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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Meng, Lubin Jiang, Xue Jia, Tianwang Wu, Dongrui School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan China

ISBN: (纸本)9781665410205

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation, text input, games, and so on. However, EEG signals inherently carry rich personal information, necessitating privacy protection. This paper demonstrates that multiple types of private information (user identity, gender, and BCI-experience) can be easily inferred from EEG data, imposing a serious privacy threat to BCIs, To address this issue, we design perturbations to convert the original EEG data into privacy-protected EEG data, which conceal the private information while maintaining the primary BCI task performance. Experimental results demonstrated that the privacy-protected EEG data can significantly reduce the classification accuracy of user identity, gender and BCI-experience, but almost do not affect at all the classification accuracy of the primary BCI task, enabling user privacy protection in EEG-based BCIs. © 2024 IEEE.

关键词： Differential privacy

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Cognitive Navigation for intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1933-1943页

作者： Qiming Liu Xinru Cui Zhe Liu Hesheng Wang Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Laboratory of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong UniversityShanghai 200240China

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory *** introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity *** tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual ***,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy *** from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory *** validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

关键词： Graph neural networks(GNNs) spatial memory topological map visual navigation

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Combined UAMP and MF Message Passing Algorithm for Multi-Target Wideband DOA Estimation with Dirichlet Process Prior

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Tsinghua Science and Technology 2024年第4期29卷 1069-1081页

作者： Shanwen Guan Xinhua Lu Ji Li Rushi Lan Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic TechnologyGuilin 541004China Guilin Huigu Institute of AI Industrial Technology Guilin 541004China School of Information Engineering Nanyang Institute of TechnologyNanyang 473000China

When estimating the direction of arrival (DOA) of wideband signals from multiple sources, the performance of sparse Bayesian methods is influenced by the frequency bands occupied by signals in different directions. This is particularly true when multiple signal frequency bands overlap. Message passing algorithms (MPA) with Dirichlet process (DP) prior can be employed in a sparse Bayesian learning (SBL) framework with high precision. However, existing methods suffer from either high complexity or low precision. To address this, we propose a low-complexity DOA estimation algorithm based on a factor graph. This approach introduces two strong constraints via a stretching transformation of the factor graph. The first constraint separates the observation from the DP prior, enabling the application of the unitary approximate message passing (UAMP) algorithm for simplified inference and mitigation of divergence issues. The second constraint compensates for the deviation in estimation angle caused by the grid mismatch problem. Compared to state-of-the-art algorithms, our proposed method offers higher estimation accuracy and lower complexity.

关键词： wideband direction of arrival(DOA)estimation sparse Bayesian learning(SBL) unitary approximate message passing(UAMP)algorithm Dirichlet process(DP)

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Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

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Chinese Journal of Electronics 2024年第3期33卷 814-822页

作者： Junqi LIU Tao WEN Guo XIE Yuan CAO Clive Roberts Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology The School of Electronic and Information Engineering Beijing Jiaotong University Birmingham Centre for Railway Research and Education University of Birmingham

The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and *** have studies considered diverse noises along the *** fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple *** decomposes condition monitoring signals after coarse-grained processing in varying *** this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single *** the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the *** adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关键词： Railway point machines Fault diagnosis Multi-time-scale Variational mode decomposition

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Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 547-555页

作者： Kun Zhu Chengpu Yu Yiming Wan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Institute of Technology Chongqing Innovation Center

In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output *** objective is to enhance parameter estimation performance under non-persistent *** proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is *** proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite ***,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed *** simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

关键词： Non-persistent excitation oblique projection recursive least squares(RLS) variable-direction forgetting(VDF)

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HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation

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Chinese Journal of Electronics 2023年第1期32卷 189-198页

作者： GUAN Qi SHENG Zihao XUE Shibei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

Real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely high-resolution 6D pose estimation network(HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.

关键词： Knowledge engineering Computational modeling Pose estimation Grasping Benchmark testing Feature extraction Real-time systems

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Dynamic Color Transform Networks for Wheat Head Detection

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Plant Phenomics 2022年第1期4卷 395-408页

作者： Chengxin Liu Kewei Wang Hao Lu Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of EducationSchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina

Wheat head detection can measure wheat traits such as head density and head *** wheat breeding largely relies on manual observation to detect wheat heads,yielding a tedious and inefficient *** emergence of affordable camera platforms provides opportunities for deploying computer vision(CV)algorithms in wheat head detection,enabling automated measurements of wheat *** wheat head detection,however,is challenging due to the variability of observation circumstances and the uncertainty of wheat head *** this work,we propose a simple but effective idea—dynamic color transform(DCT)—for accurate wheat head detection.

