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Improving Performance of Repetitive Control for Nonlinear systems via Improved Disturbance Compensation

IEEE Transactions on Industrial Informatics 2025年

作者： Tian, Shengnan Wang, Yibing Zhang, Manli Li, Yang Lu, Chengda Liu, Kang-Zhi She, Jinhua Wu, Min China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Chiba University Department of Electrical and Electronic Engineering Chiba263-8522 Japan Tokyo University of Technology School of Engineering Hachioji192-0982 Japan

In practice, repetitive control (RC) is a type of learning control that exhibits good tracking performance. However, existing nonlinear RC methods lack analysis and design of the learning property, which results in limited performance. This study addresses the learning-enhanced issue. First, a new fuzzy Lyapunov candidate is constructed for stability analysis, which contains an integral term associated with the membership function and a double integral term. The design of the learning-dependent term integrates the nonlinear membership function information, which enhances the learning ability. Second, an additional first-order low-pass filter is incorporated into the conventional equivalent input disturbance estimator. The new filter acts as an integrator that adjusts the bandwidth of the disturbance compensation and gradually eliminates the disturbances in the output error. Third, a recursive optimization algorithm is used to design the controllers. Experimental comparisons on motor drive systems demonstrate the effectiveness and superiority of the method. © 2005-2012 IEEE.

关键词： Membership functions

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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Jun Chen Liling Yang Wei Yu Wenping Gong Zhanchuan Cai Jiayi Ma School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Faculty of Engineering China University of Geosciences Wuhan China School of Computer Science and Engineering Macau University of Science and Technology Taipa Macau China Electronic Information School Wuhan University Wuhan China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. The code will be posted at: https://***/Liling-yang/SDSFusion.

关键词： Degradation Image fusion Semantics Training Visualization Meteorology Feature extraction Lighting Image segmentation Brightness

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Deep transfer learning-based decoder calibration for intracortical brain-machine interfaces

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Computers in Biology and Medicine 2025年第Pt A期192卷 110231-110231页

作者： Li, Xiao Dong, Xianxin Wang, Jun Mao, Haodong Tu, Xikai Li, Wei He, Jiping Li, Qiang Zhang, Peng Hubei Key Laboratory of Modern Manufacturing Quantity Engineering School of Mechanical Engineering Hubei University of Technology Wuhan430068 China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Science and Technology Hubei Wuhan China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan China Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Intracortical brain-machine interfaces (iBMIs) aim to establish a communication path between the brain and external devices. However, in the daily use of iBMIs, the non-stationarity of recorded neural signals necessitates frequent recalibration of the iBMI decoder to maintain decoding performance, which requires collecting and labeling a large amount of new data. To address this challenge and minimize the time needed for decoder recalibration, we proposed an active learning domain adversarial neural network (AL-DANN). This model leveraged a substantial volume of historical data alongside a small amount of current data (four samples per category) to calibrate the decoder. By incorporating domain adversarial and active learning strategies, the model effectively transferred knowledge from historical data to new data, reducing the demand for new samples. We validated the proposed method using neural signals recorded from three monkeys performing different movements in a classification task or a regression task. The results showed that the AL-DANN outperformed existing state-of-the-art methods. Impressively, it required only four new samples per category for decoder recalibration, leading to a recalibration time reduction of over 80 %. To our knowledge, this is the first study to incorporate deep transfer learning into iBMI decoder calibration, highlighting the significant potential of applying deep learning technologies in iBMIs. © 2025

关键词： Adversarial machine learning

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A novel hybrid neural network of fluid-structure interaction prediction for two cylinders in tandem arrangement

arXiv

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arXiv 2025年

作者： Lyu, Yanfang Zhang, Yunyang Gong, Zhiqiang Kang, Xiao Yao, Wen Pei, Yongmao State Key Laboratory for Turbulence and Complex Systems Center for Applied Physics and Technology College of Engineering Peking University Beijing100871 China Defense Innovation Institute Chinese Academy of Military Science Beijing100071 China Intelligent Game and Decision Laboratory Beijing100071 China Institute of Advanced Structure Technology Beijing Institute of Technology Beijing100081 China

Deep learning has shown promise in improving computing efficiency while ensuring modeling accuracy in fluid-structure interaction (FSI) analysis. However, its current capabilities are limited when it comes to constructing multi-object coupling systems with dynamic boundaries. To address such limitation, a novel FSI neural solver integrated by a fluid deep learning model with multi-time steps and a structural dynamic solver is proposed to accurately and reliably predict the vortex-induced vibration (VIV) evolution for two cylinders in tandem. This well-designed model in the form of end-to-end can precisely predict the instantaneous flow field state at the subsequent time by coupling the temporal flow fields of historical multi-time sequences and the current structural responses, moreover, derives the structural state at the next time. Furthermore, the novel fluid deep learning model consists of a wall shear model utilizing a multilayer perception network and flow field model with U-shaped architecture jointing the Fourier neural operator and modified convolution long-short term memory model. Both models effectively capture coupling transfer forces and predict instantaneous flow fields, with the latter demonstrating superior accuracy compared to Convolutional Neural Network- or Unet-based models with similar parameters. The prediction speed of the proposed models realizes an improvement of over 1000 times compared with the numerical simulation. Significantly, the proposed FSI neural model demonstrates exceptional capability in constructing the nonlinear complex multi-vibration systems and has substantial potential for advancing FSI modeling of flexible structures featuring pronounced nonlinear deformation boundaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Hydraulics

