IEEE Transactions on Consumer Electronics最新文献_第3页

Neuro-Modeling Infused EMT Analytics 神经建模注入EMT分析

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-14 DOI: 10.1109/TCE.2025.3542069

Qing Shen;Yifan Zhou;Peng Zhang;Yacov A. Shamash;Roshan Sharma;Bo Chen

引用次数: 0

PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues PD-SORT：基于伪深度线索的闭塞鲁棒多目标跟踪

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-13 DOI: 10.1109/TCE.2025.3541839

Yanchao Wang;Dawei Zhang;Run Li;Zhonglong Zheng;Minglu Li

{"title":"PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues","authors":"Yanchao Wang;Dawei Zhang;Run Li;Zhonglong Zheng;Minglu Li","doi":"10.1109/TCE.2025.3541839","DOIUrl":"https://doi.org/10.1109/TCE.2025.3541839","url":null,"abstract":"Multi-object tracking (MOT) is a rising topic in video processing technologies and has important application value in consumer electronics. Currently, tracking-by-detection (TBD) is the dominant paradigm for MOT, which performs target detection and association frame by frame. However, the association performance of TBD methods degrades in complex scenes with heavy occlusions, which hinders the application of such methods in real-world scenarios. To this end, we incorporate pseudo-depth cues to enhance the association performance and propose Pseudo-Depth SORT (PD-SORT). First, we extend the Kalman filter state vector with pseudo-depth states. Second, we introduce a novel depth volume IoU (DVIoU) by combining the conventional 2D IoU with pseudo-depth. Furthermore, we develop a quantized pseudo-depth measurement (QPDM) strategy for more robust data association. Besides, we also integrate camera motion compensation (CMC) to handle dynamic camera situations. With the above designs, PD-SORT significantly alleviates the occlusion-induced ambiguous associations and achieves leading performances on DanceTrack, MOT17, and MOT20. Note that the improvement is especially obvious on DanceTrack, where objects show complex motions, similar appearances, and frequent occlusions. The code is available at <uri>https://github.com/Wangyc2000/PD_SORT</uri>.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"165-177"},"PeriodicalIF":4.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Control Methods of Bidirectional Single-Phase AC-DC DAB Converter for Light EV Charging Applications 轻型电动汽车充电用双向单相AC-DC DAB变换器的改进控制方法

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-13 DOI: 10.1109/TCE.2025.3541968

Akash Kedia;Anandarup Das

{"title":"Improved Control Methods of Bidirectional Single-Phase AC-DC DAB Converter for Light EV Charging Applications","authors":"Akash Kedia;Anandarup Das","doi":"10.1109/TCE.2025.3541968","DOIUrl":"https://doi.org/10.1109/TCE.2025.3541968","url":null,"abstract":"Dual Active Bridge (DAB) based unfolder is a promising converter for Light Electric Vehicle (LEV) battery charging applications in single phase ac systems owing to several advantages such as high efficiency, high power density and galvanic isolation. For single phase applications, however, a unique challenge appears in the DAB operation, wherein the RMS value of the DAB inductor current increases much above the RMS grid current, often 1.45 times or more. This increases the conduction losses and negatively impacts the system efficiency. Additionally, distortions near zero crossing are often encountered in the converter’s ac side voltage profile, adversely affecting the grid current THD. A novel current minimization technique is proposed in this paper where the DAB inductor RMS current is reduced significantly, to only 1.15 times the output grid current. The closed loop control of the DAB is modified to accurately control the output capacitor voltage using a closed-form solution, resulting in a smooth zero transition. Further, a grid distortion mitigation technique is incorporated to improve the output grid current THD. The proposed work is verified using MATLAB Simulink software and experimentally validated using a lab prototype with a 230V, 50Hz single phase ac grid.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1842-1853"},"PeriodicalIF":4.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DRGCN-BiLSTM: An Electrocardiogram Heartbeat Classification Using Dynamic Spatial-Temporal Graph Convolutional and Bidirectional Long-Short Term Memory Technique DRGCN-BiLSTM：基于动态时空图卷积和双向长短期记忆技术的心电图心跳分类

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-11 DOI: 10.1109/TCE.2025.3540875

