IEEE Transactions on Cybernetics最新文献_第4页

IEEE Transactions on Cybernetics 电气和电子工程师学会控制论论文集

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-10-30 DOI: 10.1109/TCYB.2024.3482893

引用次数: 0

IEEE SYSTEMS, MAN, AND CYBERNETICS SOCIETY 国际电子工程师学会系统、人和控制论学会

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-10-30 DOI: 10.1109/TCYB.2024.3482891

引用次数: 0

Fast Transfer Learning Method Using Random Layer Freezing and Feature Refinement Strategy. 使用随机层冻结和特征细化策略的快速转移学习法

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-10-30 DOI: 10.1109/TCYB.2024.3483068

Wandong Zhang, Yimin Yang, Thangarajah Akilan, Q M Jonathan Wu, Tianlong Liu

{"title":"Fast Transfer Learning Method Using Random Layer Freezing and Feature Refinement Strategy.","authors":"Wandong Zhang, Yimin Yang, Thangarajah Akilan, Q M Jonathan Wu, Tianlong Liu","doi":"10.1109/TCYB.2024.3483068","DOIUrl":"10.1109/TCYB.2024.3483068","url":null,"abstract":"Recently, Moore-Penrose inverse (MPI)-based parameter fine-tuning of fully connected (FC) layers in pretrained deep convolutional neural networks (DCNNs) has emerged within the inductive transfer learning (ITL) paradigm. However, this approach has not gained significant traction in practical applications due to its stringent computational requirements. This work addresses this issue through a novel fast retraining strategy that enhances applicability of the MPI-based ITL. Specifically, during each retraining epoch, a random layer freezing protocol is utilized to manage the number of layers undergoing feature refinement. Additionally, this work incorporates an MPI-based approach for refining the trainable parameters of FC layers under batch processing, contributing to expedited convergence. Extensive experiments on several ImageNet pretrained benchmark DCNNs demonstrate that the proposed ITL achieves competitive performance with excellent convergence speed compared to conventional ITL methods. For instance, the proposed strategy converges nearly 1.5 times faster than retraining the ImageNet pretrained ResNet-50 using stochastic gradient descent with momentum (SGDM).","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142545173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Human Collaborative Control of Lower-Limb Prosthesis Based on Game Theory and Fuzzy Approximation. 基于博弈论和模糊逼近的下肢假肢人类协同控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-10-30 DOI: 10.1109/TCYB.2024.3483148

Haisheng Xia, Ming Pi, Lingjing Jin, Rong Song, Zhijun Li

{"title":"Human Collaborative Control of Lower-Limb Prosthesis Based on Game Theory and Fuzzy Approximation.","authors":"Haisheng Xia, Ming Pi, Lingjing Jin, Rong Song, Zhijun Li","doi":"10.1109/TCYB.2024.3483148","DOIUrl":"https://doi.org/10.1109/TCYB.2024.3483148","url":null,"abstract":"For leg prosthesis user, the soft tissue and skin under the stump of are not accustomed to weight bearing, excessive continuous contact pressure can lead to the risk of degenerative tissue ulceration. This article presents a novel human-robot collaborative control scheme that achieves control weight self-adjustment for robotic prostheses to minimize interaction torque. To establish the human-robot interaction relationship, we regard the contact pressure between human residual limb and the prosthetic receiving cavity as the interaction force. We aim at reducing the interaction force under the premise of minimally changing the original motion trajectory of the robotic prosthesis. The control scheme mainly includes trajectory optimization based on a dual-agent game control scheme under a cooperative relationship, and a fuzzy logic system for improving the control accuracy of trajectory tracking of robotic prostheses with unknown dynamic parameters. Experiments were carried out on two amputee participants to verify the proposed human-robot interactive control scheme in a robotic prosthesis. The results show that the interaction torque could be reduced while maintaining minimal trajectory tracking error. The proposed control scheme could potentially facilitate the dexterous manipulation of leg prostheses, thus benefiting amputees.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142545174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

A Patch-Based Method for Underwater Image Enhancement With Denoising Diffusion Models. 利用去噪扩散模型的基于补丁的水下图像增强方法

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-10-30 DOI: 10.1109/TCYB.2024.3482174

