IEEE Access最新文献

{"title":"Evaluation of Uplink Video Streaming QoE in 4G and 5G Cellular Networks Using Real-World Measurements","authors":"Darius Chmieliauskas;Šarūnas Paulikas","doi":"10.1109/ACCESS.2025.3554340","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554340","url":null,"abstract":"In this study, we propose a comprehensive method for evaluating the Quality of Experience (QoE) for uplink video streaming in commercial 4G and 5G cellular networks. Uplink video streaming is becoming increasingly important due to applications such as live content creation and remote operations. However, commercial networks are primarily optimized for downlink performance, posing significant challenges for achieving high uplink throughput. We investigate the factors affecting uplink QoE, including signal strength, network congestion, carrier aggregation, and coverage. Our methodology involves comprehensive data collection from network elements and streaming servers, encompassing RF parameters, IP packet data, and performance metrics such as throughput and latency. We employ full-reference metrics such as Video Multi-Method Assessment Fusion (VMAF), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) to assess video quality and address challenges in frame alignment for accurate evaluation. Our dataset includes video recordings, streamed video files, QoE metrics, IP packet data, and Radio Access Network (RAN) measurements, enabling robust regression analysis and machine learning for QoE prediction. We propose a system architecture for real-time data collection and streaming, integrating live video capture, streaming server setup, and network measurement tools. The results highlight the relationship between network conditions and video quality, demonstrating the impact of factors like path loss and Physical Resource Block (PRB) allocation and handover on QoE. This study provides valuable insights and practical solutions for optimizing uplink video streaming QoE in current and future cellular networks.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53996-54018"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761294","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 Wideband Circularly Polarized Antenna Array Using Irregularly Shaped Hexagonal Slot Optimized by Iterative Taguchi’s Method","authors":"Wei-Chung Weng;Chi-Keong Wong","doi":"10.1109/ACCESS.2025.3554048","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554048","url":null,"abstract":"This study proposes a novel 2.45 GHz wideband <inline-formula> <tex-math>$2times 2$ </tex-math></inline-formula> circularly polarized (CP) slot antenna array, which consists of irregularly hexagonal slot CP antenna elements and a sequentially rotated phase feed network. The CP antenna element has a single-layer, single-fed, simple structure. Designing the CP antenna element does not require stacked layers, dual-feed networks, or additional resonant/perturbing components. The antenna element, including its hexagonal slot, is optimized using the iterative Taguchi’s optimization method to broaden the antenna’s impedance and axial ratio bandwidths. Detailed CP antenna design approaches, optimization settings, CP wave mechanisms, and the element’s and antenna array’s results, are discussed. Good agreements between measured and simulated results are revealed, confirming the validity of the proposed designs. The proposed wideband <inline-formula> <tex-math>$2times 2$ </tex-math></inline-formula> CP irregularly shaped hexagonal slot antenna array provides a maximum boresight gain of 15.3 dBic and a CP bandwidth of 47.6% from 2.13 to 3.46 GHz. At the center frequency of 2.45 GHz, the cross-polarization level in the main beam direction is less than –23.6 dB; the front-to-back ratio is larger than 40 dB; and the back-lobe level is less than –22 dB. A comparison of the proposed array with other <inline-formula> <tex-math>$2times 2$ </tex-math></inline-formula> wideband CP antenna arrays in the literature shows that it has a broader CP bandwidth and higher gain than those of all the compared antenna arrays. This study also demonstrates that irregular radiating geometries provide a novel approach to antenna design.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54394-54406"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748882","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":"An Analytical Cost Function Design and Implementation for Predictive Control of Induction Machine Drives","authors":"Wei Wei;Liming Yan;Shun Tian;Xisheng Xu;Keke Sun","doi":"10.1109/ACCESS.2025.3553839","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553839","url":null,"abstract":"In finite control set-model predictive torque control (FCS-MPTC) of induction machine (IM), the optimal design of weighting factors for the cost function has always been a research difficulty in community of scholars. Generally, for weighting factors of FCS-MPTC, the rating method or cut-and-trial method are often utilized. These methods adopts the fixed value for weighting factor, which can not adapt to multiple operating modes of IM. In addition, the cut-and-trial method is cumbersome and difficult to achieve multi-objective balanced regulation. To solve this problem, this paper proposes an analytical cost function design for predictive control of induction machine drives (abbreviated as ACF-MPTC). According to the internal electromagnetic relationship of IM, the analytical expression of weighting factor is obtained through theoretical derivation. The control performances of traditional method and ACF-MPTC, which includes root mean square (RMS) of electromagnetic torque, RMS of stator flux and total harmonic distortion (THD) of stator current, for different operating modes of IM is studied. The parameter sensitivity of ACF-MPTC is analyzed, and a robust ACF-MPTC based on online parameter identification technology is proposed. The experimental results verify the effectiveness of the proposed algorithm.