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Steering motion control of a snake robot via a biomimetic approach

Frontiers of Information technology & Electronic engineering 2019年第1期20卷 32-44页

作者： Wenjuan OUYANG Wenyu LIANG Chenzui LI Hui ZHENG Qinyuan REN Ping LI State Key Laboratory of Industrial Control Technology Zhejiang University Department of Electrical and Computer Engineering National University of Singapore Zhejiang University of Science and Technology

We propose a biomimetic approach for steering motion control of a snake robot. Inspired by a vertebrate biological motor system paradigm, a hierarchical control scheme is adopted. In the control scheme, an artificial central pattern generator(CPG) is employed to generate serpentine locomotion in the robot. This generator outputs the coordinated desired joint angle commands to each lower-level effector controller, while the locomotion can be controlled through CPG modulation by a higher-level motion controller. The motion controller consists of a cerebellar model articulation controller(CMAC) and a proportional-derivative(PD) controller. Because of the fast learning ability of the CMAC, the proposed motion controller can drive the robot to track the desired orientation and adapt to unexpected perturbations. The PD controller is employed to expedite the convergence speed of the motion controller. Finally, both numerical studies and experiments proved that the proposed approach can help the snake robot achieve good tracking performance and adaptability in a varying environment.

关键词： Snake robot Central pattern generator Cerebellar model articulation controller

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IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks

arXiv

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arXiv 2022年

作者： Xue, Ping Lu, Yang Chang, Jingfei Wei, Xing Wei, Zhen The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Anhui Mine IOT and Security Monitoring Technology Key Laboratory Hefei230088 China The Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei University of Technology Hefei230009 China The Intelligent Manufacturing Institute of HeFei University of Technology Hefei230009 China

Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but cause severe accuracy degradation. Existing optimization methods for binary neural networks (BNNs) focus on fitting full-precision networks to reduce quantization errors, and suffer from the trade-off between accuracy and computational complexity. In contrast, considering the limited learning ability and information loss caused by the limited representational capability of BNNs, we propose IR2Net to stimulate the potential of BNNs and improve the network accuracy by restricting the input information and recovering the feature information, including: 1) information restriction: for a BNN, by evaluating the learning ability on the input information, discarding some of the information it cannot focus on, and limiting the amount of input information to match its learning ability;2) information recovery: due to the information loss in forward propagation, the output feature information of the network is not enough to support accurate classification. By selecting some shallow feature maps with richer information, and fusing them with the final feature maps to recover the feature information. In addition, the computational cost is reduced by streamlining the information recovery method to strike a better trade-off between accuracy and efficiency. Experimental results demonstrate that our approach still achieves comparable accuracy even with ∼10x floating-point operations (FLOPs) reduction for ResNet-18. The models and code are available at https://***/pingxue-hfut/IR2Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Recovery

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From Standard ZND(Zhang Neural Dynamics) Solving Time-Variant Linear System(LS) to Elegant-Formula ZND Solving Time-Invariant LS Linked to Jacobi Iteration

From Standard ZND(Zhang Neural Dynamics) Solving Time-Varian...

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第34届中国控制与决策会议

作者： Wuyi Yang Xiao Liu Liyu Qin Yunong Zhang School of Artificial Intelligence Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology School of Electronics and Information Technology Sun Yat-sen University School of Computer Science and Engineering Sun Yat-sen University

Solving linear system(LS) is a common and important issue in both academic and industrial *** are many methods to solve LS,including neural network(specifically,neural dynamics) and traditional numerical computation ***,traditional numerical computation algorithms comprise,but not limited to,Jacobi iteration and Gauss-Seidel iteration *** the other hand,neural network algorithms have been the hot topics in research for a long time,which are applied in various *** addition,standard Zhang neural dynamics(ZND) is a special kind of neural network to solve some time-variant problems *** this paper,an elegant-formula ZND(EFZND) algorithm is obtained through theoretical ***,the standard ZND and different EFZND models for solving LS are ***,the possible relationship between neural dynamics and traditional numerical computation algorithms,especially Jacobi iteration algorithm,is discussed from two specific EFZND models for solving ***,the feasibility and efficiency of standard ZND and EFZND models for solving LS are proved through derivation.

关键词： Zhang neural dynamics Jacobi iteration linear system numerical algorithms time-variant problems

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Numerical Simulation of Water and Mud Inrush Processes in Mountain Tunnels Using Coupled Lbm-Dem

SSRN

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SSRN 2023年

作者： Fan, Zhanfeng Wu, Xiaohua Wang, Zhijia Li, Jun Sichuan Engineering Research Center for Mechanical Properties and Engineering Technology of Unsaturated Soils Chengdu University Chengdu610106 China Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province Xihua University Chengdu610039 China School of Civil and Architectural Engineering Hainan University Haikou570228 China College of Computer Science and Cyber Security Chengdu University of Technology Chengdu610059 China

Studying the mechanism of sudden water inrush and mud flow in mountain tunnels is crucial for implementing preventive measures. In addition, tunnel excavation through a fault or fractured zone can easily trigger sudden water inrush or mudflow. In the paper, the coupled lattice Boltzmann method (LBM) and discrete element method (DEM) were used to simulate the process of water and mud inrush in mountain tunnels. Tunnel mud burst failure and water inrush are essentially a fluid-solid coupling process. The two-dimensional Boltzmann method for fluids and DEM for particles were introduced. The coupled LBMDEM boundary adopts the immersed moving boundary method. For the water inrush process, the presence of water was predicted using advanced geological prediction of seismic waves in the tunnel. A numerical model was established to reproduce the flow of water particles in karst pipelines. The model had a length of 7 cm, a width of 4 cm, and consisted of 100 particles. For the mud inrush process, a numerical model was established with dimensions of 5 cm in length and 4 cm in height. In this model, 720 particles were randomly generated. The results indicated that the evolution process of the three consecutive stages of © 2023, The Authors. All rights reserved.

