaume Crevecoeur,&nbsp;Hendrik Vansompel","doi":"10.1049/smt2.70010","DOIUrl":"https://doi.org/10.1049/smt2.70010","url":null,"abstract":"<p>Electromagnetic devices have gained widespread use in various systems such as renewable energy systems, electrical motors, generators, and transformers. Despite the state-of-the-art modeling techniques, there are differences between the measured thermal behavior of electromagnetic devices and modeled ones. This research aims to bridge this gap by employing a combination of the finite element method and inverse modeling technique via non-collocated sensor configurations. Due to the restricted physical space and economic constraints, only a limited number of sensors can be strategically positioned within a structure. Consequently, the problem of robust and optimal sensor placement holds crucial significance on the accuracy and quality of the collected data influencing the performance, energy efficiency, and the measured thermal behavior of these devices. The objective of optimally locating sensors to acquire temperature data is to minimize the number of sensors and determine the optimal locations for capturing the most sensitive information. In this research, the challenge of robust and optimal sensor placement in the presence of uncertain thermal parameters is addressed using the Gramian-based method, facilitating the reconstruction of thermal properties by capturing the most sensitive temperature data. The experimental and simulation results demonstrate the effectiveness of the proposed approach in optimally selecting and placing thermal sensors and accurately determining the thermal parameters of the electromagnetic devices even in the presence of parameter uncertainties.</p>","PeriodicalId":54999,"journal":{"name":"Iet Science Measurement & Technology","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smt2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852815","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}

{"title":"Design and Evaluation of an Integrated Ultra-High Frequency and Optical Sensor for Partial Discharge Detection in GIS","authors":"Feng Chen,&nbsp;Zhiyong Shen,&nbsp;Xing Li,&nbsp;Mao Li,&nbsp;Wenjia Li,&nbsp;Dengwei Ding","doi":"10.1049/smt2.70006","DOIUrl":"https://doi.org/10.1049/smt2.70006","url":null,"abstract":"<p>Partial discharge (PD) detection is an important technique for monitoring and evaluating the insulation condition of gas-insulated switchgear (GIS) equipment. The joint analysis and diagnosis of multiple signals can effectively improve the sensitivity and reliability of PD detection. In this paper, an integrated ultra-high frequency (UHF) and optical sensor is proposed and designed for PD detection. The effectiveness and sensitivity of the designed sensor are experimentally tested. Furthermore, a 500 kV GIS test platform is built, and PD measurements for different types of defects (metal particle on the insulator surface, floating potential, and protrusion) are carried out based on the integrated sensor. The results show that the integrated sensor can detect discharge signals with a minimum apparent charge below 2 pC and has good detection performance for different types of defects. Due to different propagation and attenuation characteristics, there is no strict correspondence between the amplitude of optical and UHF signals. This means that even if the amplitude of the UHF signal is close, the optical signal amplitude may still differ significantly. Compared to UHF signals, the amplitude distribution of optical signals is more dispersed, resulting in differences in the phase-resolved PD pattern characteristics between optical and UHF signals. Moreover, the effectiveness of the optical method is more easily affected by the sensor and defect position compared to the UHF method, and in some cases, the sensitivity of the optical method is lower than that of the UHF method. The results of this study provide a foundation for a reliable and sensitive PD detection technique in the GIS.</p>","PeriodicalId":54999,"journal":{"name":"Iet Science Measurement & Technology","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smt2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836457","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}

{"title":"A Deep Learning Based Prediction of Specific Absorption Rate Hot-Spots Induced by Broadband Electromagnetic Devices","authors":"Shayan Dodge,&nbsp;Nunzia Fontana,&nbsp;Maria Evelina Mognaschi,&nbsp;Eliana Canicattì,&nbsp;Sami Barmada","doi":"10.1049/smt2.70009","DOIUrl":"https://doi.org/10.1049/smt2.70009","url":null,"abstract":"<p>The rapid growth of wearable electromagnetic devices has raised concerns about the potential health effects of electromagnetic fields, particularly due to their interaction with biological tissues. The key parameter for assessing these effects is the specific absorption rate (SAR), which serves as the standard for evaluating energy absorption and associated thermal effects on the human body. However, traditional numerical methods for SAR estimation are computationally expensive, limiting their application to real-time scenarios. This study addresses this limitation by using a deep learning approach to predict the positions of SAR hotspots efficiently and accurately. A convolutional neural network model was developed to predict hotspot locations with minimal computational effort, using tissue distribution and operating frequencies. The dataset includes tissue images augmented with physical properties such as density and permittivity, the latter being frequency dependent, to enhance the model precision. The proposed method demonstrates robust performance of data-driven approaches in predicting SAR hotspots in real time, providing a foundation for safer and more effective deployment of electromagnetic devices, including wearable and medical applications. The source code used in this study is openly available at https://github.com/ShayanDodge/DL-SAR-Hotspots.</p>","PeriodicalId":54999,"journal":{"name":"Iet Science Measurement & Technology","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smt2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818530","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}

