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4th IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Lv, Junting Liu, Lianguang Zhang, Yutong Li, Depeng Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

ISBN: (纸本)9781665490283

Although deep learning of 3D point clouds has made significant progress, the robustness of 3D models has not been fully investigated. Existing 3D attack methods show satisfactory performance under the white-box setting but frequently suffer from a low transferability to attack black-box models. A key point to improve the transferability of attacks is to find the commonalities of multiple models. To this end, a novel attack method is proposed by only leveraging perturbations in local geometric feature areas termed FAP. Specifically, local perturbations are pertinently added by calculating the curvature to sample more points in the feature areas that 3D models potentially focus on. Moreover, to address the drawback of poor isometric robustness common to 3D models, we propose a generalized framework for diversifying input samples by adding random isometric transformations (DIT) in the attack process, which can be combined with other attack methods to improve their transferability. Evaluated on a variety of typical 3D models, the proposed attack method outperforms the current gradient-based methods in transferable black-box attacks, and the effectiveness of the proposed framework is demonstrated by extending it to other attack methods.

关键词： Deep learning

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Attitude tracking control of spacecraft with preset-time preset-bounded convergence

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2022年第18期32卷 10162-10179页

作者： Zou, An-Min Liu, Yangyang Shantou Univ Coll Engn Dept Elect & Informat Engn Shantou 515063 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Guangdong Prov Key Lab Digital Signal & Image Pro Shantou Peoples R China Shantou Univ Key Lab Intelligent Mfg Technol Minist Educ Shantou Peoples R China

This article addresses the issue of attitude tracking control of rigid spacecraft in the existence of bounded external disturbances. By introducing a smooth time-varying scaling function, a preset-time sliding mode surface is designed. Then, a nonsingular and continuous preset-time preset-bounded attitude tracking control scheme is developed. The distinctive feature of the present control law is that the size of the convergence region and the settling time can be prescribed a priori. In addition, the proposed control scheme exhibits superiority in reducing the magnitude of the applied control torques and saving energy. At last the feasibility of the derived control protocol is illustrated by numerical simulations including a comparative example.

关键词： attitude control preset-time preset-bounded stability rigid spacecraft sliding mode control

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Advancing 3D Mesh Segmentation with Regularization Losses: A Semi-Supervised Approach Using MeshCNN

Advancing 3D Mesh Segmentation with Regularization Losses: A...

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International Conference on Digital Home (ICDH)

作者： Yan Xing Shuaijun Lv Daolun Li Wanliang Zhuang Jieqing Tan Rushi Lan School of Mathematics Hefei University of Technology Hefei China Guangxi Key Lab of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

The application of deep learning in 3D mesh segmentation has attracted increasing attention. In supervisedlearning based mesh segmentation, back propagation is often based on cross entropy loss. Some prior knowledge, such as the structure similarity or geometric consistency of data, can be used to improve the segmentation effect. However, in the literature for mesh segmentation, such prior knowledge is usually either ignored in deep learning or only considered at post-processing stage. In this work, we add prior knowledge as a regularization term into the loss function for optimization, which is applied in the semi-supervised 3D mesh segmentation based on MeshCNN for the first time. A large number of experiments show that, for both semi-supervised segmentation with 30% and 70% labeled data and fully supervised segmentation, the proposed regularization term improves the segmentation performance.

关键词： Deep learning Backpropagation Three-dimensional displays Propagation losses Entropy Optimization

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Time-Varying Formation control of Autonomous Surface Vehicles Based on Affine Observer

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第10期71卷 12952-12963页

作者： Tang, Chuancong Zhang, Hai-Tao Cao, Haosen Wang, Jun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Engn Res Ctr Autonomous Intelligent Unmanned Sys Chinese Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Guangdong HUST Ind Technol Res Inst Dongguan 523808 Peoples R China Guangdong Prov Key Lab Mfg Equipment Digitizat Dongguan 523808 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Engn Res Ctr Autonomous Intelligent Unmanned Sys Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

This article addresses an affine-formation tracking control (AFTC) of underactuated autonomous surface vehicles (ASVs). A distributed observer is developed to estimate desired formation positions via an affine transformation. A trajectory tracking controller with prescribed-performance is designed to steer the ASV fleet with a time-varying formation pattern. Sufficient conditions are derived to guarantee the asymptotical stability of the closed-loop system governed by AFTC. Both numerical simulation and experimental results with five ASVs on a platform are elaborated to substantiate the effectiveness and superiority of the proposed AFTC method for achieving time-varying formation through narrow waterways.