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54313-54321"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937498","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748727","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":"Federated Learning for Fall Detection With Multimodal Residual Fusion and Pareto-Optimized Client Selection","authors":"Bao-Quan Wang;Fan Yang;Yi Wang;Fan Zhao;Yun-Fei Han;Yu-Peng Ma","doi":"10.1109/ACCESS.2025.3553419","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553419","url":null,"abstract":"With the increasing aging population and the prevalence of chronic diseases, fall detection has become a critical component in elderly healthcare monitoring. However, challenges such as multimodal data integration and joint analysis in Internet of Medical Things (IoMT) environments and data heterogeneity across sources hinder efficient and accurate fall detection. This paper proposes a Federated Learning-based framework with Multimodal Residual Fusion and Pareto-optimized Client Selection (FLPCS-MRF). Firstly, the framework incorporates a multimodal feature fusion network with a residual mechanism, which adaptively learns the optimal fusion scheme through residual connections, dynamically suppressing noise interference from redundant modalities. Secondly, to address variations in data modalities, distributions, and quality across clients, by considering all client factors rather than treating clients as independent, five innovative evaluation metrics are designed to assess the convergence and generalization performance of the local models. Finally, a Pareto-optimized client selection method is introduced to efficiently select reliable clients for global aggregation, ensuring both the stability and robustness of the global model. Extensive experiments on the UP Fall dataset demonstrate the effectiveness of the proposed approach, achieving 95.27% accuracy and 95.42% F1-score, outperforming existing methods. Additionally, it demonstrates strong robustness in complex scenarios involving imbalanced data distributions and missing modalities.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54148-54167"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748731","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":"Forecasting Stock Market Volatility Using Housing Market Indicators: A Reinforcement Learning-Based Feature Selection Approach","authors":"Pourya Zareeihemat;Samira Mohamadi;Jamal Valipour;Seyed Vahid Moravvej","doi":"10.1109/ACCESS.2025.3554224","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554224","url":null,"abstract":"This study tackles the complex challenge of accurately predicting stock market volatility through indicators from the housing market. We propose a sophisticated Early Warning System (EWS) designed to forecast stock market instability by leveraging the predictive power of housing market bubbles. Current EWS methods often face significant hurdles, including model generalization, feature selection, and hyperparameter optimization challenges. To directly address these issues, our innovative approach utilizes a spatial attention-based Transductive Long Short-Term Memory (TLSTM) model combined with a Reinforcement Learning (RL) strategy, which is further enhanced by a novel scope loss function for refined feature selection and an Artificial Bee Colony (ABC) algorithm for hyperparameter optimization. The TLSTM model surpasses traditional LSTM models by effectively capturing subtle temporal shifts and prioritizing data points proximate to the test sample, thereby enhancing model generalization. The RL component actively refines feature selection through continuous data interaction, ensuring the model captures the most significant features and effectively mitigates the risk of overfitting. The introduction of the scope loss function strategically manages the trade-off between exploiting known data and exploring new patterns, thereby maintaining a healthy balance between accuracy and generalizability. Additionally, the customized ABC algorithm specifically optimizes hyperparameters to increase the adaptability and performance of the model under varying market conditions. We validated our EWS using data from the Korean market, achieving an impressive accuracy of 90.427%. This validation demonstrates the robust capability of the system to forecast market dynamics. Our study significantly contributes to financial analytics by providing deeper insights into the interactions between housing and stock markets, particularly during periods of market bubbles. This research not only enhances predictive accuracy but also aids in understanding complex market behaviors, thereby offering valuable tools for financial risk management and decision-making.