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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Feng Yi Chi Zhang Shuheng Qiu Wei Liu Meitong Zhang Shupei Li Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo Institute of Materials & Technology and Engineering Ningbo China Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials & Technology and Engineering Ningbo China Faculty of Electrical Engineering Hebei University of Science & Technology Ningbo Institute of Materials & Technology and Engineering Chinese Academy of Sciences Ningbo China Chinese Academy of Sciences Ningbo Institute of Materials & Technology and Engineering Ningbo China

This paper presents a quasi-3D analytical method considering the rotor misalignment for yokeless and segmented armature (YASA) machines. In the proposed method, a detailed subdomain (SD) method dividing the YASA machine into five SDs is utilized to calculate the magnetic field distribution, accounting for the rotor misalignment and slotted effect with limited depth. The entire magnetic field is obtained by applying the interface and boundary conditions. In addition, based on obtained bilateral air-gap flux density, the cogging torque is calculated considering the rotor misalignment. The effectiveness of the proposed method is validated compared with finite element analysis.

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A colorimetric-bionic sensor for multimodal molecular sensing using hierarchical metamaterials

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Device 2025年第5期3卷

作者： Geng, Wenhui Zhou, Ying Huang, Kang Ying, Yibin Xie, Lijuan College of Biosystems Engineering and Food Science Zhejiang University 866 Yuhangtang Road Hangzhou 310058 China The National Key Laboratory of Agricultural Equipment Technology Beijing 100083 China Zhejiang Key Laboratory of Intelligent Sensing and Robotics for Agriculture Hangzhou 310058 China Hangzhou Customs Technology Center 398 Jianshe San Road Xiaoshan District Hangzhou City China Department of Biological Systems Engineering Washington State University Pullman 99164 WA United States

Metamaterials can enhance a local electric field for molecular sensing. However, the difficulty in specifically recognizing target molecules and integrating multiple functionalities restricts their development. Here, we present a hierarchical metamaterial-based colorimetric-bionic sensor (HMm-CBs) using a layer-by-layer assembly method for multimodal molecular sensing. The CBs layers offer molecular recognition ability by enabling the specific perception of analytes, and the HMm layers provide coupling of vibrations between analyte molecules at the terahertz (THz) frequency. The HMm-CBs have broadband adaptability in controlling electromagnetic waves from optical to THz frequency. As a proof of principle, we designed a sensor for analyzing tetracycline hydrochlorides (TCHs). The sensor, in conjunction with a back propagation neural network (BPNN), identifies four types of TCHs. As a demonstration, a multichannel indicator is created for real-time monitoring of shrimp freshness and volatile metabolites. © 2025 The Author(s)

关键词： antibiotics carbon nanotubes chemometric algorithms colorimetric sensor array DTI-3: Develop metamaterials

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Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

arXiv

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arXiv 2023年

作者： Kang, Jiawen He, Jiayi Du, Hongyang Xiong, Zehui Yang, Zhaohui Huang, Xumin Xie, Shengli The School of Automation Guangdong University of Technology Guangzhou510006 China The Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China Zhejiang Laboratory Hangzhou31121 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore

For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse applications to achieve this goal. SemCom enables high-quality and ultra-efficient vehicular communication, even with explosively increasing data traffic among vehicles. In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool. The global and local metaverse are brand-new concepts from the metaverse’s distribution standpoint. Considering the QoS of users, this article explores the potential security vulnerabilities of the proposed framework. To that purpose, this study highlights a specific security risk to the framework’s SemCom module and offers a viable defense solution, so encouraging community researchers to focus more on vehicular metaverse security. Finally, we provide an overview of the open issues of secure SemCom in the vehicular metaverses, notably pointing out potential future research directions. © 2023, CC BY.

关键词： Quality of service

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Analysis Motion Imagination EEG Signal in Spatiotemporal-energy domain 2019

Analysis Motion Imagination EEG Signal in Spatiotemporal-ene...

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Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

作者： Zhengyan Sheng Xiao Yao Ruixuan Chen Wenyu Diao Weiliang Li Yao Li The College of IoT Engineering Hohai University China The College of IoT Engineering Hohai University China Changzhou Key Laboratory of Robotics and Intelligent Technology China

ISBN: (纸本)9781450376273

Brain is the most advanced part of the nervous system. As an emerging neighborhood exploring the brain, the brain-computer interface (BCI) may completely influence people's current communication and lifestyle in the future. We propose an architecture for feature extraction and classification model in brain-computer interface (BCI). Three features of EEG signal are extracted and combined in an attempt to describe the EEG signal more comprehensively including Wavelet packet decomposition, Information entropy and Co-space pattern (CSP). We construct the C-LSTM model combining the Convolutional Neural Networks (CNN) and the Long Short-Term Memory (LSTM) to perform the classification. Comparison was made with traditional approaches, and results show the method has a better performance for classification of motor imagery.

