IEEE Access最新文献

{"title":"Experimental Implementation of a 3-Level TNPC-IGBT Inverter for Uniform Stress Distribution and THD Mitigation: MPC-Driven Switching Optimization, LCL Filter Integration, and Real-Time Stress Monitoring","authors":"Ahmed H. Okilly;Cheolgyu Kim;Jeihoon Baek","doi":"10.1109/ACCESS.2025.3604037","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3604037","url":null,"abstract":"The increasing demand for high-quality power conversion in industrial applications has led to advancements in multilevel inverter design and control. This paper presents a design and experimental implementation of a 3-level T-type neutral-point clamped (TNPC) inverter utilizing space vector pulse width modulation (SVPWM) and model predictive control (MPC) for optimized switching state selection. The proposed approach ensures DC-link voltage balance, symmetrical load voltage and current, reduced voltage harmonics, and uniform stress distribution among the inverter’s three legs. An LCL filter is integrated based on phase margin optimization criteria to maintain total harmonic distortion (THD) of the current within acceptable limits. Real-time stress monitoring circuits are developed to assess key parameters including on-state voltage, case temperature, and collector current, which are essential for the reliability analysis of the IGBT modules. The configuration is validated through laboratory experimentation and the use of a highly inductive load with currents of up to 100 A. Findings indicate uniform voltage and current distribution, reduced harmonics of less than 0.1% for current and 5% for voltage, under full load conditions, and enhanced dynamic performance and system reliability, making the proposed method suitable for high-quality industrial applications. Furthermore, the developed experimental setup with uniform stress distribution simplifies the TNPC-IGBT module reliability assessment using a one-leg equivalent circuit to estimate the lifespan and conduct reliability analysis, rather than analyzing the module’s three legs.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"152762-152781"},"PeriodicalIF":3.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998124","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":"A Cyber Resilient Framework for V2X Enabled Roundabouts in Intelligent Transportation Systems","authors":"Waseem Abbass;Nasim Abbas;Uzma Majeed;Waqas Nawaz;Qaiser Abbas;Ashfaq Hussain Farooqi","doi":"10.1109/ACCESS.2025.3604095","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3604095","url":null,"abstract":"Vehicle-to-everything (V2X) communication systems are increasingly susceptible to cyber-physical threats that exploit trust assumptions, coordination latency, and semantic inconsistencies across agents. These vulnerabilities, particularly in dense or adversarial environments, undermine the reliability of cooperative perception, anomaly detection, and safety-critical maneuver execution. This paper presents CR-V2XR, a cross-layer, federated, and trust-aware coordination framework designed to enhance resilience in connected and autonomous vehicular networks. CR-V2XR integrates multi-modal anomaly detection with delay-sensitive trust estimation using features extracted from basic safety messages (BSMs), behavioral deviations, entropy shifts, and inter-vehicle trust validation. The architecture employs federated learning for distributed anomaly detection without centralized aggregation and uses a control layer that supports delay-aware trajectory selection. A multi-objective NSGA-III optimizer enables online trade-off adaptation across detection accuracy (DA), collision probability, and communication overhead. Simulations across eleven adversarial scenarios, including Sybil, wormhole, falsification, and replay attacks, demonstrate that CR-V2XR achieves 95% detection accuracy under worst-case attacks, reduces collision probability from 0.61 to 0.27 at 300 vehicles, maintains bounded delay, typically 45–60 ms under nominal load, and remains resilient under high-stress conditions with delays up to 180 ms and communication overhead (<inline-formula> <tex-math>$leq 6.1$ </tex-math></inline-formula> MB/s). Compared to centralized IDS and stateless baselines, CR-V2XR improves detection fidelity, scalability, and robustness under non-IID data and partial synchronization. These results establish CR-V2XR as a viable architecture for delay-constrained, trust-centric coordination in federated V2X environments subject to persistent adversarial threats.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"154775-154802"},"PeriodicalIF":3.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021217","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":"CLEVER: A Novel Approach for Improving EV Charging Duration and Load Predictions Using Curriculum Learning Approach","authors":"Esraa Eldesouky;Ahmed Fathalla;Mahmoud Bekhit;Ahmad Salah","doi":"10.1109/ACCESS.2025.3604342","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3604342","url":null,"abstract":"Despite advancements in electric vehicle (EV) charging prediction models, existing approaches are suffering from complex charging patterns. The curriculum learning (CL) is a training approach which resembles the natural human learning progression by introducing training samples through different patterns, hence efficiently structuring the learning process. While the CL has been successfully used in other domains, its application in EV charging prediction remains