IEEE Access最新文献

{"title":"Dual-Channel Dynamic Gated Spatio-Temporal Graph for Traffic Flow Forecasting","authors":"Chao Wang;Jun-Feng Hao;He Huang;Wang Zou;Xia Sun;Ting Peng","doi":"10.1109/ACCESS.2025.3553535","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553535","url":null,"abstract":"Traffic flow forecasting is a critical and essential technology in the field of Intelligent Transportation Systems (ITS), as it plays a pivotal role in optimizing traffic management, improving road safety, and enhancing the overall efficiency of transportation networks. However, current research neglects the relationships between the local and global traffic flow data. Additionally, the predefined static graph structure fails to adequately capture the dynamic spatial features of traffic flow. To address the these challenges, this paper proposes a Dual-Channel Dynamic Gated Spatio-Temporal graph network (DC-DGST) for traffic flow prediction. We consider hourly slices as the local feature and daily slices to be the global feature of traffic flow. The DC-DGST framework employs a dual-channel structure to capture spatiotemporal dependencies between global and local features. It transforms the predefined static graph into a dynamic graph, enabling the establishment of connections between input data and historical information. Furthermore, we design gated spatio-temporal blocks based on residual structures within the spatio-temporal module. Specifically, we utilize Graph Gated Neural Networks (GGNNs) to learn and integrate both static and dynamic graphs, while Transformer encoders are used to capture long-range dependencies in the temporal sequence. We conducted a series of experiments on four publicly available benchmark datasets: PEMS03, PEMS04, PEMS07, and PEMS08. The results demonstrate that our model significantly outperforms baseline models. Moreover, the dual-channel structure effectively captures the correlation between local and global traffic flow features, while the dynamic graph enhances the model’s overall performance.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"52995-53006"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726263","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":"FetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US","authors":"Musa Turkan;Emre Dandil;Furkan Erturk Urfali;Mehmet Korkmaz","doi":"10.1109/ACCESS.2025.3553548","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553548","url":null,"abstract":"Automated classification of fetal movements in ultrasound (US) videos is critical for assessing fetal well-being and detecting potential complications during pregnancy. This study introduces FetalMovNet, a novel deep learning model that incorporates an attention mechanism to improve the classification of fetal movement in US video sequences. The model integrates convolutional neural networks (CNN) for feature extraction and an attention mechanism to capture spatio-temporal patterns, significantly improving classification performance of fetal movements. To evaluate FetalMovNet, we construct a new dataset containing fetal movements in US across seven different anatomical structures-head, body, arm, hand, heart, leg, and foot. Experimental results show that FetalMovNet achieves an accuracy of 0.9887, precision of 0.9871, recall of 0.9910, and an F1-score of 0.9891, outperforming state-of-the-art CNN and CNN-LSTM architectures. Ablation studies confirm the effectiveness of the attention mechanism, with FetalMovNet achieving an area under curve (AUC) score of 0.9957, compared to 0.9471 for CNN and 0.9543 for CNN-LSTM. The proposed FetalMovNet model provides a robust and clinically applicable tool for real-time fetal movement monitoring, reducing the need for manual assessment and improving prenatal care.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"52508-52527"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726422","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":"Techno-Economic and Environmental Analysis of Solar PV System at Sher-e-Bangla National Cricket Stadium: A Comprehensive Case Study","authors":"Mohammad Ariful Islam Rafi;Md Sajid Hasan;Imam-Ur-Rashid;Md Manzurul Hasan;Jawadul Alam Chowdhury;Moshiur Rahman Sohan;Nahid A. Jahan;M. Mofazzal Hossain","doi":"10.1109/ACCESS.2025.3553636","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553636","url":null,"abstract":"The proposed rooftop solar photovoltaic (PV) system at the Sher-e-Bangla National Cricket Stadium (SBNCS) demonstrates a sustainable energy solution addressing Bangladesh’s energy challenges. The system has a capacity of 83.2 kWp and is estimated to generate 129.5 MWh of energy annually. This deployment reduces reliance on fossil fuels and contributes to global Sustainable Development Goal 7 (SDG7). The performance evaluation reveals a Performance Ratio (PR) of 79.4%, ensuring efficient solar resource utilization. Economically, the project involves a total investment of <inline-formula> <tex-math>${$}$ </tex-math></inline-formula>101,031, achieving annual energy cost savings of <inline-formula> <tex-math>${$}$ </tex-math></inline-formula>5,370. Financial feasibility metrics include a Net Present Value (NPV) of <inline-formula> <tex-math>${$}$ </tex-math></inline-formula>99,131.5, an Internal Rate of Return (IRR) of 6%, and a Payback Period (PBP) of 13 years. Furthermore, the system reduces 50 tons of CO2 emissions annually, resulting in a Social Cost of Carbon (SCC) savings of <inline-formula> <tex-math>${$}$ </tex-math></inline-formula>77,064 over its 25-year lifespan. The project’s Levelized Cost of Energy (LCOE) is calculated as <inline-formula> <tex-math>${$}$ </tex-math></inline-formula>0.03/kWh, reflecting its long-term cost-effectiveness. This analysis highlights the economic, environmental, and performance benefits of implementing a rooftop solar PV system at SBNCS, offering a scalable model for integrating renewable energy into and other stadiums and large infrastructure. This study can aid in the integration of renewable energy into the grid and assist policymakers in facilitating the future energy storage systems and expanding grid-tied operations for enhanced sustainability.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"52658-52682"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726376","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":"QRS-Trs: Style Transfer-Based Image-to-Image Translation for Carbon Stock Estimation in Quantitative Remote Sensing","authors":"Zhenyu Yu;Jinnian Wang;Hanqing Chen;Mohd Yamani Idna Idris","doi":"10.1109/ACCESS.2025.3554045","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554045","url":null,"abstract":"Forests serve as vital carbon reservoirs, reducing atmospheric CO2 and mitigating climate change. Monitoring carbon stocks typically combines ground-based data with satellite remote sensing, yet accuracy remains a challenge. This study analyzes Huize County, China, using GF-1 WFV and Landsat TM images and introduces the Quantitative Remote Sensing Transformer (QRS-Trs), which leverages style transfer and attention mechanisms to enhance carbon stock estimation as an image-to-image translation task. QRS-Trs demonstrates three advantages: 1) Swin-Pix2Pix effectively reduces inter-domain discrepancies caused by sensor and lighting variations while excelling in de-clouding, outperforming Pix2Pix. 2) It incorporates a median filter to eliminate anomalies and a mask module to exclude non-target areas, achieving MAE =16.29 Mg/ha, RMSE =29.38 Mg/ha, <inline-formula> <tex-math>$R^{2} =0.71$ </tex-math></inline-formula>, and SSIM =0.75. 3) Applied to multi-year data, from 2005 to 2020, 44.04% of the area showed increased carbon stock, 10.22% decreased, and 45.74% remained unchanged. While QRS-Trs performs well, its generalization to diverse ecological conditions depends on high-quality training data. Nevertheless, this study provides a robust approach for high-resolution carbon stock estimation, contributing to improved forest carbon sink management.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"52726-52737"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning With Adaptive Quantization and Differential Privacy","authors":"Emre Ardıç;Yakup Genç","doi":"10.1109/ACCESS.2025.3554138","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554138","url":null,"abstract":"Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing underlying data. One of the key challenges of FL is the communication bottleneck caused by variations in connection speed and bandwidth across devices. Therefore, it is essential to reduce the size of transmitted data during training. Additionally, there is a potential risk of exposing sensitive information through the model or gradient analysis during training. To address both privacy and communication efficiency, we combine differential privacy (DP) and adaptive quantization methods. We use Laplacian-based DP to preserve privacy, which is relatively underexplored in FL and offers tighter privacy guarantees than Gaussian-based DP. We propose a simple and efficient global bit-length scheduler using round-based cosine annealing, along with a client-based scheduler that dynamically adapts based on client contribution estimated through dataset entropy analysis. We evaluate our approach through extensive experiments on CIFAR10, MNIST, and medical imaging datasets, using non-IID data distributions across varying client counts, bit-length schedulers, and privacy budgets. The results show that our adaptive quantization methods reduce total communicated data by up to 52.64% for MNIST, 45.06% for CIFAR10, and 31% to 37% for medical imaging datasets compared to 32-bit float training while maintaining competitive model accuracy and ensuring robust privacy through DP.