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"52621-52643"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726583","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":"Set Membership Adaptive Non Parametric Identification of Non-Linear Systems","authors":"Juan Alejandro Castano;Fernando Quevedo;Fredy Ruiz","doi":"10.1109/ACCESS.2025.3554187","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554187","url":null,"abstract":"Adaptation is a desirable feature when dealing with the identification of complex systems. However, this property can be difficult to achieve when non-convex model structures such as neural networks are employed to parametrize the unknown system. This work introduces a novel approach for dynamically adapting the data set that defines the model in non-parametric Set Membership identification methods. The proposed solution constructs a nonparametric, nonlinear model of a discrete-time dynamical system by exploring the data set, assuming the system follows a Nonlinear Auto-Regressive model with exogenous Inputs (NARX) structure. The identification data are assumed to be affected by unknown but bounded noise. Specifically, two strategies are proposed to adapt the identification data set while preserving system performance dynamically. The first strategy allows the data set to incorporate new data as novel modeling information becomes available, while redundant information can be eliminated when memory conditions are reached. The second strategy introduces new information sequentially; once an auxiliary memory vector in the data set reaches its desired cardinality, the method orderly replaces the oldest data with newer dynamics. These strategies enable the identified models to adapt in response to unmodeled behaviors arising from time-varying dynamics or limited initial data sets, minimizing the need for extensive experimentations and allowing to dynamically reconstruct the data set for developing data-driven models. The effectiveness of the proposed approaches is demonstrated through the experimental modeling of a nonlinear mechatronic system. Performance is benchmarked against neural network models and a static Set Membership identification strategy. Results indicate that the proposed dynamic data set generation approach improves the accuracy and robustness of the model when using non-informative experimental data sets as starting point for the estimation, improving the overall performance of the data-driven modeling task and facilitating the use of these modeling techniques in real environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54254-54266"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748875","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 Novel Learning-Based MPC Method via Basic-Residual Cooperative Model","authors":"Yuesheng Liu;Zhongxian Xu;Ning He;Lile He;Fuan Cheng","doi":"10.1109/ACCESS.2025.3554168","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554168","url":null,"abstract":"This study proposes a novel model predictive control (MPC) method based on the basic-residual cooperative model. Compared to existing learning-based MPC methods that rely on a single network model as prediction models for either static feature capture or dynamic adaptation, which often result in insufficient adaptability or compromised computational efficiency, the proposed method integrates a dual-network architecture: a Long Short-Term Memory (LSTM) network to capture static system features, and a self-attention feed-forward neural network to adapt to dynamic aspects. The convergence and stability of the resulting control system are proven through theoretical analysis. The effectiveness of proposed method is validated through numerical simulations and experiments. Experimental results show that the proposed MPC method can reduce the prediction model’s root mean square error by about 70% compared to classical static model-based MPC and cuts computational time by about 30% compared to classical dynamic model-based MPC. The proposed method significantly enhances the model adaptability and computational efficiency of nonlinear dynamic systems, such as autonomous vehicles and robots.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"54192-54203"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748916","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":"mHealth Case Study Presenting Design SynMeth, a Rapid Prototyping MBSE Methodology, by Advancing Specific OPM-to-SysML Mapping","authors":"Cristian Vizitiu;Kevin Dominey;Alexandru Nistorescu;Adrian Dinculescu;Mihaela Marin","doi":"10.1109/ACCESS.2025.3553945","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3553945","url":null,"abstract":"Designing IoT-based cognitive assessment and monitoring devices for older adults poses critical challenges in managing trade-offs between accessibility and functionality. With the global aging population to exceed 2 billion by 2050, an increasing number of older adults will require Active Assisted Living (AAL) technologies to support independent living. Cognitive impairments make standard interfaces difficult to use, necessitating user-centered design approaches. Effective solutions must address the transition from document-centric to model-based design, incorporating co-design with users and caregivers, iterative modeling cycles, and continuous model evolution. This study highlights key factors that call for a hybrid approach, blending the flexibility of rapid prototyping with the accuracy and robustness of precision engineering. This study demonstrates a successful implementation of a cognitive detection device within an AAL European project by presenting Design SynMeth, a novel blended Model-Based Systems Engineering methodology that maps Object-Process Methodology (OPM) to Systems Modeling Language (SysML) using a modified MagicGrid framework. This approach bridges early and late design phases, integrating OPM’s strength in conceptual modeling and SysML’s rigor in technical specifications. Design SynMeth enhances system design efficiency and adaptability to IoT challenges. The case study reveals how Design SynMeth methodology models the architecture of a mobile health well-being device for detecting cognitive issues, supporting seniors’ autonomy. It highlights the dynamic interplay between problem and solution domains, leveraging OPM diagrams for problem domain and SysML diagrams for requirements and solution domain. This work advances the state of the art in IoT-based cognitive monitoring and promotes innovative, human-centered engineering for aging societies.