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Microscopic electrochemical–mechanical coupling in layered cathodes under high-voltage and fast-charging conditions

EES Batteries

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EES Batteries 2025年第1期1卷 73-99页

作者： Ziqi Wu Chenchen Zhang Mengyi Zheng Chaoyuan Zeng Wen Yang Ruiwen Shao Beijing Advanced Innovation Center for Intelligent Robots and Systems School of Medical Technology Beijing Institute of Technology Beijing 100081 China School of Chemistry and Chemical Engineering Key Laboratory of Ministry of Education for Advanced Materials in Tropical Island Resources Collaborative Innovation Center of Ecological Civilization Hainan University No. 58 Renmin Avenue Haikou 570228 China Key Laboratory of Cluster Science of Ministry of Education Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials School of Chemistry and Chemical Engineering Beijing Institute of Technology Beijing 100081 P. R. China Analysis & Testing Center Beijing Institute of Technology Beijing 102488 China

The operation of modern society and the advancement of the electric vehicle industry are intrinsically linked to high-performance lithium-ion batteries (LIBs). However, the inherent structural instability of traditional cathode materials under high voltage (high capacity) and rapid charging conditions leads to swift degradation of battery voltage and capacity. This significantly impedes the commercialization of LIBs and contributes to issues such as “range anxiety”. Currently, the failure mechanisms of cathode materials remain highly debated, with most research focusing on material modification and interfacial transport kinetics. Recent studies indicate that the accumulation of nanoscale strain and lattice displacement within cathode materials are critical driving forces behind structural degradation and oxygen loss. Despite their fundamental importance, lattice displacements and nanoscale strain remain among the least understood structural characteristics in battery materials. This review highlights the latest research advancements concerning the electrochemical–mechanical coupling mechanisms and advanced modification strategies of cathode materials. We begin by elucidating the electrochemical–mechanical failure mechanisms of cathode materials, spanning from the atomic to the macroscopic scale, and the interconnections among various failure mechanisms, with particular emphasis on lattice defects and microscopic mechanical anisotropy. Subsequently, we discuss advanced strategies and recent progress in modifying layered oxide cathode materials, which hold promise for furthering the development of the new energy industry. Finally, in the context of a carbon-neutral future and the evolving high-performance electric vehicle industry, we summarize and provide insights into the application prospects and developmental directions of layered oxide cathode materials from perspectives of cost, performance, safety, and environmental friendliness.

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A Framework for Prioritizing the Connected Vehicle Infrastructure Service in Mixed Autonomy Traffic: A Fuzzy-Analytic Hierarchy Process Approach

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Journal of advanced Transportation 2025年第1期2025卷

作者： Kim, Woosuk Moon, Junhyeong Choi, Sung-Soo Kang, Yong-Shin Lee, Sangsoo Kim, Hyungjoo Department of Transportation Engineering University of Seoul Seoul Korea Republic of Department of Urban and Transportation Engineering University of Kyonggi Kyonggi Korea Republic of Industrial Intelligence Laboratory Advanced Institute of Convergence Technology Suwon Korea Republic of Department of Transportation Systems Engineering Ajou University Republic of Korea Suwon Korea Republic of Intelligent Transportation Systems Laboratory Advanced Institute of Convergence Technology Suwon Korea Republic of

This study investigates the integration and prioritization of connected vehicle infrastructure services (CVISs) in mixed autonomy traffic systems using a fuzzy–analytic hierarchy process (Fuzzy–AHP). The study aims to enhance operational efficiency in environments where autonomous vehicles (AVs) and human-driven vehicles (HVs) coexist. By evaluating 92 existing services, the research selects and prioritizes 17 critical services that address safety and efficiency challenges. The methodology involves a Fuzzy–AHP analysis to assess service importance and a modified–importance–performance analysis (M–IPA) to categorize services as either specialized or common based on their utility for AVs and HVs. The findings highlight the pivotal roles of emergency management, traffic operation, and pedestrian detection services in improving traffic safety and flow. This study contributes to the theoretical and practical understanding of CVIS implementation, offering a framework for policymakers and engineers to optimize infrastructure in mixed autonomy traffic scenarios. Copyright © 2025 Woosuk Kim et al. Journal of advanced Transportation published by John Wiley & Sons Ltd.

