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arXiv 2025年

作者： Wang, Ziwei Li, Siyang Chen, Xiaoqing Li, Wei Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

Objective: An electroencephalography (EEG)-based brain-computer interface (BCI) serves as a direct communication pathway between the human brain and an external device. While supervised learning has been extensively explored for motor imagery (MI) EEG classification, small data quantity has been a key factor limiting the performance of deep feature learning. Methods: This paper proposes a knowledge-driven time-space-frequency based multi-view contrastive network (MVCNet) for MI EEG decoding in BCIs. MVCNet integrates knowledge from the time, space, and frequency domains into the training process through data augmentations from multiple views, fostering more discriminative feature learning of the characteristics of EEG data. We introduce a cross-view contrasting module to learn from different augmented views and a cross-model contrasting module to enhance the consistency of features extracted between knowledge-guided and data-driven models. Results: The combination of EEG data augmentation strategies was systematically investigated for more informative supervised contrastive learning. Experiments on four public MI datasets and three different architectures demonstrated that MVCNet outperformed 10 existing approaches. Significance: Our approach can significantly boost EEG classification performance beyond designated networks, showcasing the potential to enhance the feature learning process for better EEG decoding. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Boosting Explore-Exploit Behavior for Navigating Vehicle by Maintaining Informative Topological Frontier

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-11页

作者： Liu, Qiming Cui, Xinru Liu, Zhe Wang, Hesheng Department of Automation Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

Target search in unknown environments places high demands not only on an autonomous vehicle's ability to perceive and interpret target cues, but also on its conscious of collecting these cues by active exploration. While existing navigation methods have successfully built target-driven policies by maintaining memory of explored areas, there has been a lack of focus on facilitating target-aware exploration-the informative frontier information at unexplored yet visible areas is often overlooked. In this paper, we introduce a novel topology-based memory structure, Frontier-enhanced Topological Memory (FTM), and a Hierarchical Topology Encoding and Extraction (HTEE) module, fostering the autonomous vehicle's awareness of both environmental exploration and target approach. Specifically, FTM innovatively incorporates informative ghost nodes on traditional topological map to represent unexplored yet visible regions. We leverage an online-trained implicit scene representation to estimate the positions and generate features of these ghost nodes. The HTEE then employs implicit graph convolutions and attention mechanisms to extract cognitive information from FTM, taking into account the hierarchical memory structure, target cues, and current state. Our design bolsters cognitive navigation decisions. The experiments in the high-fidelity environments, including performance tests, visualizations, and interpretability experiments, validate the effectiveness of our approach in enhancing the vehicle's exploratory behavior. The improved exploration awareness for target cue collection, in turn, enhances the success rate and path efficiency of target search. Furthermore, we demonstrate the adaptability of our algorithm in real-world physical environments. IEEE

关键词： Data mining

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Energy Management Strategies for PEMFC-Lithium Battery Hybrid Power Systems Based on Pt Degradation

Energy Management Strategies for PEMFC-Lithium Battery Hybri...

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第42届中国控制会议

作者： Ziang Guo Beijia Li Linghong Zeng Jun Fu Chuang Sheng Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology Shenzhen Huazhong University of Science and Technology Research Institute

Proton Exchange Membrane Fuel Cells(PEMFCs) are prone to decreased lifespan due to the degradation of the platinum(Pt) catalyst during *** this study,we have established a one-dimensional model to investigate the effects of different voltage conditions on Pt *** potential is found to be the most significant factor affecting the *** exposure to high potentials will significantly reduce the PEMFC's lifespan,and frequent voltage changes will exacerbate the *** mitigate this issue,we have designed three energy management strategies with different limitations and simulated their performance in the operating scenario of a fuel-cell hybrid electric vehicle(FCHEV).The results demonstrate that strategies restricting high potential occurrences and voltage variations can effectively alleviate Pt degradation.

