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A Flexible-Integrated Multimodal Hydrogel-Based Sensing Patch

Nano-Micro Letters 2025年第7期17卷 115-133页

作者： Peng Wang Guoqing Wang Guifen Sun Chenchen Bao Yang Li Chuizhou Meng Zhao Yao School of Mechanical Engineering University of Jinan School of Information Science and Engineering University of Jinan State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human-Robot InteractionSchool of Mechanical EngineeringHebei University of Technology School of Integrated Circuits Shandong University State Key Laboratory of Integrated Chips and Systems Fudan University College of Electronics and Information Qingdao University

Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health. However, current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body. Herein, we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring. The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device. The hydrogel as core substrate exhibits strong toughness and water retention, and the multimodal sensing of temperature, pressure, and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference. The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability. Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring. Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network. Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved, which provides a promising approach for sleep monitoring.

关键词： Electrospun nanofibers Multimodal sensing Pressure sensor Proximity sensor Temperature sensor

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Integrating Friction Noise for In Situ Monitoring of Polymer Wear Performance: A Machine Learning Approach in Tribology

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Journal of Tribology 2025年第6期147卷 061701页

作者： Chen, Shengshan Cheng, Ganlin Guo, Fei Jia, Xiaohong Wen, Xiaohao State Key Laboratory of Tribology in Advanced Equipment Tsinghua University Beijing100084 China Macau Institute of Systems Engineering Macau University of Science and Technology 999078 China

Friction and wear between mating surfaces significantly affect the efficiency and performance of mechanical systems. Traditional tribological research relies on post-observation methods, limiting the understanding of dynamic friction behavior. In contrast, in situ monitoring provides real-time insights into evolving friction dynamics. This study employs machine learning to monitor polymer wear performance through friction noise. The predictive accuracy of various machine learning methods, including Extremely Randomized Trees, Gradient-Boosting Decision Trees, AdaBoost, LightGBM, Deep Forest, and Deep Neural Networks, is compared for wear-type classification. Additionally, the LSBoost regression is selected as the optimal method for predicting polymer wear-rates across various temperatures. The results underscore the potential of using friction noise and machine learning for real-time wear monitoring, offering valuable insights for tribological system maintenance and failure prediction. © 2024 by ASME.

关键词： Tribology

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Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning

arXiv

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arXiv 2024年

作者： Ji, Yiming Yun, Kaijie Liu, Yang Xie, Zongwu Liu, Hong State Key Laboratory of Robotics and Systems Harbin Institute of Technology China

Q-learning is a widely used reinforcement learning technique for solving path planning problems. It primarily involves the interaction between an agent and its environment, enabling the agent to learn an optimal strategy that maximizes cumulative rewards. Although many studies have reported the effectiveness of Q-learning, it still faces slow convergence issues in practical applications. To address this issue, we propose the NDR-QL method, which utilizes neural network outputs as heuristic information to accelerate the convergence process of Q-learning. Specifically, we improved the dual-output neural network model by introducing a start-end channel separation mechanism and enhancing the feature fusion process. After training, the proposed NDR model can output a narrowly focused optimal probability distribution, referred to as the guideline, and a broadly distributed suboptimal distribution, referred to as the region. Subsequently, based on the guideline prediction, we calculate the continuous reward function for the Q-learning method, and based on the region prediction, we initialize the Q-table with a bias. We conducted training, validation, and path planning simulation experiments on public datasets. The results indicate that the NDR model outperforms previous methods by up to 5% in prediction accuracy. Furthermore, the proposed NDR-QL method improves the convergence speed of the baseline Q-learning method by 90% and also surpasses the previously improved Q-learning methods in path quality metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Dynamic Finite Element Modeling and Simulation of Soft Robots

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Chinese Journal of Mechanical Engineering 2022年第2期35卷 45-55页

作者： Liang Ding Lizhou Niu Yang Su Huaiguang Yang Guangjun Liu Haibo Gao Zongquan Deng State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China Department of Aerospace Engineering Ryerson UniversityTorontoON M5B 2K3Canada

Soft robots have become important members of the robot community with many potential applications owing to their unique flexibility and security embedded at the material *** increasing number of researchers are interested in their designing,manufacturing,modeling,and ***,the dynamic simulation of soft robots is difficult owing to their infinite degrees of freedom and nonlinear characteristics that are associated with soft materials and flexible geometric *** this study,a novel multi-flexible body dynamic modeling and simulation technique is introduced for soft *** actuators for soft robots are modeled in a virtual environment,including soft cable-driven,spring actuation,and pneumatic driving.A pneumatic driving simulation was demonstrated by the bending modules with different materials.A cable-driven soft robot arm prototype and a cylindrical soft module actuated by shape memory alley springs inspired by an octopus were manufactured and used to validate the simulation model,and the experimental results demonstrated adequate *** proposed technique can be widely applied for the modeling and dynamic simulation of other soft robots,including hybrid actuated robots and rigid-flexible coupling *** study also provides a fundamental framework for simulating soft mobile robots and soft manipulators in contact with the environment.

