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Analysis of Conjoint Response in Post-stroke Patients Using the Attention-coupled Weighting Method

Machine Intelligence Research 2025年第2期22卷 352-367页

作者： Jingyao Chen Chen Wang Ningcun Xu Zeng-Guang Hou Liang Peng Pu Zhang Faculty of Innovation Engineering CASIA-MUST Joint Laboratory of Intelligence Science and TechnologyMacao University of Science and TechnologyMacao 999078China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Analysis and Testing Beijing Academy of Science and TechnologyBeijing 100089China Department of Rehabilitation Evaluation China Rehabilitation Research CenterBeijing 100068China

Rehabilitation assessment plays a vital role in the recovery process of post-stroke ***,many studies have focused on the implementation of automated assessment ***,the impact of stroke on the synergistic pattern between different human joints is not well *** this study,we developed an attention-coupled weighting based quantitative analysis model to identify the pathological alteration in the conjoint response structure between stroke patients and healthy participants,thus providing more intrinsic suggestions for the post-stroke rehabilitation *** collaboration with the China Rehabilitation Research Center and the Macao University of Science and technology,we recruited 15 post-stroke patients and 15 healthy participants to complete several actions selected from the Fugl-Meyer assessment(FMA)scale,which is a commonly used post-stroke rehabilitation assessment scale in clinical *** study proposed an attentional coupling weight(ACW)extraction *** filtering the weights in the attentional coupling network using a priori matrix,we can analyse the effect of stroke on the alteration of synergistic structures and the coordination patterns of the upper and lower ***,we used a commonly accepted synergy analysis method,the nonnegative matrix decomposition(NMF)method,for comparison and *** experimental results demonstrated the effectiveness of our method in quantifying the synergistic structure pattern alterations in post-stroke patients,which provides a new targeted option for the rehabilitation process.

关键词： Intelligent assessment conjoint response attentional coupling network spatio-temporal weight extraction.

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Evaluation Method of Motor Coordination Ability in Children Based on Machine Vision

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Tsinghua Science and technology 2025年第2期30卷 633-649页

作者： Yi Lei Dawei Shu Miao Yu Donglin Shi Jianqiang Li Yanjie Chen Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Department of Automation Tsinghua UniversityBeijing 100084China Pharmacovigilance Research Center for information technology and Data Science Cross-strait Tsinghua Research InstituteXiamen 361000China Institute of Systems Engineering Dalian University of TechnologyDalian 116024China Physical Education Department Hebei Sports UniversityShijiazhuang 050049China Department of Children’s Health Care Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijing 100045China

Motor coordination is crucial for preschoolers’development and is a key factor in assessing childhood *** diagnostic methods often rely on subjective manual *** paper presents a machine vision-based approach aimed at improving the objectivity and adaptability of *** method proposed involves the extraction of key points from the human skeleton through the utilization of a lightweight pose estimation network,thereby transforming video assessments into evaluations of keypoint *** study uses different methods to handle static and dynamic actions,including regularization and Dynamic Time Warping(DTW)for spatial alignment and temporal discrepancies.A penalty-adjusted single-frame pose similarity method is used to evaluate *** lightweight pose estimation model reduces parameters by 85%,uses only 6.6%of the original computational load,and has an average detection missing rate of less than 1%.The average error for static actions is 0.071 with a correlation coefficient of 0.766,and for dynamic actions it is 0.145 with a correlation coefficient of *** results confirm the proposed method’s effectiveness,which includes customized visual components like motion waveform graphs to improve accuracy in pediatric healthcare diagnoses.

关键词： developmental coordination disorder action quality assessment pose estimation dynamic time warping decision support system

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Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud Computing

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IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

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Conditional Disturbance Compensation-Based Fault-Tolerant Group Consensus Control for MASs: An Event-Based Switching Method

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IEEE Transactions on automation Science and Engineering 2025年 22卷 13828-13841页

作者： Liu, Pei-Ming Guo, Xiang-Gui Wang, Jian-Liang Che, Wei-Wei Wu, Zheng-Guang University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering Beijing100083 China University of Science and Technology Beijing Shunde Innovation School Foshan528000 China Beihang University Hangzhou Innovation Institute Autonomous Intelligent Systems Department Hangzhou310051 China Northeastern University College of Information Science and Engineering Shenyang266071 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou310027 China

With respect to the group consensus control for multi-agent systems (MASs) with disturbances and actuator nonidentical and unknown direction faults (NUDFs), this paper proposes a novel fault-tolerant control scheme by incorporating an event-based switching function and a saturation function. The event-based switching function is implemented to directly switch to the desired working mode, thereby reducing invalid switching after a reverse fault. Concurrently, the saturation function, acting at the software level, can not only prevent the instantaneous impact of excessive reverse input caused by a reverse fault, but also eliminate input peaking and state chattering after switching the working mode. It is worth mentioning that the proposed switching-based fault-tolerant control method can avoid the control chocks and excessive control gains as in the existing methods by using Nussbaum functions and thus improve the transient performance. Additionally, for disturbances, the designed conditional disturbance compensation mechanism ensures a smaller steady-state error, and less energy consumption by utilizing beneficial disturbances and compensating for harmful ones. Besides, the conditional compensation mechanism also reduces the saturation error. Finally, the effectiveness of the proposed control scheme are verified through a simulation example of uncrewed aerial vehicles (UAVs). © 2004-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Data Imputation Techniques Using the Bag of Functions: Addressing Variable Input Lengths and Missing Data in Time Series Decomposition

Data Imputation Techniques Using the Bag of Functions: Addre...

