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arXiv 2024年

作者： Peng, Ruimin An, Jiayu Wu, Dongrui 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

Electroencephalogram (EEG)-based seizure subtype classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutualSHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Divergence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification. © 2024, CC BY.

关键词： Self-supervised learning

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Late Better than Early: A Decision-Level Information Fusion Approach for Rgb-Thermal Crowd Counting with Illumination Awareness

SSRN

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SSRN 2024年

作者： Cheng, Jian Feng, Chen Xiao, Yang Cao, Zhiguo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

In this paper, we make the first research effort to address the RGB-Thermal (RGB-T) crowd counting problem with decision-level late fusion manner. Being different from the existing pixel-level or feature-level fusion methods, our proposition chooses to fuse the density maps yielded by RGB and thermal counterparts via spatially adaptive weighting with RGB illumination-aware attention. Our key intuition to conduct RGB-T density map fusion lies in 3 main folders. First, compared with the raw RGB-T images or convolutional feature maps, RGB-T density maps contain stronger counting-wise semantic meanings. Secondly, they are also of high spatial resolution for revealing fine local details. Thirdly, RGB-T density maps are complementary to each other for effective counting pattern characterization. To fuse them adaptively, a spatial weighting map is generated concerning the issues of RGB illumination awareness and local counting pattern characterization ability jointly. To the best of our knowledge, we are the first to leverage RGB-T crowd counting via concerning these 2 issues in a unified way. Meanwhile, cross-modality feature interaction is conducted between RGB and thermal modalities to facilitate spatial weighting map generation. The experiments on 2 well-established RGB-T crowd counting datasets, i.e., RGBT-CC and DroneRGBT) verify the superiority of our proposition. The source code and pretrained models will be released upon acceptance at https://***/hustaia/DLF-IA. © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Comparison of Large-Scale MOEAs with Informed Initialization for Voltage Transformer Ratio Error Estimation 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Li, Lianghao He, Cheng Li, Hongbin Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications. The large number of decision variables in LSMOP leads to a tremendous high-dimensional search space, which is still challenging for existing multiobjective evolutionary algorithms (MOEAs). The voltage transformer ratio error estimation (TREE) problem is a typical LSMOP in real-world applications. A number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been adopted for solving the TREE problem, but so far there is still considerable potential for improvement in their results. In this paper, we propose a informed initialization for solving the TREE problem. The physical properties of the TREE problem are used to construct an initial set of solutions with specific characteristics. The proposed initialization method is tested against the default initialization method on five TREE problems by being applied on six state-of-the-art LSMOEAs and a classical MOEA. The experimental results demonstrate that the initialization method has a significant effect on the performance of LSMOEAs in solving the TREE problems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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A Point Cloud Segmentation Method Based on Ground Point Cloud Removal and Multi-Scale Twin Range image 42

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

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42nd Chinese control Conference, CCC 2023

作者： Zhou, Yicheng Peng, Gang Duan, Hangqi Wu, Zhangang Zhu, Xukang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Shantui Construction Machinery Co. Ltd. Jining272073 China

ISBN: (纸本)9789887581543

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 objects. Based 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 points. On 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. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： image enhancement

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Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition

arXiv

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arXiv 2024年

作者： Xu, Yifan Jiang, Xue Wu, Dongrui 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

Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evaluators are usually needed for each affective sample to obtain its ground-truth label, which is expensive. To save the labeling cost, this paper proposes an inconsistency-based active learning approach for cross-task transfer between emotion classification and estimation. Affective norms are utilized as prior knowledge to connect the label spaces of categorical and dimensional emotions. Then, the prediction inconsistency on the two tasks for the unlabeled samples is used to guide sample selection in active learning for the target task. Experiments on within-corpus and cross-corpus transfers demonstrated that cross-task inconsistency could be a very valuable metric in active learning. To our knowledge, this is the first work that utilizes prior knowledge on affective norms and data in a different task to facilitate active learning for a new task, even the two tasks are from different datasets. © 2024, CC BY.