Neenu Sharma;Deepak Joshi

{"title":"DRGCN-BiLSTM: An Electrocardiogram Heartbeat Classification Using Dynamic Spatial-Temporal Graph Convolutional and Bidirectional Long-Short Term Memory Technique","authors":"Neenu Sharma;Deepak Joshi","doi":"10.1109/TCE.2025.3540875","DOIUrl":"https://doi.org/10.1109/TCE.2025.3540875","url":null,"abstract":"An automated cardiac rhythm classification using electrocardiograms is crucial for accurate and timely diagnosis of cardiovascular disease. Recent advances in deep learning have facilitated automated arrhythmias recognition, surpassing traditional ECG methods that depend on manual feature extraction. Despite significant progress in existing arrhythmias classification techniques, the current method fails to utilize spatial-temporal interaction and task-specific features include temporal dependencies among observations and a significantly imbalanced class distribution in the dataset. Thus, the accuracy of network-based ECG heart-beat classification still requires improvements. To address this issue, an effective classification algorithm combined with synthetic minority oversampling technique (SMOTE)-Tomek, dynamic perceptive region spatial-temporal graph convolutional network with bidirectional long-short term memory (DRGCN-BiLSTM) is proposed for effective intelligent arrhythmia recognition. This <inline-formula> <tex-math>$ mathrm {DRGCN_BiLSTM}$ </tex-math></inline-formula> model employs a trainable weighted <inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula>-neighborhood graph to capture the pattern of time series within ECG segments. The SMOTE-Tomek technique addresses data imbalance, while BiLSTM captures temporal features from the graph network. The presented technique was validated on the MIT-BIH arrhythmia database, comprising a total of 109,253 ECG beats and the experimental results demonstrate the average accuracy of 99.92% respectively. The proposed method achieves superior performance as compared to state-of-the-art techniques, which results in better diagnosis of heart-related problems.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"579-593"},"PeriodicalIF":4.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scale Feature-Aware Generative Adversarial Network Improve MRI Device Data Imbalance for Healthy Consumption 规模特征感知生成对抗网络改善MRI设备健康消费数据不平衡

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-11 DOI: 10.1109/TCE.2025.3540776

Yawu Zhao;Shudong Wang;Sibo Qiao;Yulin Zhang;Jiehuan Wang;Wenjing Yin;Tiyao Liu;Shanchen Pang

{"title":"Scale Feature-Aware Generative Adversarial Network Improve MRI Device Data Imbalance for Healthy Consumption","authors":"Yawu Zhao;Shudong Wang;Sibo Qiao;Yulin Zhang;Jiehuan Wang;Wenjing Yin;Tiyao Liu;Shanchen Pang","doi":"10.1109/TCE.2025.3540776","DOIUrl":"https://doi.org/10.1109/TCE.2025.3540776","url":null,"abstract":"In consumer healthcare, the imbalance between the imaging data of MRI devices creates a significant barrier to the generalization ability of medical image segmentation. We employ generative adversarial networks to address the challenges inherent in the heterogeneity of data distribution among different MRI devices. First, we propose the Scale Feature Awareness Module (SFAM), which can skillfully capture image details at different scales and broader contextual information. Then, we propose the Dynamic Scale Attention Module (DSAM), which aims to combine feature mappings at different scales for different paths dynamically. We can assign different dynamic weights to each scale to enhance the feature representation. Finally, we propose a multi-scale discriminator to guide the generation of aneurysm images with different diameters. Based on the above modules, we designed Scale Feature Aware Generative Adversarial Network (SFAGAN) to generate medical images with the same distribution. It is experimentally demonstrated that SFAGAN improves PSNR, SSIM, and FID values by 0.64, 0.62, and 7.99, respectively, over the SOTA method. In addition, we use the generated data for downstream segmentation tasks to demonstrate the quality of the generated images. Notably, our SFAGAN has significant performance, making its application in medical systems very feasible.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"984-996"},"PeriodicalIF":4.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DiffusionClusNet: Deep Clustering-Driven Diffusion Models for Ultrasound Image Enhancement DiffusionClusNet：超声图像增强的深度聚类驱动扩散模型

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-11 DOI: 10.1109/TCE.2025.3540502