unexploited. In this work, the CL is to be leveraged for the first time to improve the EV charging behavior predictions in both EV charging duration and charging load prediction. A feature-based curriculum learning approach, named CLEVER (Curriculum Learning EV chargER), is proposed for predicting charging session load and duration. CLEVER employs an advanced data stratification mechanism that introduces training samples progressively according to complexity metrics computed from temperature variations, state of charge variations, and temporal patterns. The CLEVER method integrates a CL strategy with a staged schedule mechanism over four neural network architectures: ANN, DNN, LSTM, and GRU. The performances obtained exhibit notable gains, where CL scores 20.9% reduction of Mean Absolute Error for GRU-based forecasting of EV charging duration and 2.2% improvement for DNN-based charging load forecasting. The CLEVER methodology shows considerable improvements in predicting the duration of EV charging, with, as many as 23.0% reductions in Mean Absolute Error with GRU models on Level 1 chargers and a near 20.7% improvement with DNN models on DC Fast Chargers. For EV charging load forecasts, curriculum learning produces consistent, but modest, gains, with improved up to 2.4% with ANN models on DC Fast Chargers and 1.6-2.1% improvements across different neural network architectures. This comprehensive analysis across different charger types, user groups, vehicle models, temperatures, and temporal patterns makes CL a superior approach to enhancing EV charging infrastructure management and grid stability.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"153263-153280"},"PeriodicalIF":3.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11144765","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998338","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":"A Modified Min-Max Method With Adaptive Distance Adjustment for RSSI-Based Indoor Localization","authors":"Apidet Booranawong;Naruesorn Prakobboon;Hiroshi Saito","doi":"10.1109/ACCESS.2025.3603986","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3603986","url":null,"abstract":"In range-based localization systems based on received signal strength indicator (RSSI), position estimates are determined by measuring RSSI levels between all reference nodes and an unknown target. The RSSI level indicates the distance between the target and references. Because the RSSI signal is time-varying and fluctuates due to multipath effects, particularly in indoor contexts, this variation can cause distance calculation and localization inaccuracies. Inadequate estimation findings can lead to poor judgments throughout the system. In this paper, we present a modified min-max method to reduce RSSI-to-distance error and to improve localization precision. The novelty of this study is that an autonomous reference node identification, area separation, area selection, and adaptive distance adjustment solution are proposed and integrated with the traditional min-max method. The distances between the target and references are automatically measured and compensated. Experiments in real-world scenarios using 2.4 GHz ZigBee/IEEE 802.15.4 wireless networks have been conducted in different indoor environments, including an office room, an electrical machine laboratory, and a second-floor walking corridor. Experimental results show that the proposed method has estimation errors lower than the traditional min-max method: 0.460 m (proposed) and 0.715 m (traditional) for the office room, 0.735 m and 1.503 m for the machine laboratory, and 0.661 m and 1.340 m for the corridor. The proposed method significantly outperforms the min-max method by 35.623%, 51.063%, and 50.627%, respectively. For in mobile target scenarios, the proposed method also provides a more estimated precision of tracking results. Finally, the computational cost analysis of the proposed method in terms of mathematical operations is discussed. The worst-case scenario and the results obtained from the experiments are also reported.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"152010-152032"},"PeriodicalIF":3.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11144764","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998032","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":"Multilingual Relation Extraction: A Survey","authors":"Manzoor Ali;René Speck;Hamada M. Zahera;Muhammad Saleem;Diego Moussallem;Axel-Cyrille Ngonga Ngomo","doi":"10.1109/ACCESS.2025.3604258","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3604258","url":null,"abstract":"Relation extraction plays a fundamental role in applications of various research fields such as knowledge graph construction, event extraction, and question answering over knowledge graphs, as they often rely on extracting relationships between named entities. Relation extraction has been extensively studied in high-resource languages like English. However, there remains a significant gap in supporting languages with limited resources, defined as those lacking comprehensive annotated corpora, linguistic tools, or pre-trained models, limiting the completeness and accuracy of applications that rely on multilingual data. This paper provides a comprehensive survey of recent advances in relation extraction, focusing on multilingual approaches. We systematically review state-of-the-art methods, datasets used for evaluation, and key features leveraged in these approaches. Additionally, we perform a detailed comparative analysis of the surveyed methods, examining their methodologies, target domains, levels of extraction, explored languages, and effectiveness. Finally, we identify promising directions for future research, with an emphasis on enhancing multilingual relation extraction.