关键词： Active learning

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A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding

arXiv

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arXiv 2024年

作者： Chen, Xiaoqing Jia, Tianwang Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

An electroencephalogram (EEG) based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, EEG-based BCIs face at least three major challenges in real-world applications: data scarcity and individual differences, adversarial vulnerability, and data privacy. While previous studies have addressed one or two of these issues, simultaneous accommodation of all three challenges remains challenging and unexplored. This paper fills this gap, by proposing an Aligned and Augmented Adversarial Ensemble (A3E) algorithm and integrating it into three privacy protection scenarios (centralized source-free transfer, federated source-free transfer, and source data perturbation), achieving simultaneously accurate decoding, adversarial robustness, and privacy protection of EEG-based BCIs. Experiments on three public EEG datasets demonstrated that our proposed approach outperformed over 10 classic and state-of-the-art approaches in both accuracy and robustness in all three privacy-preserving scenarios, even outperforming state-of-the-art transfer learning approaches that do not consider privacy protection at all. This is the first time that three major challenges in EEG-based BCIs can be addressed simultaneously, significantly improving the practicalness of EEG decoding in real-world BCIs. © 2024, CC BY.

关键词： Transfer learning

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Passivity-Based Robust control Against Quantified False Data Injection Attacks in Cyber-Physical Systems

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1440-1450页

作者： Yue Zhao Ze Chen Chunjie Zhou Yu-Chu Tian Yuanqing Qin Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Computer Science Queensland University of TechnologyBrisbane QLD 4001Australia

Secure control against cyber attacks becomes increasingly significant in cyber-physical systems(CPSs).False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false data such as sensor measurements and control *** quantified false data injection attacks,this paper establishes an effective defense framework from the energy conversion ***,we design an energy controller to dynamically adjust the system energy changes caused by unknown *** designed energy controller stabilizes the attacked CPSs and ensures the dynamic performance of the system by adjusting the amount of damping ***,with the disturbance attenuation technique,the burden of control system design is simplified because there is no need to design an attack *** addition,this secure control method is simple to implement because it avoids complicated mathematical *** effectiveness of our control method is demonstrated through an industrial CPS that controls a permanent magnet synchronous motor.

关键词： Cyber-physical systems energy controller energy conversion false data injection attacks L2 disturbance attenuation technology

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and Machine intelligence 2025年 PP卷 PP页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： image-Text Matching Medical Report Generation Sample-graph Consistency Self-boosting framework

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An improved estimation of distribution algorithm for multi-compartment electric vehicle routing problem

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Journal of Systems Engineering and Electronics 2021年第2期32卷 365-379页

作者： SHEN Yindong PENG Liwen LI Jingpeng Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computing Science and Mathematics University of StirlingStirling FK94LAUK

The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process,are employed.A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost,distribution cost,time window penalty cost and charging service *** solve the problem,an estimation of the distribution algorithm based on Lévy flight(EDA-LF)is proposed to perform a local search at each iteration to prevent the algorithm from falling into local *** results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA *** addition,when comparing with existing algorithms,the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale *** experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles.

关键词： multi-compartment vehicle routing problem electric vehicle routing problem(EVRP) soft time window multiple charging type estimation of distribution algorithm(EDA) Lévy flight

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Circular Target Attack-Defense Stackelberg Game for Multi-Agents Systems

Circular Target Attack-Defense Stackelberg Game for Multi-Ag...

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第35届中国过程控制会议

作者： Ning Xing Hai-Tao Zhang Yaozhong Zheng School of Artificial Intelligence and Automation Engineering Research Center of Autonomous Intelligent Unmanned Systems Key Laboratory of Image Processing and Intelligent Control State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and Technology

In this paper, the problem of guarding a circular area is proposed and solved using a Stackelberg differential game theoretic approach. The objective of the attacker is to breach the perimeter of the defended area, while the defenders endeavor to thwart such attempts. The dynamics of the attack-defense game are modeled according to the distance and position relations among defenders, attackers, and the center of defense area. The optimal Stackelberg equilibrium control strategies for both defenders and attackers are designed to guarantee the defense mission's success. Then, the effectiveness of the proposed method is validated through numerical simulation.

关键词： Multi-agents system Stackelberg differential game graphical game

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