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IEEE International Conference on Mechatronics and automation

作者： Yuchen Mao Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Application Complicated Systems and Intelligent Robot Laboratory School of Electrical Engineering and Automation Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system.

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Mechanical Structure and control Methods for Lower-Limb Rehabilitation Robots

Mechanical Structure and Control Methods for Lower-Limb Reha...

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第40届中国控制会议

作者： Wangyang Ge Ruoyu Jiang Juan Zhao Zhentao Liu Zhaohui Yang Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Rehabilitation Union HospitalTongji Medical CollegeHuazhong Univ.Science & Technology School of Engineering Tokyo University of Technology

As a rehabilitation aid,lower-limb rehabilitation robots help patients recover their walking *** paper presets a review of such a rehabilitation ***,we classify commercially available common types of lower-limb rehabilitation ***,we briefly describe the structure and characteristics of those robots,Then,we present the control methods used in those ***,we explain future research directions.

关键词： rehabilitation robots mechanical structure control method biological signal

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Comparison of the Smith-Waterman and Needleman-Wunsch algorithms for online similarity analysis of industrial alarm floods

Comparison of the Smith-Waterman and Needleman-Wunsch algori...

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IEEE Canada Electric Power Conference, EPEC

作者： Md Rezwan Parvez Wenkai Hu Tongwen Chen University of Alberta Edmonton Alberta Canada School of Automation and Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan China

ISBN: (数字)9781728164892

ISBN: (纸本)9781728164908

Alarm floods are considered to be the major obstacles that prevent the smooth process operations of large-scale industrial facilities. During an alarm flood situation, industrial operators often get confused by too many alarms and thus have difficulties in observing and handling critical alarms. In recent years, sequence alignment based similarity analysis has emerged as an effective way to handle alarm floods. Alarm floods caused by the same fault are very likely to consist of the same group of alarms in a certain sequential order. Conducting realtime sequence alignment of industrial alarm floods can help operators to quickly recall the root cause and make prompt corrective actions. This paper proposes the online similarity analysis of alarm floods based on the Smith-Waterman and Needleman-Wunsch algorithms, and compares their differences and application conditions. Case studies are provided to illustrate the proposed online similarity analysis methods and the differences of the two sequence alignment algorithms.

关键词： Heuristic algorithms Indexes Prediction algorithms Power systems Pattern matching Frequency measurement Conferences

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A Novel Multi-objective Evolutionary Algorithm Based on Space Partitioning 11th

A Novel Multi-objective Evolutionary Algorithm Based on Spac...

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11th International Symposium on Intelligence Computation and Applications, ISICA 2019

作者： Wu, Xiaofang Li, Changhe Zeng, Sanyou Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9789811555763

To design an effective multi-objective optimization evolutionary algorithms (MOEA), we need to address the following issues: 1) the sensitivity to the shape of true Pareto front (PF) on decomposition-based MOEAs;2) the loss of diversity due to paying so much attention to the convergence on domination-based MOEAs;3) the curse of dimensionality for many-objective optimization problems on grid-based MOEAs. This paper proposes an MOEA based on space partitioning (MOEA-SP) to address the above issues. In MOEA-SP, subspaces, partitioned by a k-dimensional tree (kd-tree), are sorted according to a bi-indicator criterion defined in this paper. Subspace-oriented and Max-Min selection methods are introduced to increase selection pressure and maintain diversity, respectively. Experimental studies show that MOEA-SP outperforms several compared algorithms on a set of benchmarks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Adaptive control for asynchronously switched fractional-order multi-agent systems

Adaptive control for asynchronously switched fractional-orde...

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第三十九届中国控制会议

作者： Xiongliang Zhang Shiqi Zheng Feng Liu Mingyuan Zhao Yuan Sun School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems School of Electrical and Electronic Engineering The University of Adelaide

In this paper, we discuss the adaptive controller design for a class of fractional-order（FO） multi-agent systems（MAS）.All the agents are described by FO pure-feedback nonlinear systems. This is more general than the existing works, which only considered strict-feedback or integer-order nonlinear systems. Meanwhile, asynchronous switching are considered in FO MAS,meaning the agents have heterogeneous switching dynamics. A new neural network based adaptive controller is proposed for the considered FO MAS, which can make the consensus tracking error converge to a small neighborhood of origin. Simulation verifies the results obtained.

关键词： Consensus tracking fractional-order multi-agent systems asynchronously switched systems

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Learning Multiple Network Embeddings for Social Influence Prediction ⁎

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IFAC-PapersOnLine 2020年第2期53卷 2868-2873页

作者： Feng Wang Jinhua She Yasuhiro Ohyama Min Wu School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China School of Engineering Tokyo University of Technology Tokyo 192-0982 Japan

How to effectively predict social influence is an essential issue in social network analysis. Almost all reported methods for social influence prediction are mainly concerned with estimating influence probabilities for each linking edge. However, all of this past work cannot accurately predict influence probabilities for all edges due to the problem of data sparsity. Unlike conventional approaches, this work focuses on exploring a cross problem for multiple network embeddings and social influence prediction. This study developed a new end-to-end approach, Multi-Influor, that learns multiple influence vectors for each user in social networks, instead of estimating influence probabilities for each edge. The multiple network embeddings consider multi-dimensional influence factors that incorporate pairwise node interactions, network structures, and global similarity comparisons. Moreover, this study solves the problem of influence evaluation caused by sparse observations. Extensive comparisons based on large-scale datasets indicate that the Multi-Influor approach outperforms several state-of-the-art baselines, and the experimental results demonstrate that the Multi-Influor approach is more practical on real-world social networks.

