IEEE Open Journal of the Industrial Electronics Society最新文献_第8页

Cost-Effective and High-Power-Density Nonisolated Bidirectional On-Board Charger 具有成本效益和高功率密度的非隔离双向车载充电器

IF 4.6

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-07-02 DOI: 10.1109/OJIES.2026.3709792

Héctor Sarnago;Óscar Lucía

{"title":"Cost-Effective and High-Power-Density Nonisolated Bidirectional On-Board Charger","authors":"Héctor Sarnago;Óscar Lucía","doi":"10.1109/OJIES.2026.3709792","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3709792","url":null,"abstract":"This article presents a nonisolated, single-stage, bidirectional on-board charger (OBC) for electric vehicles based on a differential Y-rectifier with one phase-inductor per phase, enabling seamless operation on single- and three-phase grids. A straightforward, unchanged modulation, and current-control strategy is retained across 1ϕ/3ϕ operation, yielding mode-invariant device stresses and efficiency, and supporting equal rated power in both configurations. The topology features a markedly reduced component count and a common reference coincident with the high voltage (HV) battery, simplifying gate-drive and sensing circuitry, and lowering isolation requirements in the control path. In addition, the converter integrates a buck-type power pulsation buffer (PPB) that compensates the 2·<italic>fac ripple otherwise imposed on the battery, thereby allowing a compact energy buffer and high power density. Grid-compliant operation is ensured under Ubatt>Ûac (practically ∼400 V), targeting modern 800 V battery platforms. The proposed converter is tested by means of a hardware prototype rated at 11 kW that operates over 85–256 Vrms, 40–65 Hz, with battery voltages ≥ 400 V (tested up to 850 V). The design achieves 10.2 kW/L power density and peak efficiencies > 97%, with PF ≥ 0.99 and THDi ≤ 5%. Experimental results validate operation in both grid configurations and confirm invariant device rms/peak stresses, identical control, and equivalent efficiency between 1ϕ and 3ϕ operation, establishing a cost-effective, high-power-density alternative to two-stage OBCs.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"1126-1137"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11594041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148489374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Closed-Loop Control of Modular Multiactive Bridge Converter 模块化多有源桥式变换器的闭环控制

IF 4.6

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-03-30 DOI: 10.1109/OJIES.2026.3678741

Leonardo Ortega;Silvana Gamboa;Cristina Fernández;Pablo Zumel

引用次数: 0

Real-Time Safe Production Scheduling via Causal Inference and Hybrid-Event Control 基于因果推理和混合事件控制的实时安全生产调度

IF 4.3

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-04-20 DOI: 10.1109/OJIES.2026.3685707

Yong Lin;Yadi Lin;Wendi Lin

{"title":"Real-Time Safe Production Scheduling via Causal Inference and Hybrid-Event Control","authors":"Yong Lin;Yadi Lin;Wendi Lin","doi":"10.1109/OJIES.2026.3685707","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3685707","url":null,"abstract":"This article proposes a real-time and certifiably safe scheduling framework for cyber-physical manufacturing systems, designed to operate under hybrid discrete–continuous dynamics and deployment-level latency constraints. The approach integrates two core components. First, a <italic>wavelet-invariant causal modeling module that uncovers multiscale dependencies in sensor data while preserving conditional-independence structure under orthonormal wavelet projections, and second, a <italic>hybrid-event barrier certificate (HRCBC) filter that enforces hard safety constraints across both continuous flows and discrete event triggers, such as tool changeovers and emergency halts. Departing from traditional methods that assume smooth dynamics or fixed planning horizons, the architecture fuses offline causal discovery with fast, online, safety-filtered control. Causal graphs are learned via orthonormal wavelet projections and refreshed per shift or upon concept-drift detection. Real-time safety is maintained by solving a convex quadratic program with warm starts and p99 latency under 10 ms. The method is validated on two industrial lines (Site-A: EV-module assembly; Site-B: appliance assembly) and on public synthetic benchmarks (SynTwin-Bench), with ablations and multisite simulations. Across the reported testbeds, wavelet-invariant structural transformation+HRCBC improves cycle time by up to 28% and operating cost by 20% relative to the incumbent dispatcher; throughout the reported evaluation horizon, no safety violations were recorded by the independent programmable logic controller (PLC) safety layer, while maintaining PLC-compatible latency. The system is deployable on standard industrial gateways and interoperable with the manufacturing execution system (MES) and PLC via RESTful and open platform communications unified architecture (OPC UA) adapters.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"811-824"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11488495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-Phase to Three-Phase Unified Power Quality Conditioners Applied to Electrical Distribution Grids in Remote Areas 单相到三相统一电能质量调节器在偏远地区配电网中的应用