关键词： Autonomous surface vehicles (ASVs) control systems cooperative systems observers

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A Multi-View Multi-Scale Neural Network for Multi-label ECG Classification

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第3期7卷 648-660页

作者： Yang, Shunxiang Lian, Cheng Zeng, Zhigang Xu, Bingrong Zang, Junbin Zhang, Zhidong Wuhan Univ Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China North Univ China Sch Instrument & Elect Taiyuan 038507 Peoples R China

The 12-lead electrocardiogram (ECG) is a common method used to diagnose cardiovascular diseases. Recently, ECG classification using deep neural networks has been more accurate and efficient than traditional methods. Most ECG classification methods usually connect the 12-lead ECG into a matrix and then input this matrix into a deep neural network. We propose a multi-view and multi-scale deep neural network for ECG classification tasks considering different leads as different views, taking full advantage of the diversity of different lead features in a 12-lead ECG. The proposed network utilizes a multi-view approach to effectively fuse different lead features, and uses a multi-scale convolutional neural network structure to obtain the temporal features of an ECG at different scales. In addition, the spatial information and channel relationships of ECG features are captured by coordinate attention to enhance the feature representation of the network. Since our network contains six view networks, to reduce the size of the network, we also explore the distillation of dark knowledge from the multi-view network into a single-view network. Experimental results on multiple multi-label datasets show that our multi-view network outperforms existing state-of-the-art networks in multiple tasks.

关键词： Electrocardiography Convolution Feature extraction Neural networks Deep learning Kernel Knowledge engineering ECG classification multi-view multi-scale attention knowledge distillation

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Reward Shaping-based Double Deep Q-networks for Unmanned Surface Vessel Navigation and Obstacle Avoidance 48

Reward Shaping-based Double Deep Q-networks for Unmanned Sur...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Gan, Zihan Zheng, Jinghong Jiang, Zhenyu Lu, Renzhi Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University of Science and Technology Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan430081 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In this paper, a method for navigation and obstacle avoidance of unmanned surface vessel (USV) based on reinforcement learning and reward shaping is proposed. This approach uses double deep Q networks (DDQN) to make decisions based on the continuous states observed from sensors in USV. In addition, a new reward function is designed based on prior knowledge to accelerate the convergence of the algorithm and improve the performance. For training the neural networks, a simulation platform is developed, in which a 3 degree of freedom mathematical model describes USV dynamic system and two-dimension actions are required to control USV. Simulation results on the platform demonstrate the DDQN hoists USV's capabilities of navigation and obstacle avoidance, and reward shaping technique improves the speed of convergence. © 2022 IEEE.

关键词： Reinforcement learning

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Adaptive neural event-triggered consensus control for unknown nonlinear second-order delayed multi-agent systems

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NEUROCOMPUTING 2024年 598卷

作者： Chen, Jiejie Jiang, Ping Chen, Boshan Zeng, Zhigang Hubei Normal Univ Coll Comp Sci & Informat Engn Huangshi 435002 Peoples R China Hubei PolyTech Univ Comp Sch Huangshi 435002 Peoples R China Hubei Normal Univ Coll Math & Stat Huangshi 435002 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In this paper, we discuss event -triggered consensus control for a heterogeneous second -order multi -agent systems with unknown nonlinear functions and state delays (SODMASs). Firstly, a broad adaptive dynamic event -triggered mechanism (ETM), which includes many existing ETM, is proposed. Based on this ETM and the approximation property of neural networks, an adaptive event -triggered protocol with weight estimation of neural network and time -varying integral gain compensation is designed to reduce communication frequency and to eliminate the uncertainty of nonlinear function and state delays. Then, the consensus problem for the heterogeneous SODMASs under the protocol is solved successfully by selecting a suitable Lyapunov-Krasovskii functional. In addition to these, it is also proved that all agents can exclude Zeno behavior under the proposed event -triggered protocol. Finally, a numerical example is given to verify the effectiveness of the proposed consensus protocol algorithm.