引用

26th International Conference on Industrial technology, ICIT 2025

作者： Torres, David Orlando Salazar Altinses, Diyar Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and learning systems Soest Germany

ISBN: (纸本)9798331521950

In time series analysis, the ability to effectively handle data with varying input lengths and missing data is crucial for accurate modeling. This paper presents the Bag-of-Functions-Driven Imputation framework, which leverages sequence-length independent techniques to decompose time series data while accommodating inputs of differing sizes. Unlike traditional methods that require uniform input lengths, the Padding-BoF framework employs a flexible encoding approach, allowing for the integration of variable-length time series and missing elements in the data. Through a series of experiments, we demonstrate that the BoF framework not only ensures precise reconstruction of the original data but also enhances data imputation capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling and disparate operational cycles, making it a valuable tool for applications in fields such as finance, healthcare, and industrial monitoring. © 2025 IEEE.

关键词： Data reduction

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Data Imputation Techniques Using the Bag of Functions: Addressing Variable Input Lengths and Missing Data in Time Series Decomposition

Data Imputation Techniques Using the Bag of Functions: Addre...

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IEEE International Conference on Industrial technology (ICIT)

作者： David Orlando Salazar Torres Diyar Altinses Andreas Schwung Department of Automation Technology and learning systems South Westphalia University of Applied Sciences Soest Germany

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

关键词： Learning systems Adaptation models Accuracy Time series analysis Finance Medical services Imputation Data models Encoding Monitoring

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A Case Study on Peri-Urban Public Transport Optimisation From an Energy and Environmental Perspective

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IET Intelligent Transport systems 2025年第1期19卷

作者： Vasconcelos, André Coelho, Margarida C. Bandeira, Jorge M. Department of Mechanical Engineering Centre for Mechanical Technology and Automation University of Aveiro Aveiro Portugal LASI – Intelligent Systems Associate Laboratory Aveiro Portugal Department of Environment and Planning Centre for Mechanical Technology and Automation University of Aveiro Aveiro Portugal

Peri-urban and rural mobility presents unique challenges due to its reliance on fossil fuels and the lack of comprehensive studies on its externalities. Furthermore, the provision of efficient public transport in peri-urban areas is complicated due to fluctuating demand patterns. In this context, this paper explores the optimisation of a bimodal (bus line and a diesel railway line) public transport corridor in the peri-urban area of a European medium-sized city. A four-stage research methodology is employed: initial system characterisation, energy and environmental performance analysis, operational efficiency assessment, and the development and evaluation of alternative transport strategies. The study concludes that the variability in demand necessitates the implementation of innovative, complementary services that can adapt to changing passenger numbers while optimising existing resources. Findings of the case study indicate that aligning bus types with demand variability and integrating electric buses can lead to substantial reductions in CO2 emissions. The use of minibuses during off-peak hours could achieve a 50% reduction in emissions, while the adoption of a bus rapid transit (BRT) system may result in a 90% decrease compared to conventional diesel trains. The research underlines the potential for innovative service models to utilise existing infrastructure more efficiently. © 2025 The Author(s). IET Intelligent Transport systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Intermodal transportation

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Certificated Actor-Critic: Hierarchical Reinforcement Learning with Control Barrier Functions for Safe Navigation

arXiv

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arXiv 2025年

作者： Xie, Junjun Zhao, Shuhao Hu, Liang Gao, Huijun Department of Automation School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

Control Barrier Functions (CBFs) have emerged as a prominent approach to designing safe navigation systems of robots. Despite their popularity, current CBF-based methods exhibit some limitations: optimization-based safe control techniques tend to be either myopic or computationally intensive, and they rely on simplified system models;conversely, the learning-based methods suffer from the lack of quantitative indication in terms of navigation performance and safety. In this paper, we present a new model-free reinforcement learning algorithm called Certificated Actor-Critic (CAC), which introduces a hierarchical reinforcement learning framework and well-defined reward functions derived from CBFs. We carry out theoretical analysis and proof of our algorithm, and propose several improvements in algorithm implementation. Our analysis is validated by two simulation experiments, showing the effectiveness of our proposed CAC algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Liquid refractive index measurement method based on orthogonal polarization laser self-mixing interference

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Optoelectronics Letters 2025年

作者： ZHAO Yan WANG Haipeng DU Shengzhi TONG Jigang KANG Jun School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Department of Electrical Engineering， Tshwane University of Technology

In this paper， a novel method combining orthogonal polarization laser self-mixing interference is proposed. The method utilizes a rotating cuvette to measure the refractive index of liquids at different concentrations. The cuvette is filled with the liquid to be measured and rotated by a certain angle. The change in the number of interference fringes， caused by comparing an empty cuvette with a liquid-filled cuvette， is used to calculate the refractive index of the liquid. A four-fold logic subdivision algorithm is then used to improve measurement resolution. The experimental results show that for pure water and different NaCl and glucose solutions concentrations， the average relative errors are 0.47%， 0.59%， and 2.17%， respectively， with the maximum relative error within ±2.54%. The standard deviation of all solutions is less than 3.4%.

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Weak feedback self-mixing interference fringe slope discrimination method based on deep learning

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Optoelectronics Letters 2025年

作者： ZHAO Yan LIN Maohua DU Shengzhi TONG Jigang LIU Bin HAN Fangfang School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Department of Electrical Engineering， Tshwane University of Technology

In order to identify the tilt direction of the self-mixing interference (SMI) signals under weak feedback regime interfered by noise， a deep learning method is proposed. The one-dimensional U-Net (1D U-Net) neural network can discriminate the direction of self-mixing fringes accurately and quickly. For experimental SMI signals， the 1D U-Net can be used for discriminant direction after a one-step normalization. Simulation and experimental results show that the proposed method is suitable for SMI signals with noise within the whole weak feedback regime， and can maintain a high discrimination accuracy for signals interfered by 5dB noise. Combined with fringe counting method， accurate and rapid SMI signal displacement reconstruction can be realized.

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