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"151907-151933"},"PeriodicalIF":3.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998308","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":"Spatio-Temporal Graph Spectral Network for Personalized Itinerary Recommendation","authors":"Teng Wang;Rui Cheng;Yiheng Wang","doi":"10.1109/ACCESS.2025.3604019","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3604019","url":null,"abstract":"The goal of personalized itinerary recommendation is to generate travel routes that closely match each user’s unique preferences and spatiotemporal constraints. This task, however, is complicated by the prevalence of incidental and random “noise” interactions embedded within users’ behavioral histories. Most existing methods process these noisy records directly in the node domain, struggling to reliably separate stable interests from fleeting actions. To address this fundamental challenge, we propose a novel Spatio-Temporal Graph Spectral Network (ST-GSN). Rather than analyzing user behaviors solely in the node space, our approach shifts the perspective into the graph spectral domain. Specifically, we construct for each user a dynamic graph enriched with spatial, temporal, and semantic information, then project their behavioral signals into the spectral domain via the Graph Fourier Transform (GFT). We hypothesize that stable user preferences manifest as low-frequency, energy-concentrated signals, while noise emerges as high-frequency components. Leveraging this property, we design a learnable adaptive filter that precisely isolates and suppresses noise in the spectral space, enabling the extraction of a user’s core intent. The model further incorporates Time2Vec for fine-grained modeling of dwell and travel times, and employs a multi-task learning framework to enhance the robustness of its representations. Extensive experiments on the public Foursquare and Gowalla datasets show that ST-GSN consistently outperforms a suite of strong baselines across all key metrics. Most notably, in the full-corpus ranking scenario that best simulates real-world deployment, the advantage of ST-GSN becomes even more pronounced, demonstrating outstanding performance and resilience in the face of complex, noisy environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"152479-152492"},"PeriodicalIF":3.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005460","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":"Modular Lower Limb 3D-Printed Orthoprosthesis for Toddlers With Fibular Hemimelia","authors":"Lilli Anders;Paula Agulheiro;Hugo Plácido Da Silva;Cláudia Quaresma","doi":"10.1109/ACCESS.2025.3604278","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3604278","url":null,"abstract":"Toddlers with congenital limb deficiencies, such as fibular hemimelia, face major barriers to independent mobility during a critical window of motor development. Despite the need for early intervention, existing orthoprosthetic solutions are often inaccessible and poorly suited to the anatomical and functional needs of children aged 12–36 months. This study presents a novel, user-centered approach to a modular pediatric lower limb orthoprosthesis dubbed +Limb developed in close collaboration with caregivers, physicians, and therapists. Using additive manufacturing, successive prototypes were rapidly produced and tested in a clinical setting. The final device met key requirements for modularity, light weight, postural support, and adjustability. Functional evaluations demonstrated significant improvements, including correction of a 4.5 cm leg length discrepancy, reduction of pelvic tilt from 15° to 0°, and initiation of unassisted gait with controlled knee flexion up to 45°. To study how the body and muscles responded, different sensors were used, including pressure sensors (FSRs), muscle activity sensors (EMG), and a pressure plate for foot contact analysis. This case study demonstrates the feasibility of a low-cost, customizable orthoprosthesis for toddlers with fibular hemimelia and emphasizes the advantages of combining additive manufacturing and sensor-based feedback to support early-stage rehabilitation in pediatric populations.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"155124-155140"},"PeriodicalIF":3.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021231","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":"A Bilevel Optimization Framework for Hybrid DSO-VPP Coordination: Enhancing Congestion Management and Market Efficiency in Decentralized Energy Systems","authors":"Ilham Ramadhan Maulana;Rehman Zafar;Il-Yop Chung","doi":"10.1109/ACCESS.2025.3604172","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3604172","url":null,"abstract":"This paper presents a bilevel optimization framework that improves congestion management and economic efficiency in decentralized energy markets by coordinating hybrid Distribution System Operators (DSOs) and Virtual Power Plants (VPPs). The framework utilizes Distributed Locational Marginal Pricing (DLMP) to align technical constraints with market incentives and addresses challenges arising from high-penetration Distributed Energy Resources (DERs). Simulations on the modified KEPCO Active Distribution Test System (KADTS) show performance improvements compared to conventional methods, including profit increases of up to 3.35%, effective congestion mitigation, and enhanced operational flexibility enabled by adaptive DLMP mechanisms and strategic bidding. The framework is scalable, adaptable, and compatible with existing market structures. It offers a practical approach to modern energy systems and supports ongoing developments in smart grids and sustainable grid modernization.