关键词： Risk management

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A Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年

作者： Zeng, Hongjian Xiong, Yonghua She, Jinhua Yu, Anjun China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji Tokyo192-0982 Japan Jiangxi Ganyue Expressway Co. Ltd Jiangxi Nanchang330000 China

Sparse Mobile Crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers' mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms. © 2014 IEEE.

关键词： Genetic algorithms

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Single-cell omics: experimental workflow, data analyses and applications

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Science China(Life Sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Cutting Skill Assessment by Motion Analysis Using Deep Learning and Spatial Marker Tracking

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IEEE Transactions on Biomedical Engineering 2025年第6期72卷 1963-1972页

作者： Su, Bai-Quan Ma, Xu-Dong Li, Weihan Kuang, Zi-Ao Gong, Yi Wang, Gang Zhang, Qingqian Liu, Wenyong Li, Changsheng Gao, Li Wang, Junchen Beijing University of Posts and Telecommunications Medical Robotics Laboratory School of Automation Beijing100876 China University of Tokyo School of Mechanical Engineering Tokyo113-0033 Japan Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China Peking University General Surgery Department Beijing100191 China Beihang University Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beijing100083 China Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China Peking University Department of Periodontology National Stomatological Center School and Hospital of Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing100081 China Beihang University School of Mechanical Engineering and Automation Beijing100083 China

The assessment of surgical skill is crucial for indicating a surgeon's proficiency. While motion analysis of surgical tools is widely used in endoscopic surgery, it is not commonly applied to open surgery. Instead, open surgery skill assessment relies on observing the trajectory of surgical tools on tissue. This observation-based method often lacks clear standards, leading to inaccurate assessments. This paper presents a method for evaluating cutting skill in open surgery through scalpel motion analysis. A 3D multiple-facet ArUco code cube is designed, and a dataset of tip coordinate system poses for various scalpels in the ArUco code coordinate system (ACS) is established using the pivot calibration method. The YOLOv8 model and an image dataset of different scalpels are used to identify the scalpel type and select its tip position. The tip position is then transformed from ACS to a binocular camera coordinate system (BCS), representing the incision curve made by the scalpel. Five assessment metrics are proposed to quantify the surgeon's cutting skill: average incision curvature deviation, incision length difference, incision endpoint deviation, average incision deviation, and average cutting jerk. Experiments involving twenty expert and novice surgeons performing four common incisions (straight line, polyline, semicircle, and cross line) demonstrate the metrics' effectiveness. The metrics provide a clear, objective display of individual cutting skills, and a combined ranking reveals comparative skill levels. This study offers a precise method for evaluating surgeons' cutting skills with a scalpel in open surgery. © 1964-2012 IEEE.

关键词： Surgery

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Enhanced photocatalytic overall water splitting via non-precious Zn-induced spatial separation of photoexcited excitons in polymeric carbon nitride

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Fuel 2025年 381卷

作者： Jin, Zhengyuan Xu, Yangsen Xue, Bin Zhang, Luhong Wang, Xinzhong Arif, Nayab Ahsan Iqbal, Muhammad Qi, Lu Zeng, Yu-Jia Ohno, Teruhisa Institute of Information Technology SZIIT Innovation Harbor Shenzhen Institute of Information Technology Shenzhen518172 China Shenzhen Key Laboratory of Ultraintense Laser and Advanced Material Technology Center for Advanced Material Diagnostic Technology College of Engineering Physics Shenzhen Technology University Shenzhen518118 China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies for Urban Lifeline Engineering Dongguan University of Technology Dongguan523808 China Department of Applied Chemistry Faculty of Engineering Kyushu Institute of Technology 1-1 Sensuicho Tobata Kitakyushu804-8550 Japan

Photocatalytic water splitting has emerged as a vital technology for producing clean hydrogen fuel, addressing global energy needs. However, most research has focused on half-water splitting with sacrificial agents, which limits its practical application in fuel production. In this study, we present an advanced co-catalyst system for polymeric carbon nitride (CN) designed for efficient overall water splitting, a crucial step toward scalable hydrogen fuel generation. The co-catalyst system includes full loading of CoOx, Zn-Pt, and Cr2O3, with Zn strategically inserted between Pt and CN. This configuration enhances the spatial separation of photoexcited carriers, promoting efficient segregation of oxidation and reduction sites while minimizing electron-hole recombination. The optimized system achieves a stoichiometric H2 and O2 ratio of 2:1, with a hydrogen evolution rate that is nine times higher than when Pt alone is used as the co-catalyst. This advancement provides a pathway for efficient solar-driven hydrogen production, addressing the critical challenge of moving beyond half-water splitting and contributing to the development of clean fuel technologies. © 2024 Elsevier Ltd

关键词： Hydrogen fuels

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