关键词： Multiple Network Embeddings Representation Learning Online Social Networks Neural Network Social Influence Prediction

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Model-free Output Consensus control for Nonlinear Multi-agent systems with Random Disturbance

Model-free Output Consensus Control for Nonlinear Multi-agen...

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Industrial Cyber-Physical systems (ICPS)

作者： Yipu Sun Xin Chen Hao Fu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781728163895

ISBN: (纸本)9781728163901

The model-free output consensus control problem for the nonlinear multi-agent systems (MASs) with random disturbance is considered. In order to avoid the difficulty of designing a consensus protocol when the dynamic model is unknown, this paper proposes an online learning method for a consensus protocol by Gaussian regression based two-phase iteration. First, output of the leader is estimated by an adaptive distributed observer. On this basis, measurable input/output data is used to solve the consensus control problem of model-free systems. A two-phase iteration for Gaussian-kernel based adaptive critic design (GK-ACD) is proposed to approximate optimal Q-function and optimal control policies, which can simultaneously approximate and update value functions and hyper-parameters. This avoids the problem of artificial selection of improper hyper-parameters may mislead the kernel-based regression. Finally, the simulation results show that, without knowledge of dynamics, the distributed policies achieves the output consensus control of nonlinear MASs, and proves the effectiveness of the method.

关键词： Observers Adaptation models Vehicle dynamics system dynamics Synchronization Nonlinear dynamical systems Multi-agent systems

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Drone Image Stitching Using Local Least Square Alignment

Drone Image Stitching Using Local Least Square Alignment

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Qi Wan Linbo Luo Jun Chen Yong Wang Donghai Guo School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

This paper proposes a strategy for drone image stitching using local least square alignment, which aims to effectively stitch multiple overlapping drone images into a natural panoramic image. Existing traditional methods using simple homography cannot handle the situation that the input drone images have parallax effect, and the mosaic result always suffers from artifacts. In order to achieve natural-looking stitching results without the above limitation, we divide the proposed method into the following two steps, namely, local least square alignment and global similarity constraint. Starting from initial feature sets obtained by traditional feature extraction methods, we construct a robust alignment energy based on parallax errors to adaptively eliminate parallax effects. The energy can be efficiently minimized used least square estimate. Combined with global similarity constraint, our proposed strategy can flexibly improve the naturalness of the results. Experiments show that our stitching strategy can more effectively eliminate parallax effects and achieve natural-looking results compared to other state-of-the-art methods.

关键词： Drones Image stitching Distortion Feature extraction Optimization Geology Adaptation models

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FreeDriveRF: Monocular RGB Dynamic NeRF without Poses for Autonomous Driving via Point-Level Dynamic-Static Decoupling

arXiv

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arXiv 2025年

作者： Wen, Yue Song, Liang Liu, Yijia Zhu, Siting Miao, Yanzi Han, Lijun Wang, Hesheng 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 Shanghai200240 China Dimanshen Technology Co. Ltd. China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China

Dynamic scene reconstruction for autonomous driving enables vehicles to perceive and interpret complex scene changes more precisely. Dynamic Neural Radiance Fields (NeRFs) have recently shown promising capability in scene modeling. However, many existing methods rely heavily on accurate poses inputs and multi-sensor data, leading to increased system complexity. To address this, we propose FreeDriveRF, which reconstructs dynamic driving scenes using only sequential RGB images without requiring poses inputs. We innovatively decouple dynamic and static parts at the early sampling level using semantic supervision, mitigating image blurring and artifacts. To overcome the challenges posed by object motion and occlusion in monocular camera, we introduce a warped ray-guided dynamic object rendering consistency loss, utilizing optical flow to better constrain the dynamic modeling process. Additionally, we incorporate estimated dynamic flow to constrain the pose optimization process, improving the stability and accuracy of unbounded scene reconstruction. Extensive experiments conducted on the KITTI and Waymo datasets demonstrate the superior performance of our method in dynamic scene modeling for autonomous driving. Our implementation will be available at https://***/IRMVLab/FreeDriveRF. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical flows

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Burden control Strategy Based on Reinforcement Learning for Gas Utilization Rate in Blast Furnace ⁎

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IFAC-PapersOnLine 2020年第2期53卷 11704-11709页

作者： Xiaoling Shen Jianqi An Min Wu Jinhua She School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China School of Engineering Tokyo University of Technology HachiojiTokyo 192-0982 Japan

Gas utilization rate (GUR) is an important state parameter to reflect the energy consumption, the quality and production of the pig iron, and the distribution of the gas flow in a blast furnace. The GUR is mainly adjusted by burden distribution and hot-blast supply. According to the analysis of mechanism and data, burden distribution and hot-blast supply affect the GUR on a long-time scale and short-time scale, respectively. However, few of the previous researches proposed the control method for the GUR and they did not consider multi-time-scale characteristics. Thus, it is necessary to design a control strategy or system for the GUR considering the multi-time-scale characteristics, which can make the GUR have a reasonable development trend. This paper presented a burden control strategy based on a reinforcement learning algorithm for the GUR. The method improved the development trend of the GUR on a long-time scale. The experimental results demonstrated that the sequence of the parameters of the burden distribution given by the presented method ensured a reasonable development trend of the GUR on a long-time scale.

关键词： Blast furnace gas utilization rate burden control strategy reinforcement learning algorithm long-time scale

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