Haisheng Xia, Binglei Bao, Fei Liao, Jintao Chen, Binglu Wang, Zhijun Li

{"title":"A Patch-Based Method for Underwater Image Enhancement With Denoising Diffusion Models.","authors":"Haisheng Xia, Binglei Bao, Fei Liao, Jintao Chen, Binglu Wang, Zhijun Li","doi":"10.1109/TCYB.2024.3482174","DOIUrl":"https://doi.org/10.1109/TCYB.2024.3482174","url":null,"abstract":"The enhancement of underwater images has emerged as a significant technological challenge in advancing marine research and exploration tasks. Due to the scattering of suspended particles and absorption of light in underwater environments, underwater images tend to present blurriness and predominantly color distortion. In this study, we propose a novel approach utilizing denoising diffusion models to improve underwater degraded images. After training the noise estimation network of the denoising diffusion models, we accelerate the deterministic sampling process with denoising diffusion implicit models. We also propose a patch-based method by implementing average sampling between overlapping image patches at each sampling step, enabling the generation of images at arbitrary resolution while preserving their natural appearance and details. Through benchmark experiments, we illustrate that our method outperforms or closely approaches state-of-the-art techniques in terms of effectiveness and performance. We demonstrate that our approach reduces the interference of underwater environments with the semantic information of the images by salient object detection experiments.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142545171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Neural Network-Based Sliding Mode Control for Semi-Markov Jumping Systems With Singular Perturbation. 基于神经网络的奇异扰动半马尔可夫跳跃系统滑模控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-10-30 DOI: 10.1109/TCYB.2024.3481870

Jun Cheng, Jiangming Xu, Huaicheng Yan, Zheng-Guang Wu, Wenhai Qi

引用次数: 0

Unified Flowing Normality Learning for Rotating Machinery Anomaly Detection in Continuous Time-Varying Conditions 用于连续时变条件下旋转机械异常检测的统一流动正态学习

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-10-29 DOI: 10.1109/tcyb.2024.3481871

Chenye Hu, Jingyao Wu, Chuang Sun, Xuefeng Chen, Asoke K. Nandi, Ruqiang Yan

引用次数: 0

Adjustable Jacobi-Fourier Moment for Image Representation. 用于图像表示的可调雅各比-傅里叶矩。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-10-28 DOI: 10.1109/TCYB.2024.3482352

Jianwei Yang, Xin Yuan, Xiaoqi Lu, Yuan Yan Tang

{"title":"Adjustable Jacobi-Fourier Moment for Image Representation.","authors":"Jianwei Yang, Xin Yuan, Xiaoqi Lu, Yuan Yan Tang","doi":"10.1109/TCYB.2024.3482352","DOIUrl":"10.1109/TCYB.2024.3482352","url":null,"abstract":"The widely adopted Jacobi-Fourier moment (JFM) is limited by its inability to effectively capture spatial information. Although fractional-order JFM ( FOJFM) introduces spatial information through a fractional-order parameter, the control of spatial information remains inadequate. This limitation stems from the insufficient control over zeros distribution associated with the used moment's radial kernel. To address this issue, we generalize both JFM and FOJFM into a transformed JFM. A transformed function with four parameters is designed, and adjustable JFM (AJFM) is proposed. Two parameters correlate to increasing velocities on the left and right parts of the transformed functions, enabling zeros quantities of radial kernel fall in the left and right parts of the interval. The other two parameters segment the transformed function, adjusting regions where different quantities of zeros fall in. This refined control over the radial kernel's zero distribution enhances the versatility of feature extraction by the AJFM, governed by the introduced parameters. Experimental results demonstrate that AJFM, with properly chosen parameters, can emphasize specific regions within an image more effectively.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Sliding Mode Fault-Tolerant Control for Nonlinear High-Order Fully Actuated Systems 非线性高阶全致动系统的滑动模式容错控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-10-25 DOI: 10.1109/tcyb.2024.3482320

Yuqi Jiang, Qian Wang, Guoda Chen, Zhengguang Wu

引用次数: 0

Quantum Few-Shot Image Classification 量子图像分类

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-10-25 DOI: 10.1109/tcyb.2024.3476339

Zhihao Huang, Jinjing Shi, Xuelong Li

引用次数: 0