{"title":"VisGCL: Visibility Graph Convolutional Learning on Time Series Data for Arc Fault Detection in Low-Voltage Distribution Networks","authors":"Junfeng Yang,&nbsp;Nawaraj Kumar Mahato,&nbsp;Jiaxuan Yang,&nbsp;Gangjun Gong,&nbsp;Li Liu,&nbsp;Ren Qiang,&nbsp;Luyao Wang,&nbsp;Xue Liu","doi":"10.1049/smt2.70007","DOIUrl":"https://doi.org/10.1049/smt2.70007","url":null,"abstract":"<p>Arc faults in low-voltage distribution networks significantly threaten power system safety due to their randomness and concealment. Traditional arc fault detection methods, which rely on time-domain and frequency-domain features, often struggle with accuracy and robustness in variable load environments. To address these challenges, this paper introduces Visibility Graph Convolutional Learning (VisGCL), a novel approach that segments current signals into visibility graphs and employs hierarchical graph convolutional networks for analysis. This method directly learns arc failure modes from the graphical representation of current signals, simplifying the detection process and enhancing both accuracy and robustness. Experimental results demonstrate that the proposed method achieves an accuracy of 98.58 ± 0.14%, with precision, recall, and F1-score reaching 98.05 ± 0.25%, 98.36 ± 0.47%, and 98.16 ± 0.23%, respectively. Extensive experiments validate the effectiveness of VisGCL, confirming its superiority in detecting arc faults under diverse load conditions, and offering a new efficient and reliable solution for arc fault detection in low-voltage distribution networks.</p>","PeriodicalId":54999,"journal":{"name":"Iet Science Measurement & Technology","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smt2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818529","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}

{"title":"Data driven parameter identification of magnetic properties in steel sheets","authors":"Eniz Mušeljić,&nbsp;Alice Reinbacher-Köstinger,&nbsp;Andreas Gschwentner,&nbsp;Manfred Kaltenbacher","doi":"10.1049/smt2.12231","DOIUrl":"https://doi.org/10.1049/smt2.12231","url":null,"abstract":"<p>As simulations play a crucial role for the development of modern electrical machines, it is very important to have good material models used in these simulations. Material models are dependent on certain material parameters which often cannot be measured directly and usually require a lot of computational resources to be determined. This paper investigates the application of neural networks and Gaussian processes for the identification of the magnetic permeability in electrical steel sheets. Through the manufacturing process of such steel sheets, different cutting techniques produce different material behaviour in the vicinity of the cutting edge. Therefore, the method requires the generation of datasets dependent on the degradation profile of the cut steel sheets. This is achieved through simulation and the constructed models can be reused without further simulation runs. This paper also uses an ensemble method to mitigate the issue of measurement noise. For the whole training and testing only simulation data is used as actual measurement data is not yet available.</p>","PeriodicalId":54999,"journal":{"name":"Iet Science Measurement & Technology","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smt2.12231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801707","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}

{"title":"Scheduling Algorithm for Power Wireless Sensor Networks Considering Service Priority and Delay Constraints","authors":"Kaiyun Wen,&nbsp;Hongshan Zhao","doi":"10.1049/smt2.70008","DOIUrl":"https://doi.org/10.1049/smt2.70008","url":null,"abstract":"<p>The power wireless sensor network integrates wireless communication and intelligent sensing technology to monitor the operating status of power equipment in real-time, thereby improving the stability and safety of the power system. However, numerous sensor data flows may lead to network congestion and end-to-end delays. In addition, in the power system, monitoring data has different delay constraints and reliability requirements, and it is necessary to divide the flow into different priorities for transmission. Therefore, this paper proposes a scheduling algorithm for power wireless sensor networks that considers service priority. We construct a power wireless sensor network model that includes network topology, queue length and problem definition. Data flow priority and delay constraints are met by introducing queue weight factors and virtual queues. The Lyapunov optimisation method maximises the throughput of the priority classification-based power wireless sensor network. Moreover, the queue stability of the scheduling algorithm is theoretically proved. The simulation results show that the proposed algorithm can ensure the stability of all queues and strictly meet the priority and delay constraints of various data flows in the network.</p>","PeriodicalId":54999,"journal":{"name":"Iet Science Measurement & Technology","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smt2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801384","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}

{"title":"A Machine-Learning Inspired Field-Based Method for the Optimal Magnetic Design of Leakage Reactance Transformers","authors":"Paolo Di Barba,&nbsp;Maria Evelina Mognaschi,&nbsp;Lukasz Szymanski,&nbsp;Slawomir Wiak","doi":"10.1049/smt2.70011","DOIUrl":"https://doi.org/10.1049/smt2.70011","url":null,"abstract":"<p>A method for the optimal design of special transformers is proposed; it is based on machine learning models, which, in turn, are informed by a sequence of magnetic field analyses. The optimal design of a leakage reactance transformer is considered as the case study. The results show that surrogate models amenable to artificial neural networks (ANNs) are able to approximate the dependence of leakage reactance on winding geometry, eventually reducing the computational burden of automated optimal design problems for this class of transformers. Moreover, the deep learning approach based on a Convolutional neural network (CNN) proved to be able to approximate the field distribution in a given region of the domain, knowing the image of the cross-section of the primary winding.</p>","PeriodicalId":54999,"journal":{"name":"Iet Science Measurement & Technology","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smt2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143787334","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}