IF 4.3

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-03-23 DOI: 10.1109/OJIES.2026.3676869

Calebe Hermann de Oliveira Lima;Gustavo Azevedo Teixeira;Fabrício Bradaschia;Leonardo Rodrigues Limongi;Rafael Cavalcanti Neto;Gustavo Medeiros de Souza Azevedo

{"title":"Single-Phase to Three-Phase Unified Power Quality Conditioners Applied to Electrical Distribution Grids in Remote Areas","authors":"Calebe Hermann de Oliveira Lima;Gustavo Azevedo Teixeira;Fabrício Bradaschia;Leonardo Rodrigues Limongi;Rafael Cavalcanti Neto;Gustavo Medeiros de Souza Azevedo","doi":"10.1109/OJIES.2026.3676869","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3676869","url":null,"abstract":"In remote areas, electrical distribution grids are typically single-phase due to infrastructure and cost limitations, which makes the direct supply of three-phase loads unfeasible. Conventional solutions generally rely on transformers or full power processing converters, leading to increased system size, cost, and power losses. This article proposes two transformerless <inline-formula><tex-math>$1phi 3phi$</tex-math></inline-formula> Unified Power Quality Conditioner topologies capable of supplying three-phase loads from a single-phase distribution system while providing power quality enhancement. The proposed topologies also enable the connection of four-wire loads, allowing the supply of loads requiring a neutral conductor. By operating under a partial power processing principle, only a fraction of the total load power is actively processed by the power electronic stages, contributing to reduced component ratings and improved system-level efficiency. The operating principle, mathematical modeling and the associated control diagram of each topology are discussed. Simulation results at multi-kW power levels are used to assess scalability, while experimental results obtained from a laboratory prototype demonstrate the effectiveness of the proposed solutions under steady-state operation as well as during voltage disturbances and transient conditions. In addition, efficiency and partial power processing analyses are presented.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"689-711"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11454544","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flexible Power Extraction in Photovoltaic Energy Systems Using Voltage Window Search Algorithm Under Partial Shading Conditions 部分遮阳条件下基于电压窗搜索算法的光伏能源系统柔性电力提取

IF 4.6

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-09-28 DOI: 10.1109/OJIES.2026.3738023

Sajib Ahmed;Saad Mekhilef;Yam P. Siwakoti

{"title":"Flexible Power Extraction in Photovoltaic Energy Systems Using Voltage Window Search Algorithm Under Partial Shading Conditions","authors":"Sajib Ahmed;Saad Mekhilef;Yam P. Siwakoti","doi":"10.1109/OJIES.2026.3738023","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3738023","url":null,"abstract":"Flexible power point tracking (FPPT) is emerging alongside maximum power point tracking (MPPT) to adjust power output based on grid demand rather than solely maximizing it. This article presents an FPPT algorithm that operates effectively under partial shading conditions. By modifying the voltage window search MPPT algorithm and incorporating additional functionalities, the proposed approach operates on the left side of the power–voltage curve, offering improved robustness. A DC–DC buck–boost converter and a proportional–integral controller are implemented to achieve optimal performance. To ensure steady operation near the target power point, an oscillation detection mechanism is integrated, which stabilizes the power output once the flexible power point is reached and prevents unnecessary fluctuations. The proposed approach is validated on four irradiance patterns, one uniform and three partially shaded, under three distinct FPPT power criteria. The effectiveness of the algorithm is confirmed by experimental results, achieving the desired power output with an average tracking time of 1.54 s, an average power tracking variance of 0.224%, and validated across twelve experimental scenarios (four patterns under three FPPT power criteria), together with a loss analysis of the converter stage and an assessment of the computational cost of the algorithm, demonstrating its potential for grid-integrated photovoltaic applications.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"1584-1597"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11711447","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cyber Attacks Detection, Prevention, and Source Localization in Digital Substation Communication Using Hybrid Statistical-Deep Learning 基于混合统计-深度学习的数字变电站通信网络攻击检测、预防和源定位