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53531-53545"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937483","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761543","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":"Federated Learning With Sailfish-Optimized Ensemble Models for Anomaly Detection in IoT Edge Computing Environment","authors":"Aravam Babu;A. Bagubali","doi":"10.1109/ACCESS.2025.3554301","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554301","url":null,"abstract":"The Internet of Things (IoT) has transformed cyber-physical systems by enabling seamless connectivity and automation. However, IoT devices face resource constraints, making anomaly detection challenging. Traditional centralized approaches suffer from computational inefficiencies, increased latency, and privacy concerns, making them unsuitable for real-time anomaly detection in distributed IoT environments. To address these challenges, this paper proposes a privacy-preserving anomaly detection framework that integrates Federated Learning (FL) with an optimized Isolation Forest model. FL enables decentralized training on IoT devices, reducing the risk of data breaches. However, anomaly detection performance is often hindered by suboptimal parameter selection. To overcome this, the Sailfish Optimization Algorithm (SFO) is incorporated to fine-tune the Isolation Forest model’s parameters dynamically, balancing exploration and exploitation. This optimization enhances accuracy while maintaining data confidentiality. Additionally, the framework is evaluated against leading FL-based and traditional anomaly detection models, including Local Outlier Factor (LOF), Generative Adversaria (GAN), and Variational autoencoder (VAE), demonstrating superior performance in recall and F1-score. Extensive experiments on benchmark datasets confirm that the proposed method achieves higher anomaly detection efficiency with a lower error rate than existing methods. The results establish this framework as a scalable, privacy-preserving, and computationally efficient solution for anomaly detection in IoT edge environments, addressing critical limitations in security, latency, and data privacy in real-world applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53171-53187"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761293","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":"Innovative Load Frequency Control: Integrating Adaptive Backstepping and Disturbance Observers","authors":"Javad Ansari;Mohamadreza Homayounzade;Ali Reza Abbasi","doi":"10.1109/ACCESS.2025.3554141","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3554141","url":null,"abstract":"Load frequency control (LFC) in large interconnected power systems is crucial for balancing electricity supply and demand while minimizing frequency deviations. Traditional methods like proportional-integral-derivative (PID)controllers and advanced techniques such as evolutionary algorithms and artificial intelligence (AI) have limitations, including computational complexity, sensitivity to parameter changes, and high resource demands. This paper introduces a novel decentralized observer-based backstepping control (DOBC) strategy to overcome these challenges. In our work, each area controller utilizes local measurements and feedback signals to regulate its own area frequency. This approach inherently reduces the reliance on centralized communication and minimizes the impact of potential communication failures, such as packet losses and delays. The proposed method synergistically combines backstepping and disturbance observer techniques, resulting in rapid and stable system responses with reduced control effort, while a noncertainty equivalent adaptive approach ensures exponential disturbance estimation and maintains system stability under time-varying disturbances. Unlike conventional sliding mode control, the proposed method eliminates chattering, making it suitable for sensitive applications. Simulations validate its effectiveness under time delays, parametric uncertainties, nonlinearities, and load disturbances. Results show superior transient response, better oscillation damping, and lower control effort compared to adaptive neuro-fuzzy inference system based fractional-order PID-acceleration controller (ANFIS-FOPIDA), Second-Order sliding mode control (SOSMC), and Deep Reinforcement Learning (DRL). The paper concludes with a rigorous stability and robustness analysis, demonstrating the method’s resilience to parametric uncertainties and time-varying disturbances. This highlights its practical applicability and advantages in modern power systems.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"53673-53693"},"PeriodicalIF":3.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937696","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740379","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}