IEEE Transactions on Consumer Electronics最新文献_第10页

Human Body Parsing in Thermal InfraRed Domain 热红外领域的人体解析

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-06 DOI: 10.1109/tce.2024.3438809

Zeyu Wang, Kai Shen, Dong Wang, Haibin Shen, Kejie Huang

引用次数: 0

An Adapted Route Recommendation Model for Consumers Based on 6G Networks, Blockchain and Soft Computing Methods 基于 6G 网络、区块链和软计算方法的消费者适应性路线推荐模型

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-06 DOI: 10.1109/tce.2024.3437639

Jiming Ma

引用次数: 0

A Dynamically Adaptive Rising-Edge Shifting Frequency PWM Implemented MPFT Technique for Portable EV Charger Development 用于便携式电动汽车充电器开发的动态自适应上升沿移频 PWM MPFT 技术

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-06 DOI: 10.1109/TCE.2024.3438878

Saran Chaurasiya;Bhim Singh

{"title":"A Dynamically Adaptive Rising-Edge Shifting Frequency PWM Implemented MPFT Technique for Portable EV Charger Development","authors":"Saran Chaurasiya;Bhim Singh","doi":"10.1109/TCE.2024.3438878","DOIUrl":"10.1109/TCE.2024.3438878","url":null,"abstract":"For EV charging applications, performance of LLC resonance half-bridge is greatly influenced by non-linear variations in battery parameters. This has a significant impact on charging efficiency and system reliability. For efficient operation, all FETs must operate with zero voltage switching (ZVS). Additionally, converter must operate with minimum circulation of reactive power among all active and passive devices. To deal with this issue, this paper proposes hybrid modulation scheme for LLC resonating converter. It is designed with coordinated utilization of both frequency modulation and rising edge shift (RES) modulation. Modulation scheme modifies pulse width dynamically and control rising edge with amount of power processing. This provides right choice of switching frequency and intermediated DC link range, which reduces switching losses in MOSFET and conduction body diode losses. Additionally, active hybrid DC link tracking reduces limit of maximum switching frequency and ensures a limited amount of reactive energy to facilitate ZVS operation. Combined approach provides maximum power factor tracking (MPFT) for wide voltage operation of charger. For this, a 500W system is designed with single phase Vienna rectifier for power factor correction (PFC) and half bridge LLC converter as a DC-DC isolated stage. It is designed for 160V-240V AC over 41-60V charging voltage.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 3","pages":"5363-5375"},"PeriodicalIF":4.3,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930009","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

Large-Scale Medical Records Analysis Driven by AI-Driven Method in Healthcare Consumer Electronics 医疗保健消费电子产品中的人工智能方法驱动的大规模医疗记录分析

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-06 DOI: 10.1109/tce.2024.3439577

Xin Wang, Zhonghua Liu, Longhao Zou, Jilong Wang, Xianglin Zhang, Ning Liu

引用次数: 0

Decentralized Native Path Planning for Smart Vehicles With Evolutionary Algorithm and Blockchain Technology 利用进化算法和区块链技术实现智能车辆的去中心化本地路径规划

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-06 DOI: 10.1109/tce.2024.3437686

Yu Wu

引用次数: 0

Using Multimodal Biometrics, Data Hiding and Encryption for Secure Healthcare Imaging System 利用多模态生物识别技术、数据隐藏和加密技术实现安全的医疗成像系统

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-05 DOI: 10.1109/tce.2024.3438356

Kedar Nath Singh, Naman Baranwal, Amit Kumar Singh, Amrit Kumar Agrawal, Huiyu Zhou

引用次数: 0

Multistage Evolutionary Generative Adversarial Network for Image Generation 用于图像生成的多级进化生成对抗网络

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-05 DOI: 10.1109/TCE.2024.3438683

Xiu Zhang;Baiwei Sun;Xin Zhang

{"title":"Multistage Evolutionary Generative Adversarial Network for Image Generation","authors":"Xiu Zhang;Baiwei Sun;Xin Zhang","doi":"10.1109/TCE.2024.3438683","DOIUrl":"10.1109/TCE.2024.3438683","url":null,"abstract":"Consumer electronic devices are popular in human’s everyday use, and cover a wide range of devices and services. Consumer electronics like smartphone and tablet use digital technologies to enhance human’s entertainment and health. The creation of digital content or data augmentation sometimes requires using generative artificial intelligence technologies. Although data generation systems have been successfully used in some consumer products, it is still challenging to create a powerful generative system due to the complexity of input signals and the difficulty of model training. In this paper, a multistage evolutionary generative adversarial network (GAN) framework is proposed to alleviate the above challenges. The multistage evolutionary GAN is a general framework and can be instantiated to existing evolutionary GAN and its variants. Moreover, this paper designs a two-stage and a three-stage evolutionary GAN methods. The two models show that different variation operators and evaluation methods can be used in different stages. Experiments are conducted on both synthetic and real-world datasets. The results show that the proposed methods are effective in capturing complex input signals and alleviating the model training problem. The proposed methods can greatly facilitate the application of image generation systems in consumer products.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 3","pages":"5483-5492"},"PeriodicalIF":4.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930012","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

Harnessing Generative Modeling and Autoencoders Against Adversarial Threats in Autonomous Vehicles 利用生成模型和自动编码器应对自动驾驶汽车中的对抗性威胁

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-05 DOI: 10.1109/TCE.2024.3437419

Kathiroli Raja;Sudhakar Theerthagiri;Sriram Venkataraman Swaminathan;Sivassri Suresh;Gunasekaran Raja

{"title":"Harnessing Generative Modeling and Autoencoders Against Adversarial Threats in Autonomous Vehicles","authors":"Kathiroli Raja;Sudhakar Theerthagiri;Sriram Venkataraman Swaminathan;Sivassri Suresh;Gunasekaran Raja","doi":"10.1109/TCE.2024.3437419","DOIUrl":"10.1109/TCE.2024.3437419","url":null,"abstract":"The safety and security of Autonomous Vehicles (AVs) have been an active area of interest and study in recent years. To enable human behavior, Deep Learning (DL) and Machine Learning (ML) models are extensively used to make accurate decisions. However, the DL and ML models are susceptible to various attacks, like adversarial attacks, leading to miscalculated decisions. Existing solutions defend against adversarial attacks proactively or reactively. To improve the defense methodologies, we propose a novel hybrid Defense Strategy for Autonomous Vehicles against Adversarial Attacks (DSAA), incorporating both reactive and proactive measures with adversarial training with Neural Structured Learning (NSL) and a generative denoising autoencoder to remove the adversarial perturbations. In addition, a randomized channel that adds calculated noise to the model parameter is utilized to encounter white-box and black-box attacks. The experimental results demonstrate that the proposed DSAA effectively mitigates proactive and reactive attacks compared to other existing defense methods, showcasing its performance by achieving an average accuracy of 80.15%.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 3","pages":"6216-6223"},"PeriodicalIF":4.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930010","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

Explainable AI-empowered Neuromorphic Computing Framework for Consumer Healthcare 面向消费者医疗保健的可解释人工智能神经形态计算框架

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-05 DOI: 10.1109/tce.2024.3438160

Siva Sai, Shubham Sharma, Vinay Chamola

引用次数: 0

Household Appliance Non-Intrusive Load Monitoring using Alternating Direction Method of Multipliers Based on Relaxation Distance and Neighborhood Search 利用基于松弛距离和邻域搜索的交替方向乘法进行家用电器非侵入式负荷监测

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-08-05 DOI: 10.1109/tce.2024.3438428

Wei Li, Linfeng Yang, Jinbao Jian

引用次数: 0