IF 4.6

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-10-05 DOI: 10.1109/OJIES.2026.3740198

Nicola Cibin;Bas Mulder;Herman Carstens;Junho Hong;Alexandru Ştefanov

{"title":"Cyber Attacks Detection, Prevention, and Source Localization in Digital Substation Communication Using Hybrid Statistical-Deep Learning","authors":"Nicola Cibin;Bas Mulder;Herman Carstens;Junho Hong;Alexandru Ştefanov","doi":"10.1109/OJIES.2026.3740198","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3740198","url":null,"abstract":"The digital transformation of power systems is accelerating the adoption of IEC 61850 standard. However, its communication protocols lack built-in authentication and encryption, leaving them vulnerable to Man-in-the-Middle (MitM) and malicious frame injection attacks that can disrupt protection schemes operation. While most existing research focuses on detecting cyber attacks in digital substations, intrusion prevention systems have been largely overlooked due to concerns about potential network disruptions. To address this gap, this article proposes an integrated hybrid statistical-deep learning method for detecting, preventing, and localizing IEC 61850 sampled values (SVs)-based cyber attacks. The method models SV frames arrival times using exponentially modified Gaussian distributions and prevents malicious frames from reaching the targeted intelligent electronic devices (IEDs). Malicious frames are dropped with minimal processing overhead and latency, while the method remains robust to network latency, jitter, and time-synchronization issues, and ensures a near-zero false positive rate under nonattack conditions. Long short-term memory and Elman recurrent neural networks are used to identify anomalous variations in the estimated probability distributions across IEDs for detecting and localizing MitM attacks on SV streams. The proposed method is validated across three testbeds comprising industrial-grade communication and protection devices, hardware-in-the-loop simulations, virtualized IEDs and merging units, and high-fidelity emulated networks. Results demonstrate the method’s practicality and effectiveness for deployment in IEC 61850-compliant digital substations.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"1622-1637"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11719912","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transferable Electric Vehicle Battery Capacity Estimation From Real-World Charging Data Using Spectral Learning 基于频谱学习的可转移电动汽车充电数据容量估计

IF 4.3

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-04-28 DOI: 10.1109/OJIES.2026.3688098

Andrei Zakharov;Vasily Volovich;Ilya Makarov

{"title":"Transferable Electric Vehicle Battery Capacity Estimation From Real-World Charging Data Using Spectral Learning","authors":"Andrei Zakharov;Vasily Volovich;Ilya Makarov","doi":"10.1109/OJIES.2026.3688098","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3688098","url":null,"abstract":"Reliable estimation of electric vehicle battery capacity is critical for range prediction, maintenance planning, and second-life reuse. While vehicle manufacturers employ proprietary algorithms within the battery management system, these methods are closed, model-specific, and inaccessible to fleet operators, charging networks, and regulators. This gap creates a clear need for transferable, data-driven approaches that use universally available signals, such as charging traces to deliver transparent, fleet-scale capacity estimates. We present a spectral learning framework based on Fourier neural operators that map per-cycle multivariate charging data to capacity estimates. The model incorporates both per-cycle predictions and sequence-level constraints to ensure smoothness and temporal consistency. Evaluated on a real-world dataset comprising 83 EVs and over 15 800 charging cycles, the method achieves a relative root mean square error (RMSE) of 1.9%, reducing error by nearly 40% compared to strong baselines. Importantly, we show that proposed approach generalizes across vehicle manufacturers via light fine-tuning with state-of-the-art accuracy recovered on independent subsets. These results demonstrate that frequency-domain operator learning provides a compact, transferable, and interpretable tool for reliable battery health estimation beyond proprietary systems, enabling fleet-scale monitoring and supporting the wider adoption of electric mobility.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"892-904"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11495199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 Survey of Control and Energy Management in Hydrogen Production Systems 制氢系统控制与能源管理综述

IF 4.3

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-02-19 DOI: 10.1109/OJIES.2026.3666464

