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IEEE International Conference on Robotics and Biomimetics

作者： Lili Wu Yang Liu Junfang Zhou Zhigang Wang Yushuang Wang Yongchen Tang Beijing Electro-Mechanical Engineering Institute Beijing China 91039 Army School of Electrical and Information Engineering Tianjin University Tianjin China Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan China

ISBN: (纸本)9781665405362

The joint angle control of snake robot is an important component for the path tracking control. However, existing controllers can not realize the accurate control of the joint angle of snake robots since they rely on model information and can not adapt to the uncertain environment. Here, aiming at the above problems, a fuzzy extended state observer (FESO)-based joint angle controller for snake robots is designed. The total system disturbance including model uncertainty and external disturbance is estimated by FESO and then rejected by adding a corresponding part to the control law. Moreover, the peaking phenomenon caused by initial estimation errors can be suppressed in the proposed FESO. Finally, the effectiveness of the designed joint angle controller is verified by simulations.

关键词： Adaptation models Estimation error Uncertainty control design Conferences Biomimetics Biological system modeling

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Utility-Oriented Underwater image Quality Assessment Based on Transfer Learning

arXiv

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arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater image Utility Database (UIUD) and a learning-based Underwater image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

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Byzantine-Resilient Multi-Agent Distributed Exact Optimization with Less Data

arXiv

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arXiv 2023年

作者： Zhai, Yang Liu, Zhi-Wei Yue, Dong Hu, Songlin Xie, Xiangpeng The Institute of Advanced Technology for Carbon Neutrality Nanjing University of Posts and Telecommunications Nanjing210023 China The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China

This paper studies the distributed multi-agent resilient optimization problem under the f-total Byzantine attacks. Compared with the previous work on Byzantine-resilient multi-agent exact optimization problems, we do not require the communication topology to be fully connected. Under the redundancy of cost functions, we propose the distributed comparative gradient elimination resilient optimization algorithm based on the traditional assumptions on strongly convex global costs and Lipschitz continuous gradients. Under this algorithm, we successfully prove that if the number of in-neighbors of each normal agent is greater than some constant and the parameter f satisfies certain conditions, all normal agents' local estimations of the global variable will finally reach consensus and converge to the optimized solution. Finally, the numerical experiments successfully verify the correctness of the results. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Network resilience

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PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS 2022年 971卷 1-108页

作者： Liu, Xueming Li, Daqing Ma, Manqing Szymanski, Boleslaw K. Stanley, H. Eugene Gao, Jianxi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Civil Aviat Univ China Coll Safety Sci & Engn Tianjin 300300 Peoples R China Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA Network Sci Rensselaer Polytech Inst Technol Ctr Troy NY 12180 USA Boston Univ Ctr Polymer Studies Dept Phys Boston MA 02215 USA

Many systems on our planet shift abruptly and irreversibly from the desired state to an undesired state when forced across a "tipping point". Some examples are mass extinctions within ecosystems, cascading failures in infrastructure systems, and changes in human and animal social networks. The ability to avoid such regime shifts or to recover quickly from such a non-resilient state demonstrates a system's resilience;system resilience is a quality that enables a system to adjust its activities to retain its basic functionality when errors and failures occur. In the past 50 years, attention has been paid almost exclusively to low-dimensional systems;scholars have focused on the calibration of the resilience functions of such systems and the identification of indicators of early warning signals based on two to three connected components. In recent years, taking advantage of network theory and the availability of lavish real datasets, network scientists have begun to explore real-world complex networked multidimensional systems, as well as their resilience functions and early warning indicators. This report presents a comprehensive review of resilience functions and regime shifts in complex systems in domains such as ecology, biology, society, and infrastructure. The research approach includes empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also review the definitions of some ambiguous terms, including robustness, resilience, and stability.

关键词： Complex networks Resilience Nonlinear dynamics Alternative stable states Tipping points Phase transitions

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Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

Selection of Cancer Biomarkers from Microarray Gene Expressi...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanjing Jiang Jianqing Lin Huachao Zhu Yabing Huang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Pathology Renmin Hospital of Wuhan University Wuhan China

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy.

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New Criteria of Passivity Analysis for Fuzzy Time-Delay Systems With Parameter Uncertainties

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IEEE Transactions on Fuzzy Systems 2015年第6期23卷 2284-2301页

作者： Wen, Shiping Zeng, Zhigang Huang, Tingwen Yu, Xinghuo Xiao, Mingqing School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Texas A and M University at Qatar Doha23874 Qatar Platform Technologies Research Institute RMIT University MelbourneVIC3001 Australia Department of Mathematics Southern Illinois University CarbondaleIL62901 United States

This paper investigates the passivity problem for a class of uncertain stochastic fuzzy nonlinear systems with mixed delays and nonlinear noise disturbances by employing an improved free-weighting matrix approach. The fuzzy system is based on the Takagi-Sugeno model that is often used to represent the complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning. To reflect more realistic dynamical behaviors of the system, the parameter uncertainties, the stochastic disturbances, and nonlinearities are considered, where the parameter uncertainties enter into all the system matrices, the stochastic disturbances are given in the form of a Brownian motion. The mixed delays comprise both discrete and distributed time-varying delays. By taking the relationship among the time delays, their lower and upper bounds into account, some less conservative linear-matrix-inequality-based delay-dependent passivity criteria are obtained without ignoring any useful terms in the derivative of Lyapunov functional. Finally, numerical examples are given to demonstrate the effectiveness and merits of the proposed methods. © 1993-2012 IEEE.