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54322-54337"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937694","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748832","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":"Dynamic State Estimation in Power Electronics-Dominated Grids Using a Digital Twin Approach","authors":"Ildar Idrisov;Ilya Veretennikov;Federico M. Ibanez","doi":"10.1109/ACCESS.2025.3554258","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554258","url":null,"abstract":"This paper proposes a digital twin (DT) based approach to improve dynamic state estimation (DSE) in power electronics-dominated grids (PEDGs), ultimately contributing to a more reliable, efficient, and sustainable power system by enabling better integration of renewable energy sources and reducing grid instability. Traditional DSE methods struggle with the complexities of PEDGs, such as high penetration of distributed generation and energy storage, leading to inaccurate state estimations. Our approach leverages a real-time, high-fidelity virtual model of a microgrid, including detailed models of DG units, power electronics and load dynamics. This enables accurate state estimation even during transient events. The proposed approach is validated using a hardware-in-the-loop test bench which demonstrates the DT’s accuracy in tracking grid dynamics under various operating conditions. Furthermore, a sensitivity analysis highlights the importance of accurate parameter estimation and minimal communication delays for robust DSE. This research provides a framework for developing reliable and accurate DSE solutions for modern PEDGs, enabling advanced grid control, optimization, and predictive maintenance strategies.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53938-53948"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748835","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 Multiphysics Dataset Generation Procedure for the Data-Driven Modeling of Traction Electric Motors","authors":"Simone Ferrari;Luigi Solimene;Riccardo Torchio;Costanza Anerdi;Fabio Freschi;Luca Giaccone;Gianmarco Lorenti;Francesco Lucchinizz;Piergiorgio Alotto;Gianmario Pellegrino;Maurizio Repetto","doi":"10.1109/ACCESS.2025.3554147","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554147","url":null,"abstract":"This paper presents the work done to address two main challenges in the simulation and design of electric machines for traction applications. On one hand, the modeling process is becoming increasingly complex as the demand for higher efficiency, high power density, and low cost pushes the speed and compactness of the motor to high levels. As a result, the interactions between multiple physical domains (e.g., electromagnetic, thermal, structural, etc.) can no longer be neglected, even in preliminary designs. Consequently, research into new modeling solutions in this area is currently active and widespread. On the other hand, new computational methodologies based on data-driven machine learning are becoming increasingly widespread as the computational power available for this task increases. However, to assess their performance and realize their potential in surrogate and meta-modeling electrical machines, a standardized benchmark for comparing these new approaches is needed. To address these challenges, the paper presents an open-source dataset that provides a reliable foundation for the multi-physical analysis of electric motors used in traction applications. One of the main novelties of this approach is that geometrical and physical data of the motor configuration are shared among different analysis codes. Attention is focused on tailoring the numerical discretization so that the same mesh can be used in different domains, avoiding data conversions and possible numerical inaccuracies. The paper thoroughly explains the workflow developed to create the database, detailing the methodological aspects. Ultimately, the resulting database is made available as an open resource for other researchers in the field. The resulting dataset represents a tool for benchmarking advanced computational methodologies and promoting reproducibility in research.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54534-54546"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937701","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748856","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":"Evolutionary Synthesis of High-Capacity Reconfigurable Multilayer Road Networks Using a Multiagent Hybrid Clustering-Assisted Genetic Algorithm","authors":"Andranik S. Akopov;Levon A. Beklaryan","doi":"10.1109/ACCESS.2025.3554054","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554054","url":null,"abstract":"Modern requirements for urban traffic management and control call for the design of high-capacity reconfigurable multilayer road networks (RMRNs). This paper discusses the proposed evolutionary synthesis approach, a promising method for finding the best configurations of RMRNs, aiming to create road networks with optimized layouts that maximize vehicle outflow. As the complexity of RMRNs increases, due to the addition of overpasses and tunnels, the expenses for building these road networks also rise significantly. Therefore, it is essential to find a balance when choosing the optimal topological solution for an RMRN. These solutions need to maximize traffic flow while minimizing the complexity of the RMRN. To achieve this goal, a new multiagent hybrid clustering-assisted genetic algorithm (MA-HCAGA). The proposed algorithm combines the use of binary-coded crossovers and mutations as genetic operators, and biobjective discrete particle swarm optimization (BODPSO) techniques to improve the evolutionary search process. In addition, the algorithm combines the use of finite-state machines (FSMs) to control the transitions between the states of agent-processes and the fuzzy clustering technique (FCA) to estimate the swarm and select clusters for interaction among the groups of agent-processes and particle swarms. The superior performance of the MA-HCAGA algorithm in evolutionary synthesis of RMRNs has been demonstrated through comparisons with other well-known multiobjective optimization methods. MA-HCAGA has been successfully applied in the evolutionary synthesis of RMRNs, allowing a decision maker to select the optimal RMRN topologies along the approximate Pareto front by selecting specific solutions. A traffic flow simulation model, aggregated with the MA-HCAGA algorithm, has been developed to simulate vehicle flow at various configurations of RMRNs. The results of this study show the effectiveness of the proposed method for configuring RMRNs in order to optimize vehicle outflow and reduce the complexity of RMRNs.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53448-53474"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937695","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761577","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":"Classification and Time-Frequency Localization of Arbitrary LPWAN Signals With Radial Deformable DETR","authors":"Chun Ho Kong;Haibo Hu","doi":"10.1109/ACCESS.2025.3554080","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554080","url":null,"abstract":"With the increasing adoption of Internet-of-Things (IoT) technologies, numerous devices utilizing protocols such as Sigfox and LoRa are now widely available inexpensively and operate in unlicensed ISM bands. However, challenges such as inventory management, unauthorized usage, and network performance must be addressed. Future adoption of emerging IoT protocols with various modulation schemes, bandwidth, and data rates can further complicate this. Therefore, it is important not only to classify but also to localize the frequency, bandwidth, and time of these LPWAN signals on the air for management, security, or band planning purposes. SOTA algorithms usually look through the whole received signal on the time domain or frequency domain only to perform classification tasks, without finding out the corresponding time-frequency location of the signal. This paper proposes to classify and localize time-frequency locations of LPWAN signals by an enhanced version of Deformable DEtection TRansformer (Deformable DETR). We devise an attention radius suitable for processing Low Power Wide Area Network (LPWAN) Spectrogram traces extracted from Software Defined Radios (SDRs) IQ data with Short-Time Fourier Transform (STFT). Inspired by Large Language Models (LLMs), sequences of STFT vectors from SDR IQs can leverage attention mechanisms, and finding out LPWAN signals in spectrograms is similar to object detection tasks in computer vision. Our method eliminates the need for hand-crafting CNN layers or signal processing pipelines for different LPWAN protocols provided that sufficient training samples are available. Therefore, we build a fully annotated dataset for Lora and Sigfox in multiple frequencies, bandwidths, packet data, and time, as well as data augmentation techniques that serve both training and validation datasets for our modified Deformable DETR model. The experimental results demonstrate an average precision of over 89.5% for LoRa signals and over 79.8% when mixed with ultra-narrow-band signals.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53065-53083"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726265","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":"The Impact of Solar Angle and Cloud Shadows on 3D Reconstruction of Rolling Stock Cargo","authors":"Carlina M. Ostrand;Adam D. Reiman;Frank W. Ciarallo;Scott L. Nykl;Clark N. Taylor;Joshua F. Krutz","doi":"10.1109/ACCESS.2025.3554409","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554409","url":null,"abstract":"Meeting the relentless demand for more efficient air cargo transportation is of paramount importance for commercial needs and military missions. This study describes an experiment to test an innovative approach that harnesses cutting-edge stereoscopic vision technology to create 3D point clouds of rolling stock cargo across varying solar angles and cloud shadow conditions. Virtual cargo point clouds are generated by calibrating and systematically organizing the depth and location points from an RGB-D camera and then reprojecting them in a virtual environment. Measurement accuracy was rigorously tested across six camera positions in various combinations of weather conditions against physical ground truth measurements. The high accuracy of such systems offers detailed, real-time insights into optimized cargo loading under unpredictable outdoor conditions. The findings, informed by both quantitative and qualitative analyses, reveal the impact of solar position, cloud coverage, and camera placement on the completeness of the point clouds, quality of the depth points, and accuracy of the measurements. This study provides insights to push the boundaries of cargo logistics possibilities in challenging outdoor environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53581-53593"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740404","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}