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Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

arXiv

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arXiv 2024年

作者： Chen, Xiaoqing Wang, Ziwei Wu, Dongrui The Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions to BCIs degrade the classification accuracy on benign samples. This phenomenon greatly affects the applicability of adversarial defense approaches to EEG-based BCIs. To mitigate this problem, we propose alignment-based adversarial training (ABAT), which performs EEG data alignment before adversarial training. Data alignment aligns EEG trials from different domains to reduce their distribution discrepancies, and adversarial training further robustifies the classification boundary. The integration of data alignment and adversarial training can make the trained EEG classifiers simultaneously more accurate and more robust. Experiments on five EEG datasets from two different BCI paradigms (motor imagery classification, and event related potential recognition), three convolutional neural network classifiers (EEGNet, ShallowCNN and DeepCNN) and three different experimental settings (offline within-subject cross-block/-session classification, online cross-session classification, and pre-trained classifiers) demonstrated its effectiveness. It is very intriguing that adversarial attacks, which are usually used to damage BCI systems, can be used in ABAT to simultaneously improve the model accuracy and robustness. © 2024, CC BY.

关键词： Adversarial machine learning

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A Physiological Signal Emotion Recognition Method Based on Domain Adaptation and Incremental Learning

A Physiological Signal Emotion Recognition Method Based on D...

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Cyber-Energy Systems and intelligent Energy (ICCSIE), International Conference on

作者： Junnan Li Xiaoping Wang the School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan P. R. China

Temporal concept shift (TCS) is an unavoidable problem in physiological signal-based emotion recognition tasks, i.e., the data distribution of physiological signals is constantly changing over time, which gradually degrades the model accuracy. To this end, we propose a method based on a combination of domain adaptation and incremental learning to reduce the impact of temporal concept drift. In this paper, domain adaptation is used to reduce the distribution differences and incremental learning is used to prevent the learned knowledge from being forgotten. Finally, we validate the effectiveness of our approach on two real datasets.

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A novel method of constructing high-dimensional digital chaotic systems on finite-state automata

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Chinese Physics B 2020年第9期29卷 233-243页

作者： Jun Zheng Han-Ping Hu School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

When chaotic systems are implemented on finite precision machines, it will lead to the problem of dynamical degradation. Aiming at this problem, most previous related works have been proposed to improve the dynamical degradation of low-dimensional chaotic maps. This paper presents a novel method to construct high-dimensional digital chaotic systems in the domain of finite computing precision. The model is proposed by coupling a high-dimensional digital system with a continuous chaotic system. A rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney's definition of chaos. Numerical experimental results for different high-dimensional digital systems indicate that the proposed method can overcome the degradation problem and construct high-dimensional digital chaos with complicated dynamical properties. Based on the construction method, a kind of pseudorandom number generator (PRNG) is also proposed as an application.

关键词： high-dimensional digital chaotic system dynamical degradation anti-control pseudorandom number generator

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HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition

arXiv

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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Zuo, Zhengrong Gao, Changxin Jin, Rong Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Twitter United States

Few-shot action recognition is a challenging but practical problem aiming to learn a model that can be easily adapted to identify new action categories with only a few labeled samples. Recent attempts mainly focus on learning deep representations for each video individually under the episodic meta-learning regime and then performing temporal alignment to match query and support videos. However, they still suffer from two drawbacks: (i) learning individual features without considering the entire task may result in limited representation capability, and (ii) existing alignment strategies are sensitive to noises and misaligned instances. To handle the two limitations, we propose a novel Hybrid Relation guided temporal Set Matching (HyRSM++) approach for few-shot action recognition. The core idea of HyRSM++ is to integrate all videos within the task to learn discriminative representations and involve a robust matching technique. To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric. Given the basic representations from the feature extractor, the hybrid relation module is introduced to fully exploit associated relations within and cross videos in an episodic task and thus can learn task-specific embeddings. Subsequently, in the temporal set matching metric, we carry out the distance measure between query and support videos from a set matching perspective and design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. In addition, we explicitly exploit the temporal coherence in videos to regularize the matching process. In this way, HyRSM++ facilitates informative correlation exchanged among videos and enables flexible predictions under the data-limited scenario. Furthermore, we extend the proposed HyRSM++ to deal with the more challenging semi-supervised few-shot action recognition and unsupervised few-shot action recognition tasks. Experimental results on multiple benchmark

关键词： Benchmarking

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A Point Cloud Segmentation Method Based on Ground Point Cloud Removal and Multi-scale Twin Range image

A Point Cloud Segmentation Method Based on Ground Point Clou...