关键词： Wheat breeding wheat

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Cross-user Gesture Recognition Based on Deep Domain Adaptation Networks 43

Cross-user Gesture Recognition Based on Deep Domain Adaptati...

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43rd Chinese control Conference, CCC 2024

作者： Fu, Zhongzheng He, Xinrun Wang, Haoyuan Chen, Xingjian Li, Yixuan Huang, Jian Huazhong University of Science and Technology Ministry of Education Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

This study introduces an innovative approach for gesture recognition in smart wearable devices using a deep domain adaptation model, focusing on the challenges posed by heterogeneous user environments and the need for precise, adaptable, and personalized gesture recognition systems. Traditional methods based on machine learning and deep learning techniques, while effective, struggle with the variance in data distribution among different users, leading to accuracy challenges. To address this, the study proposes the Domain Kernel-Alignment Adaptation Network (DKAN) model, utilizing a new Kernel-Aligned Multi-kernel Maximum Mean Discrepancy (KAMMD) method within deep networks. By calculating kernel alignments between source and target domains and adjusting weights of Gaussian kernels accordingly, DKAN emphasizes features conducive to transfer learning, enabling more accurate cross-user gesture recognition. The model achieves a significant average accuracy of 97.3% across 21 gestures, highlighting its potential in practical applications. This advancement addresses challenges in traditional gesture recognition methods and sets a new direction for future research in smart wearable technology. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Adversarial machine learning

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A Survey on Negative Transfer

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 305-329页

作者： Wen Zhang Lingfei Deng Lei Zhang Dongrui Wu the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China IEEE the School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China

Transfer learning(TL)utilizes data or knowledge from one or more source domains to facilitate learning in a target *** is particularly useful when the target domain has very few or no labeled data,due to annotation expense,privacy concerns,***,the effectiveness of TL is not always *** transfer(NT),i.e.,leveraging source domain data/knowledge undesirably reduces learning performance in the target domain,and has been a long-standing and challenging problem in *** approaches have been proposed in the literature to address this ***,there does not exist a systematic *** paper fills this gap,by first introducing the definition of NT and its causes,and reviewing over fifty representative approaches for overcoming NT,which fall into three categories:domain similarity estimation,safe transfer,and NT *** areas,including computer vision,bioinformatics,natural language processing,recommender systems,and robotics,that use NT mitigation strategies to facilitate positive transfers,are also ***,we give guidelines on NT task construction and baseline algorithms,benchmark existing TL and NT mitigation approaches on three NT-specific datasets,and point out challenges and future research *** ensure reproducibility,our code is publicized at https://***/chamwen/NT-Benchmark.

关键词： Domain adaptation domain similarity negative transfer positive transfer transfer learning

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Attention-stacking adaptive fuzzy neural networks for autism diagnosis

Attention-stacking adaptive fuzzy neural networks for autism...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Shuzhan Xiao, Lei Huang, Hui Hu, Zhongyi Wenzhou University Institute of Intelligent Information System Wenzhou University Key Laboratory of Intelligent Image Processing and Analysis College of Computer Science and Artificial Intelligence Wenzhou China

ISBN: (纸本)9798350386226

The current diagnostic methods for Autism Spectrum Disorder (ASD) based on Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) face two significant challenges. Firstly, the functional connectivity networks (FCNs) of the brain are subject to uncertainty due to noise interference during the rs-fMRI acquisition process. Secondly, variations in equipment parameters across different imaging sites lead to diverse distributions of collected features. To address these issues, this paper introduces a novel Multi-Task Stacked Adaptive Fuzzy Neural Network (AT-STTSK) specifically designed for Autism Spectrum Disorder (ASD) classification. The proposed network seamlessly integrates a Multi-Output TSK (MO-TSK) fuzzy system, a Stacked Autoencoder (SAE), and a Transformer. This integration aims to reduce the impact of uncertainty factors, enhance the model's interpretability, and leverage the multi-head attention mechanisms to capture both local and global information, thus strengthening feature learning. Additionally, we explore the utilization of a Contrastive Learning (CL) strategy to further optimize the model. By incorporating these advancements, our AT-STTSK network offers a promising approach for improving the accuracy and reliability of ASD diagnosis based on rs-fMRI data. The validity of the method in this paper is verified on ABIDE dataset, and the result shows that the accuracy rate can reach 85.7% at most. © 2024 IEEE.

关键词： Diseases

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