关键词： Time delay

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Metabolic Efficiency Improvement of Human Walking by Shoulder Stress Reduction through Load Transfer Backpack

Metabolic Efficiency Improvement of Human Walking by Shoulde...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Cao, Yu Huang, Jian Li, Xiaolong Zhang, Mengshi Xiong, Caihua Mohammed, Samer Zhu, Yaonan Hasegawa, Yasuhisa Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China UPEC LISSI F-94400 Vitry Sur Seine France Nagoya Univ Dept Micronano Mech Sci & Engn Nagoya Aichi 4648603 Japan

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

The dynamic load attached to the load gravity imposes an excessive burden to human shoulders during load carriage, resulting in possible muscle injuries and additional physical exertion. This paper proposes an active suspension backpack, capable of transferring partial load from human shoulders to pelvis and alleviating the dynamic load through separated panels and motor actuation, to reduce pressure on human shoulders and improve walking metabolic efficiency. Based on the human body motion in the vertical direction, the dynamical model of the human-backpack system with shoulder interaction force measured by a soft ballonet with an embedded air pressure sensor is introduced, and an impedance controller has been implemented to maintain a relatively small and constant pressure on the shoulder. In an experimental case study, we presents preliminary results of three healthy subjects performing a treadmill walking with a 20kg load in ACTIVE configuration where the shoulder pressure shows a decrease by 30% along with a reduction of the metabolic energy consumption by 16.4%, compared with the load LOCKED case.

关键词： Legged locomotion Pressure sensors Muscles Pressure measurement Impedance Pelvis Motion measurement

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xin Xu Wenjing Lu Jiahao Lei Peng Qiu Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Department of Vascular Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods.

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Research on the multi-objective resource-constrained transport task scheduling problem

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Wuhan Ligong Daxue Xuebao/Journal of Wuhan University of Technology 2008年第5期30卷 155-158页

作者： Wang, Jian Wang, Hong-Wei Institute of Systems Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

A resource-constrained transport task scheduling problem (RCTTSP) with two optimal objectives was considered, and a multi-objective hybrid genetic algorithm (HGA) was proposed. The proposed algorithm used the serial scheduling method to initialize the population and evaluated the individual. It used the weighted sum method and the rank-based fitness assignment method to assign the individual fitness. Firstly, this paper described the multi-objective RCTTSP and presented the principle of the HGA, and then developed the algorithm to implement several experimental cases with different problem size;lastly the effectiveness and efficiency of the algorithm were compared. The numerical result indicated that the proposed multi-objective HGA can resolve the proposed multi-objective RCTTSP efficiently.

关键词： Hybrid genetic algorithm Multi-objective Resource-constrained Serial scheduling Transport task scheduling

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The Objective Dementia Severity Scale Based on MRI with Contrastive Learning: A Whole Brain Neuroimaging Perspective

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SENSORS 2023年第15期23卷 6871-6871页

作者： Zhang, Yike Fan, Wenliang Chen, Xi Li, Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Educ Minist China Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Union Hosp Tongji Med Coll Dept Radiol Wuhan 430074 Peoples R China

In the clinical treatment of Alzheimer's disease, one of the most important tasks is evaluating its severity for diagnosis and therapy. However, traditional testing methods are deficient, such as their susceptibility to subjective factors, incomplete evaluation, low accuracy, or insufficient granularity, resulting in unreliable evaluation scores. To address these issues, we propose an objective dementia severity scale based on MRI (ODSS-MRI) using contrastive learning to automatically evaluate the neurological function of patients. The approach utilizes a deep learning framework and a contrastive learning strategy to mine relevant information from structural magnetic resonance images to obtain the patient's neurological function level score. Given that the model is driven by the patient's whole brain imaging data, but without any possible biased manual intervention or instruction from the physician or patient, it provides a comprehensive and objective evaluation of the patient's neurological function. We conducted experiments on the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, and the results showed that the proposed ODSS-MRI was correlated with the stages of AD 88.55% better than all existing methods. This demonstrates its efficacy to describe the neurological function changes of patients during AD progression. It also outperformed traditional psychiatric rating scales in discriminating different stages of AD, which is indicative of its superiority for neurological function evaluation.

关键词： medical robots disease evaluation Alzheimer's disease contrastive learning

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