关键词： Dynamic event-triggered Neural network State delay Second-order multi-agent systems Adaptive consensus control

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Deterministic-like data-driven discovery of stochastic differential equations via the Feynman–Kac formalism

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The European Physical Journal Special Topics 2024年 1-21页

作者： Ma, Chaoxiang Huang, Cheng Cheng, Cheng Li, Xiuting The College of Informatics Huazhong Agriculture University and the China–Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan People’s Republic of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Ministry of Education Key Lab of Intelligent Control and Image Processing Wuhan China

This paper develops a data-driven deterministic identification architecture for discovering stochastic differential equations (SDEs) directly from data. The architecture first generates deterministic data for stochastic processes using the Feynman–Kac formula, and gives a parabolic partial differential equation (PDE) associated with the SDE. Then, a sparse regression model is proposed to discover drift and diffusion terms in SDEs using PDE data-driven techniques, where a large candidate library of potential terms only for the drift and diffusion coefficients in SDEs need be constructed. To simultaneously infer the drift and diffusion terms, we proposed a sequential thresholded reweighted least-squares algorithm to solve the constructed sparse regression model. The main advantage of the proposed method is that on the one hand, theoretical and numerical identification results of PDEs can be used for SDEs, on the score, our SDE identification problem is translated into the parameter estimation problem of PDEs, on the other hand, the proposed algorithm is easily executed and can enhance the sparsity and accuracy. Through several classical SDEs and ordinary differential equations, the effectiveness of the proposed data-driven method is demonstrated, and several comparison experiments with state-of-the-art approaches is provided to illustrate the superiority of the developed algorithm.

关键词：

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Efficient Reinforcement Learning for 3D LiDAR Navigation of Mobile Robot 41

Efficient Reinforcement Learning for 3D LiDAR Navigation of ...

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第41届中国控制会议

作者： Yu Zhai Zhe Liu Yanzi Miao Hesheng Wang China University of Mining and Technology MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Developing an efficient automatic navigation system for mobile robots is challenging in the strange scenarios where robots can only observe the environment of the surrounding limited *** other distributed automatic navigation systems exist,they often require extracting semantic information to calculate navigation action,which requires extra modules to provide perceptual information and is not *** propose an end-to-end automatic navigation system based on the reinforcement learning *** particular,the raw 3 D LiDAR data is used to directly map an efficient navigation *** design a novel dense reward function to handle the reward sparsity issue and provide a graphical representation method to enable the efficient feature learning from the raw 3 D LiDAR data in our navigation *** addition,an imitation learning based policy initialization is introduced before the subsequent reinforcement learning,which increases the learning efficiency and,in the meantime,still encouraging the robot to explore all the potential states to achieve advanced performance than the imitated *** navigation model is trained in the Webots environment and the experimental results show that our model has efficient and flexible navigation performance in complex *** importantly,trained model can be easily extended to unfamiliar environments.

关键词： End-to-end Navigation Mobile robot Reinforcement Learning 3D LiDAR

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Analysis of MFCS topology for high efficiency and long lifetime

Analysis of MFCS topology for high efficiency and long lifet...

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第43届中国控制会议

作者： Hongfu Xiang Jiajian Wu Zhen Wang Jie Wang 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

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Multi-stack fuel cell system(MFCS) are an important basis for large-scale application of solid oxide fuel cell(SOFC)technology,MFCS can provide higher system power and longer service *** the number of stacks increases,different topologies can be formed between stacks,including parallel structures and series structures,and the physical characteristics of MFCS will also *** this study,the degradation of the stacks is first considered,and the system model of series structure and parallel structure including the degradation of the stacks is *** parallel structure proposed in this paper is the key to ensure the high efficiency and long lifetime of *** the simulation of systems with different topologies,it is found that the power generation efficiency of the systems caused by the topologies varies greatly in different output power *** efficiency of the parallel structure in the low power stage is significantly better than that of the series structure,and the parallel structure is more conducive to the high efficiency of the system in the whole *** topological structures also have a greater impact on the degradation of *** degradation rates of stacks in parallel structures are close to each other,and the state of stacks is more *** overall life of the system in parallel structures is also better than that in series structures.

关键词： SOFC MFCS topological structure stack degradation

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