Shadi Khodakaramzadeh;Tayebeh Faghihi;Qobad Shafiee;Pavol Bauer;Hani Vahedi

{"title":"A Survey of Control and Energy Management in Hydrogen Production Systems","authors":"Shadi Khodakaramzadeh;Tayebeh Faghihi;Qobad Shafiee;Pavol Bauer;Hani Vahedi","doi":"10.1109/OJIES.2026.3666464","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3666464","url":null,"abstract":"This article provides a comprehensive review of power electronics converter control and energy management for hydrogen production systems through water electrolysis. Hydrogen production from renewable energy sources is a key pathway toward decarbonizing energy systems and enabling large-scale energy storage. Efficient and dependable operation necessitates addressing the dynamic properties of electrolyzers, the intermittent nature of renewable sources, and the coordination among numerous power electronic interfaces. Unlike earlier studies that addressed these aspects separately, this review systematically connects electrolyzer modeling, converter design, control architectures, and energy management to reveal their critical interdependence. By examining these connections, the analysis reveals critical research gaps in real-time coordination, parameter adaptation, and scalable architectures, outlining pathways toward intelligent and grid-independent hydrogen production systems. This review integrates electrolyzer modeling, power converter control algorithms, AC and DC energy hub architectures, hierarchical control schemes, and energy management systems from classical to advanced methods.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"650-670"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11399925","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 Arrowhead Framework Ontology 箭头框架本体

IF 4.3

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-03-13 DOI: 10.1109/OJIES.2026.3693084

Oskar Wintercorn;Jan van Deventer

{"title":"The Arrowhead Framework Ontology","authors":"Oskar Wintercorn;Jan van Deventer","doi":"10.1109/OJIES.2026.3693084","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3693084","url":null,"abstract":"Modern industrial applications are increasingly built from independently developed systems that discover each other at run time and compose into mission-specific behavior. The challenge is not only to collect data, but to preserve enough context to understand and diagnose the behavior of the resulting system-of-systems. This article presents the arrowhead framework ontology, an explicit, queryable semantic representation of Arrowhead framework deployments. Using a climate-control demonstrator, we export timestamped resource description framework (RDF) snapshots from live systems, ingest them into GraphDB, validate them with shapes constraint language, and apply web ontology language reasoning to derive additional facts about structure and dependencies. We show that this enables practical diagnosis and impact analysis via SPARQL protocol and RDF query language, even though none of the individual systems were designed to answer such questions. The same representation also supports digital threads for traceability and lifecycle insight. Together, these results illustrate how explicit semantics can provide a nonintrusive path to more transparent and evolvable industrial systems.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"920-935"},"PeriodicalIF":4.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11517481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 Multimodal Attention and Hybrid Stacking Framework for Robust Wind Turbine Fault Diagnosis 风电机组鲁棒故障诊断的多模态关注混合叠加框架

IF 4.6

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2026-01-01 Epub Date: 2026-06-01 DOI: 10.1109/OJIES.2026.3699032

Nawel Said;Majdi Mansouri;Atef Khedher;Lazhar Kheriji;Mahamadou Abdou Tankari

{"title":"A Multimodal Attention and Hybrid Stacking Framework for Robust Wind Turbine Fault Diagnosis","authors":"Nawel Said;Majdi Mansouri;Atef Khedher;Lazhar Kheriji;Mahamadou Abdou Tankari","doi":"10.1109/OJIES.2026.3699032","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3699032","url":null,"abstract":"Reliable fault diagnosis in wind turbines is essential for ensuring operational efficiency and preventing costly downtime. This article proposes a novel multimodal attention and stacking framework that integrates domain-driven feature extraction (DDFE), global fusion integration (GFI), and improved attention optimization with hybrid stacking for intelligent fault classification. The framework first extracts discriminative features from mechanical, electrical, and environmental signals using DDFE. These features are then globally fused via GFI to capture intra- and intermodal correlations, followed by an attention-based hybrid stacking ensemble that dynamically weights modalities and combines predictions from multiple classifiers. Extensive experiments on simulated and real-world datasets demonstrate that the framework significantly outperforms conventional single-modality and early-fusion approaches, achieving up to 99.98% accuracy while maintaining robustness under varying operational conditions. The results highlight the advantages of combining physically interpretable features with data-driven multimodal learning for highly reliable wind turbine fault diagnosis.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"977-990"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11543202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0