关键词： EEG signal LSTM Brain-computer interface Co-space pattern

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Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

arXiv

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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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Efficient Network Compression Through Smooth-Lasso Constraint

Efficient Network Compression Through Smooth-Lasso Constrain...

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International Conference on Communications, Information System and Computer engineering (CISCE)

作者： Xiaowei Ye Ning Xu Xiaofeng Liu Xiao Yao Aiming Jiang The College of IoT Engineering Hohai University Changzhou China Key Laboratory of Robotics and Intelligent Technology College of IOT Engineering Hohai University Changzhou China

ISBN: (数字)9781728197616

ISBN: (纸本)9781728197623

The powerful capabilities of deep convolutional neural networks make them useful in various fields. However, most edge devices are difficult to afford the huge amount of parameters and high computational cost. Therefore, it is highly imperative to compress these huge models to make them lightweight to enable real-time inference on edge devices. Channel pruning is a mainstream method of network compression. Generally, the Lasso constraint is imposed on the scaling factor in the batch normalization layer to make them tend to zero for selecting unimportant channels and then prune them. However, Lasso is a non-smooth function that is not derivable at zero, we experimentally find that when the value of the loss function is small, it is difficult to decline continuously. Aiming at the above problems, this paper proposes a pruning strategy based on the derivable function Smooth-Lasso, using Smooth-Lasso as a regularization constraint to perform sparse training and then prune the network. Experiments on benchmark datasets and convolutional networks show that our method can not only make the loss function converge quickly, but also save more storage space and computational cost than the baseline method while maintaining the same level of accuracy as the original network.

关键词： Conferences Computer vision Information processing Pattern recognition Training Computer science Germanium

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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5888-5903页

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Qilu University of Technology Shandong Academy of Sciences Faculty of Computer Science and Technology Jinan 250014 China National University of Singapore School of Computing 21 Lower Kent Ridge Rd 119077 Singapore Great Bay University School of Computing and Information Technology Dongguan 523000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Qilu University of Technology (Shandong Academy of Sciences) Shandong Artificial Intelligence Institute Jinan 250014 China Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin 300384 China Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Shenzhen 518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. © 2005-2012 IEEE.

关键词： CLIP Diffusion face forgery detection Image-noise fusion Sample pair attention Transformer

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When Shannon Meets Nonstochastic Information: Communication and Estimation

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IEEE Transactions on Automatic Control 2025年

作者： Zhang, Wentao Zuo, Zhiqiang Zhao, Rui Wang, Yijing Hu, Guoqiang Zhang, Hui Wang, Yaonan Hunan University School of Robotics Changsha410082 China Nanyang Technological University Continental-NTU Corporate Lab Singapore639798 Singapore Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore National Engineering Research Center for Robot Visual Perception and Control Changsha410082 China Hunan University College of Electrical and Information Engineering Changsha410082 China

This notes focuses on the issue of whether or not the state of a plant can be estimated when the inputs of communication channel involve noise and disturbance simultaneously. It turns out to calculate the maximum capacity of stationary memoryless multiple quasi-access channel under nonstochastic information theory. It is shown that the maximum information rate over such a channel is determined by a convex hull spanned by the zero-error capacities which are specified on the underlying sample spaces. Moreover, the state of a plant could be estimated provided that the maximal information rate is strictly greater than the topology entropy. © 1963-2012 IEEE.

关键词： Communication channels (information theory)

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Fundus-Image-Based Retinal Vascular Analysis in Myopic Eyes 42

Fundus-Image-Based Retinal Vascular Analysis in Myopic Eyes

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42nd Chinese Control Conference, CCC 2023

作者： Shen, Jichen Zou, Haohan Wang, Yan Liu, Jinchao Yu, Ningbo College of Artificial Intelligence Nankai University Tianjin300350 China Clinical College of Ophthalmology Tianjin Medical University Tianjin300070 China Tianjin Key Lab of Ophthalmology and Visual Science Tianjin Eye Institute Tianjin Eye Hospital Nankai University Affiliated Eye Hospital Tianjin300020 China Nankai University Eye Institute Nankai University Tianjin300350 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

ISBN: (纸本)9789887581543

Myopia is prevalent worldwide and it is of great significance to prevent, control and intervene myopia in an early stage. The etiopathogenesis of myopia remains unclear and needs further research. Fundus photographs can show biomarkers and lesions of the retina in a non-invasive way directly. To discover some potential changes in myopic eyes, we extracted the retinal vascular features from vessel segmented results, and calculated the Spearman correlation coefficients between the retinal vascular features and the spherical refractive errors. The Kruskal-Wallis test was applied to explore the difference exists in the characteristics of retinal vessels among the mild, moderate and high myopia groups. The statistical analysis results indicated that the higher the degrees of myopia, the smaller the retinal vessels. This study found vascular changes in myopic eyes through fundus images, which could help to explain the mechanism of myopia and the damage caused by myopia. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Ophthalmology

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