Nuo Chen;Yongquan Zhang;Chenchen Fan;Wei Zhao;Changmiao Wang;Hai Wang

{"title":"DiffusionClusNet: Deep Clustering-Driven Diffusion Models for Ultrasound Image Enhancement","authors":"Nuo Chen;Yongquan Zhang;Chenchen Fan;Wei Zhao;Changmiao Wang;Hai Wang","doi":"10.1109/TCE.2025.3540502","DOIUrl":"https://doi.org/10.1109/TCE.2025.3540502","url":null,"abstract":"In modern medical diagnostics, high-quality ultrasound images are essential because they are cost-effective, non-invasive, and capable of providing dynamic recordings. Nevertheless, obtaining such high-quality images is challenging, especially in resource-limited areas, which negatively impacts diagnostic accuracy. To address these issues, we propose a novel method for enhancing ultrasound images using deep clustering-enhanced diffusion models. Our proposed method consists of two main components: an image enhancement pathway and an Auxiliary Classification Pathway (ACP), which are integrated through a Fusion of Image and Classification (FIC) module. The image enhancement pathway employs a structure that includes a Variational Autoencoder (VAE) encoder, a UNet denoising network, and a VAE decoder. This structure progressively reduces noise and generates high-quality images. Simultaneously, the ACP utilizes a convolutional neural network, a transformer encoder, and a clustering module to extract classification information, which supports the enhancement process. The FIC module uses a cross-attention mechanism to merge the image and classification features, thus enhancing the overall performance of image enhancement. To ensure the generated images retain their structural integrity, Structural Similarity (SSIM) loss is employed. Experiments conducted on multiple ultrasound datasets reveal that our method surpasses existing techniques in terms of peak signal-to-noise ratio and SSIM scores. Clinically, our approach significantly improves image contrast and structural detail, leading to more accurate diagnoses. This diffusion-based strategy for image enhancement and classification feature fusion introduces a fresh perspective on preserving structure and enhancing detail in medical image processing. Our Code is available at <uri>https://github.com/ichbincn/Ultrasound-Enhancement</uri>.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1495-1503"},"PeriodicalIF":4.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AWKNet: A Lightweight Neural Network for Motor Imagery Electroencephalogram Classification Based on Adaptive Wavelet Transform Kolmogorov-Arnold 基于自适应小波变换Kolmogorov-Arnold的运动图像脑电图分类轻量级神经网络

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-11 DOI: 10.1109/TCE.2025.3540970

Yu Song;Hang Zhang;Janzhi Man;Xiaoqian Jin;Qi Li

{"title":"AWKNet: A Lightweight Neural Network for Motor Imagery Electroencephalogram Classification Based on Adaptive Wavelet Transform Kolmogorov-Arnold","authors":"Yu Song;Hang Zhang;Janzhi Man;Xiaoqian Jin;Qi Li","doi":"10.1109/TCE.2025.3540970","DOIUrl":"https://doi.org/10.1109/TCE.2025.3540970","url":null,"abstract":"Motor imagery electroencephalography (MI-EEG) is widely used in the neural rehabilitation field, including for hybrid device control, such as robotic arms. However, it is difficult to apply large models with good performance in consumer electronics (CE) with limited computing and memory resources. To address this challenge, this study proposes an adaptive wavelet transform Kolmogorov-Arnold network (KAN) approach named AWKNet, which uses wavelet loss to construct personalized discrete wavelet functions for MI-EEG features suited to different topics to learn an effective multiresolution wavelet transform. Second, a depth-separable convolutional layer is used to decouple the cross-channel and frequency domain features of the EEG data, and the conventional multilayer perceptron (MLP) layer is replaced based on the KAN technique. The proposed model is lightweight and improves the performance of the brain-computer interface (BCI) system. The model was employed to classify EEG signals acquired in the BCI Comparison IV 2a dataset and in a real-world environment. In both tasks, the visualization of model weights showed that the trained AWKNet consistently generates scientifically interpretable lightweight models and outperforms more advanced neural networks in terms of classification performance, which indicates that AWKNet has broader application potential in CE. All the code is deposited on GitHub (<uri>https://github.com/Songyu-EEGsignals/AdaptiveWavelets</uri>).","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1219-1234"},"PeriodicalIF":4.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quality of Service-Aware 6G- Enabled NB-IoT for Health Monitoring in Long Distance High-Speed Trains 基于服务质量感知的6G支持的NB-IoT用于长途高速列车的健康监测

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-10 DOI: 10.1109/TCE.2025.3540197

Bipasha Guha Roy;Deepsubhra Guha Roy;Piyali Datta;Surbhi Bhatia Khan;Fatima Asiri;Manel Ayadi