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第42届中国控制会议

作者： Yicheng Zhou Gang Peng Hangqi Duan Zhangang Wu Xukang Zhu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Shantui Construction Machinery Co. Ltd.

Aiming at the shortcomings of traditional point cloud segmentation algorithms, this paper proposes a 3D lidar point cloud segmentation method combining ground filtering and multi-scale twin range image construction. This method fully considers the geometric space distribution characteristics of the point cloud and the randomness of the positions between *** on the assumption of multi plane model, the point cloud is divided into sectors. The ground points are removed through region ground plane fitting and growth, and then the multi-scale twin range image is constructed according to non-ground *** the basis of the twin range image, the Breadth First Search algorithm is improved, the depth-breadth joint search strategy is proposed, all the points to be clustered are traversed, and the adaptive clustering threshold is used for point cloud clustering,which realizes a point cloud segmentation method with higher accuracy and robustness. Through the experimental analysis of KITTI dataset and collected campus outdoor scene dataset, compared with the traditional point cloud segmentation algorithm,the method proposed in this paper can effectively improve the point cloud segmentation accuracy and robustness of mobile robots in complex environments.

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Lifetime Extension control for Multi-Stack SOFC System with Inconsistent Stacks

Lifetime Extension Control for Multi-Stack SOFC System with ...

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第42届中国控制会议

作者： Zexin Li Guoqiang Liu Wei Zhao Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science & Technology Shenzhen Huazhong University of Science and Technology Research Institute

The multi-stack fuel cell system(MFCS) is an important basis for the application of solid oxide fuel cell(SOFC)technology to large-scale power generation *** can provide higher power,longer lifetime and better *** from the single-stack fuel cell system(SFCS),there may be some inconsistencies among the stacks in the *** the inconsistencies are ignored,they will significantly affect the lifetime of the *** study focuses on the lifetime extension of multi-stack SOFC power generation systems with inconsistent *** the multi-stack system of SOFC generally cannot control the flow rate of each stack respectively,it brings a difficulty to the control of the MFCS with inconsistencies among the *** order to overcome this difficulty,this paper built a multi-stack SOFC system model,which can characterize the degradation and inconsistencies among SOFC *** then designed a power distribution strategy based on fuzzy control to extend the lifetime of the *** strategy was verified by *** simulation results show that the strategy can effectively control the inconsistencies among the stacks and significantly extend the lifetime of the MFCS.

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Cascading Failures in Interdependent Directed Networks Under Localized Attacks

SSRN

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SSRN 2023年

作者： Lv, Mengyu Pan, Linqiang Liu, Xueming Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Many real-world systems interact with one another through dependency links, which reduces the system robustness. Most previous studies on the robustness of interdependent networks focus on undirected networks, and the related studies on directed networks are limited to random or targeted attacks. However, some failure scenarios can not be described by these two kinds of attacks, such as earthquakes, floods, and epidemics, where systems are attacked in a local range. In this work, we develop a theoretical framework for analyzing the robustness of interdependent directed networks under localized attacks. We find that for degree homogeneous networks, network robustness under localized attacks is similar to that under random attacks. There are four phase transitions in the phase diagram of the network, and a four-phase transition point and a two-phase transition point are found. For degree heterogeneous networks, localized attacks are more likely to lead to collapse than random attacks. As the coupling strength between networks increases, the interdependent networks first show a second order phase transition, and then a hybrid phase transition, and a first order phase transition at last. Furthermore, as the degree heterogeneity decreases, the robustness of networks first increases and then decreases, showing a local robustness maximum. The findings could help understand network robustness and enable better design of robust interdependent systems. © 2023, The Authors. All rights reserved.

关键词： Phase diagrams

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