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"153281-153295"},"PeriodicalIF":3.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998158","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":"A Predictive Stacking Framework for Energy Output in Photovoltaic System With Uncertainty Estimates","authors":"Imad Hassan;Ibrahim Alhamrouni;Nurul Hanis Azhan;Saad Mekhilef;Mehdi Seyedmahmoudian;Saad Ijaz Majid;Alex Stojcevski","doi":"10.1109/ACCESS.2025.3604038","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3604038","url":null,"abstract":"The integration of grid-connected photovoltaic (PV) systems has transformed the global energy landscape by offering sustainable and efficient alternatives to conventional energy sources. However, the inherent variability and intermittency of solar energy pose significant challenges for grid operators, complicating reliable energy forecasting and management. Consequently, it is imperative to develop a predictive model that not only delivers accurate forecasts but also incorporates uncertainty analysis to enhance the reliability and decision-making processes. To address these issues, this study proposes a new stacking ensemble model with uncertainty analysis to accurately forecast the energy output in grid-connected PV systems. The model leverages an Extreme Learning Machine (ELM) and Convolutional Neural Network (CNN) as base learners, with a Bayesian Ridge regression model serving as a meta-learner to refine and aggregate the predictions. The ELM regressor was enhanced through bootstrapping, whereas the CNN employed a Monte Carlo Dropout to estimate the prediction uncertainty. The model’s incorporation of uncertainty analysis allows for the computation of the standard deviation and 99% confidence intervals for the predictions. Feature engineering using Shapley additive explanation (SHAP) analysis was employed to enhance predictive accuracy. The performance of the model was simulated using real-time meteorological data from Dammam, Saudi Arabia, for Amorphous Silicon (a-Si) modules. The results showed R2 values of 0.9999 (train), 0.9997 (test), and 0.9997 (forecast), with corresponding MAE values of 4.29, 7.75, and 6.71 W, respectively, indicating high accuracy for both seen and unseen data. The uncertainty analysis achieved a standard deviation of 7.77 W, PICP of 91%, and PIW of 46.56 W, demonstrating the model’s strong probabilistic reliability, narrow prediction intervals, and effective coverage of the true output values. The proposed model offers a robust and reliable framework for forecasting the energy output of grid-connected PV systems, supporting solar integration, and reducing greenhouse gas emissions.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"154046-154068"},"PeriodicalIF":3.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021216","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":"Estimation of 3D Ionospheric Electron-Density Distribution Specialized for the Korean Peninsula Using a U-Net Super-Resolution CNN","authors":"Do Hyeon Lee;Junmo Yang;Hyosang Moon;Jaehoon Jung;Myungsik Lee;Yong Bae Park","doi":"10.1109/ACCESS.2025.3603785","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3603785","url":null,"abstract":"Accurate knowledge of the three-dimensional ionospheric electron-density distribution is essential for reliable radio-wave propagation modeling, yet global empirical models (e.g., IRI-2020, NeQuick2) fail to capture local and short-term variability. In this work, we propose a U-Net–based super-resolution CNN (SRCNN) that reconstructs a regionally specialized 3D electron-density distribution over the Korean Peninsula from sparse, high-fidelity input profiles. These input profiles are generated for two sites (Icheon and Jeju) by combining direct ionosonde measurements for the bottomside with an ionosonde-corrected IRI-2020 model for the topside. The AI model was trained on electron-density distributions produced by the IRI-2020 model. The proposed model demonstrates significant improvements over the standard IRI model, showcasing its stability across all solar activity levels. Most notably, under solar-maximum conditions, the root mean square relative error (RMSRE) was drastically reduced at Icheon (from 367.23% to 16.04%) and Jeju (from 538.12% to 9.68%). The model also consistently improved other key metrics, such as the F2-peak altitude error and the Pearson correlation coefficient (<inline-formula> <tex-math>$r gt 0.99$ </tex-math></inline-formula>), proving its robust performance. The proposed approach can contribute to improving ionospheric error correction and signal quality in precise GNSS positioning, space surveillance radar, and satellite communication systems.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"153199-153211"},"PeriodicalIF":3.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11143239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998291","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}