{"title":"Quality of Service-Aware 6G- Enabled NB-IoT for Health Monitoring in Long Distance High-Speed Trains","authors":"Bipasha Guha Roy;Deepsubhra Guha Roy;Piyali Datta;Surbhi Bhatia Khan;Fatima Asiri;Manel Ayadi","doi":"10.1109/TCE.2025.3540197","DOIUrl":"https://doi.org/10.1109/TCE.2025.3540197","url":null,"abstract":"Internet of Things connectivity in home health monitoring is a high-in-demand application area. The electronics industry and procedural researchers seek high-end, secured, on-time, cost-effective ways to build reliable quality of service (QoS) proved autonomous systems using existing wireless techniques. Also, the continuous availability of a traffic-free gateway, particularly in isolated places, is necessary for large-scale data gathering and real-time data update processes to function the sensor nodes in an Internet of Things (IoT) network. Improved Narrowband IoT (NB-IoT) is one of the most desirable networks offered by 6th generation (6G) connectivity in IoT-associated remote-monitoring proceedings. In this article, we propose a QoS-aware narrow bandwidth allocation-based prototype model for healthcare monitoring in long-distance high-speed trains during the patient transfer from home to the healthcare center or vice versa. This article demonstrates a possible enhancement in social aspects of cognitive IoT applications with large data systems in industrial informatics.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1136-1147"},"PeriodicalIF":4.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Series Arc Fault Detection by Modeling and Integral Regulated Residual Analysis 基于建模和积分调节残差分析的串联电弧故障检测

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-07 DOI: 10.1109/TCE.2025.3539675

Wenchao Miao;Fengyun Zhi;Qiqi Chen;Tianling Shi;Fei Wang

{"title":"Series Arc Fault Detection by Modeling and Integral Regulated Residual Analysis","authors":"Wenchao Miao;Fengyun Zhi;Qiqi Chen;Tianling Shi;Fei Wang","doi":"10.1109/TCE.2025.3539675","DOIUrl":"https://doi.org/10.1109/TCE.2025.3539675","url":null,"abstract":"With the rapid development of renewable energy, photovoltaic systems, energy storage systems, and DC microgrids are widely used. However, arc faults can cause electrical fires and even compromise the safety and reliability of the system. In this paper, a modeling and integral-regulated-residual-analysis-based arc fault detection technique is developed for DC systems. The generalized state space average model of the DC system is developed under normal operation and arc fault conditions. The power electronic harmonics are considered in the modeling and analyzed to estimate the state variation of the system. The developed model can predict the arc current and can preliminarily determine the causes of variation. The integral regulated residual analysis method is proposed to calculate the intersection area between the model-predicted arc current and the actual arc current. The nominal values of the intersection area can be determined by experiments. Thus, an arc fault can be identified when the value of the intersection area reaches the nominal value. In this way, the modeling and integral regulated residual analysis algorithm is established and the arc detector is developed. The experimental results of 600 groups of tests verified that the technique can detect sustained and intermittent arc faults at different arc formation conditions with an accuracy of 99.5% within 80 ms despite the effects of line impedance, load transient and power electronics noise.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"1125-1135"},"PeriodicalIF":4.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Developing a Consumer Electronics Robotics With a Large Language Model Based on a Trustworthy AI Framework 基于可信赖AI框架的大型语言模型消费类电子机器人开发

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2025-02-04 DOI: 10.1109/TCE.2025.3538785

Hsin-Te Wu;Wei Wei;Syuan-Hao Li;Mu-Yen Chen

{"title":"Developing a Consumer Electronics Robotics With a Large Language Model Based on a Trustworthy AI Framework","authors":"Hsin-Te Wu;Wei Wei;Syuan-Hao Li;Mu-Yen Chen","doi":"10.1109/TCE.2025.3538785","DOIUrl":"https://doi.org/10.1109/TCE.2025.3538785","url":null,"abstract":"As many countries face the challenges of an aging population and declining birth rates, the demand for labor, particularly for assisting the elderly, is increasing. Traditional robots, being standardized products, require engineers to adjust parameters to fit the unique lifestyle of each household, which is time-consuming and inconvenient. However, with the rapid development of artificial intelligence and consumer electronics, there is a growing need for home robots with smarter interfaces to achieve the goal of intelligent living. This paper proposes a home robot based on a trustworthy AI framework, integrated with large language models (LLM). These LLM can perform natural language processing and object recognition, allowing users to control the robot’s operations through natural language commands. This innovation further advances consumer electronics. The robot’s arm can remember these actions and operate according to instructions. Additionally, the robot arm is equipped with monitoring functions, capable of overseeing the operation of other robots and using cameras to detect errors. This development is significant in the field of consumer electronics. The robot also uses Long Short-Term Memory (LSTM) networks to predict the motion paths of the robotic arm, ensuring smooth and efficient operation. This integration of AI and robotics aims to enhance the adaptability and functionality of home robots, making them more suitable for the diverse needs of modern households, improving the quality of life for the elderly, and driving innovation in the consumer electronics field.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 1","pages":"2027-2038"},"PeriodicalIF":4.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0