关键词： Finite difference method

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Incremental Dissipativity based control of Discrete-Time Nonlinear Systems via the LPV Framework

Incremental Dissipativity based Control of Discrete-Time Non...

引用

IEEE Conference on Decision and control

作者： Patrick J.W. Koelewijn Roland Tóth Siep Weiland Control Systems Group Faculty of Electrical Engineering Eindhoven University of Technology MB Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (纸本)9781665436601

Unlike for Linear Time-Invariant (LTI) systems, for nonlinear systems, there exists no general framework for systematic convex controller design which incorporates performance shaping. The Linear Parameter-Varying (LPV) framework sought to bridge this gap by extending convex LTI synthesis results such that they could be applied to nonlinear systems. However, recent literature has shown that naive application of the LPV framework can fail to guarantee the desired asymptotic stability guarantees for nonlinear systems. Incremental dissipativity theory has been successfully used in the literature to overcome these issues for Continuous-Time (CT) systems. However, so far no solution has been proposed for output-feedback based incremental control for the Discrete-Time (DT) case. Using recent results on convex analysis of incremental dissipativity for DT nonlinear systems, in this paper, we propose a convex output-feedback controller synthesis method to ensure closed-loop incremental dissipativity of DT nonlinear systems via the LPV framework. The proposed method is applied on a simulation example, demonstrating improved stability and performance properties compared to a standard LPV controller design.

关键词： Linear systems Bridges Asymptotic stability Systematics Conferences control systems Stability analysis

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A Multi-Task-Based Deep Multi-Scale Information Fusion Method for Intelligent Diagnosis of Bearing Faults

SSRN

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SSRN 2022年

作者： Xin, Ruihao Wang, Tiantian Miao, Fengbo Yu, Cuinan Feng, Xin College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin Jilin132000 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Science Jilin Institute of Chemical Technology Jilin Jilin132000 China

The use of deep learning for fault diagnosis is already a commonly used method. However, complex operating conditions under different working conditions significantly impact bearing vibration signals. Integrating the discriminative information of fault type and size into the deep learning model to achieve rich multi-task fault feature diagnosis still deserves attention. This study proposes a deep multi-task-based multi-scale feature fusion network MEAT(multi-task-based deep multi-scale feature fusion network) model to address these issues. Solve the limitations and poor adaptability of traditional convolutional neural network models under complex work. This model considers two sub-tasks, fault scale discrimination, and fault type discrimination, in the fault diagnosis multi-classification task through multi-task mapping decomposition. First, multi-dimensional features are extracted by convolution at different scales to obtain multi-dimensional fault information at different levels. Then these features are weighted and fused using a multi-layer attention mechanism to get accurate diagnosis results of bearing faults. © 2022, The Authors. All rights reserved.

关键词： Failure analysis

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Distributed Active Disturbance Rejection control of Multiple Mobile Robots for Online Workload Partition

Distributed Active Disturbance Rejection Control of Multiple...

引用

Chinese Automation Congress (CAC)

作者： Xin Li Chao Zhai Hehong Zhang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan Campus School of Automation Wuhan China School of Computer and Data Science Fuzhou University Fuzhou China

Considering the actual multi-agent coverage process, the motion trajectories are seriously affected by disturbances and noise. In this paper, a cooperative control method based on active disturbance rejection controller (ADRC) for multiple mecanum-wheeled robots (MWMR) is proposed to improve its robustness. In particular, to suppress the total disturbances during the running of each robot and reduce large computation and observation cost of high order ADRC, a reduced-order cascaded ADRC (RC-ADRC) is proposed. Moreover, distributed controller is proposed to track the expected trajectories of multiple mecanum-wheeled robots under disturbances. Compared with the proposed RC-ADRC based cooperative control scheme and PID based cooperative control one, the favorable motion trajectory tracking performance is obtained. Simulation results verify the superiority of the method.

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Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年

作者： Diao, Yiya Li, Changhe Zeng, Sanyou Yang, Shengxiang Coello, Carlos A. Coello China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Anhui University of Sciences and Technology School of Artificial Intelligence Hefei China China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Department of Computer Science CINVESTAV-IPN 07360 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L. Monterrey Mexico

Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this paper, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measurements and a 3D visualization method based on NBN. Experiments show that compared to the other main FLA methods, the four numerical measurements proposed here can effectively measure the four intended features: neutrality, ruggedness, modality, and basin of attraction, respectively, and common features of the fitness landscape can be maintained in 3D NBN visualization, regardless of the scale of the problem. NBN also provides a view of how algorithms search in high-dimensional problems with the help of the 3D NBN visualization. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Erratum to: Optimized design of a reinforced exhaust at low velocity (RELV) system for efficient viral aerosol removal in elevators (Building Simulation, (2025), 10.1007/s12273-025-1258-2)

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Building Simulation 2025年第5期18卷 1229-1229页

作者： Liu, Sumei Xu, Longhui Chen, Bingqian Deng, Zhipeng Du, Chengbo Li, Pin Zhao, Runmin Tianjin Key Laboratory of Indoor Air Environmental Quality Control School of Environmental Science and Engineering Tianjin University Tianjin300072 China School of Energy and Electrical Engineering Qinghai University Xining810016 China BYD Auto Industry Company Limited Shenzhen518119 China Department of Mechanical and Aerospace Engineering College of Engineering and Computer Science University of Central Florida OrlandoFL32816 United States School of Mechanical Engineering Purdue University West LafayetteIN47907 United States Shanghai Junyu Information Technology Co. Ltd Shanghai China

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