关键词： Soft robot Finite-element modeling Dynamic simulation

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From Plant Modeling to Narrative Science:Growing and Enjoying Scientific Fruits Naturally and Humanistically

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2522-2524页

作者： Fei-Yue Wang IEEE Macao University of Science and Technology Macao 999078China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China

Dear Editor,Modeling is the first and essential step for control and automation,and large models,from current Chat GPT or large language models(LLMs)to future large knowledge models of knowledge automation,would be th... 详细信息

关键词： smart knowledge automation

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An Adaptive Feedforward Optimization Mechanism for Improving Industrial Robots Trajectory Tracking Performance through Iterative Learning

An Adaptive Feedforward Optimization Mechanism for Improving...

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robotics, Control and Automation (ICRCA), International Conference on

作者： Chengzhi Wang Sikai Zhao Tianjiao Zheng Hegao Cai Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

This paper presents an adaptive feedforward optimization method based on iterative learning control mechanism, improving trajectory tracking accuracy from trial to trail. Conventional feedforward control researches focus on the precise prediction and compensation of torques, either by means of dynamic modelling and parameter identification or by data-driven approaches. However, there exists unpredictable time-delay for torques to work. The proposed method takes the previously less-concerned velocity feedforward part of the servo control system into consideration, and a self-adaptive fine-tuning mechanism is designed and established. The error measurement and feedforward optimization processes are integrated into an iterative learning framework to obtain efficiency and precision. The effectiveness of proposed method is validated by physical experiments on an EFORT ER15 industrial robot, and an averagely 90.71% error reduction is witnessed.

关键词： Accuracy Torque Parameter estimation Trajectory tracking Service robots Predictive models Industrial robots Feedforward systems Servosystems Tuning

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Sensing-Communication-Computation Integration for Federated Edge Learning with Controllable Model Dropout

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IEEE Internet of Things Journal 2025年第12期12卷 19767-19781页

作者： Jiao, Xiang Zhu, Guangxu Jiang, Wei Chen, Li Luo, Wu Wen, Dingzhu State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Shenzhen Research Institute of Big Data Shenzhen China The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China The CAS Key Laboratory of Wireless Optical Communication University of Science and Technology of China Hefei China Network Intelligence Center School of Information Science and Technology ShanghaiTech University Shanghai China

FEderated Edge Learning (FEEL) is an advanced paradigm in edge artificial intelligence, enabling privacy-preserving collaborative model training through periodic communication between edge devices and a central server. FEEL involves three key processes: sensing, computation, and communication for data acquisition, processing, and exchange, respectively. Due to limited system resources, optimizing each process individually may lead to suboptimal learning performance. This challenge has sparked research into Integrated Sensing-Computation-Communication (ISCC) design for enhanced FEEL. While previous work has optimized general learning parameters such as batch size and computing frequency, there is a lack of customized designs considering the neural network architecture as an optimizable variable in ISCC for FEEL. To close this gap, we introduce a novel design where each device generates a sub-model through controllable weight dropout, adding flexibility by directly manipulating the learning process and reducing computation and communication overhead. To guide ISCC resource allocation in this new setting, we present a comprehensive convergence analysis, revealing the tight coupling of sensing, computation, and communication across devices and their impact on FEEL convergence. Building on these theoretical insights, we formulate an ISCC problem aiming to maximize the FEEL convergence rate through joint optimization of variables such as batch size, sensing power, dropout rate, and communication power. This non-convex problem is decomposed into two sub-problems via alternating optimization: one controls batch size using a sorting algorithm, while the other focuses on ISCC device parameters, transformable into a convex problem solved by successive convex approximation. Extensive experiments using human motion recognition datasets demonstrate the superiority of the proposed design over baseline schemes. © 2014 IEEE.

关键词： Federated learning

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Inverse Kinematics of a Reconfigurable Space Manipulator Based on the Closed-Loop Algorithm 20

Inverse Kinematics of a Reconfigurable Space Manipulator Bas...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Zhao, Zhiyuan Yang, Xiaohang Xu, Zichun Li, Yuntao Tian, Zhonglai Zhao, Jingdong State Key Laboratory of Robotics and System Harbin Institute of Technology Heilongjiang Province Harbin China

ISBN: (纸本)9798350320831

This paper investigates a reconfigurable space manipulator's inverse kinematics (IK) with two lockable passive telescopic links (LPTLs). Depending on these LPTLs' locking state, the manipulator includes free-space and reconfiguration operation modes. Firstly, we introduce a new ball joint-based solution for the reconfigurable operation mode. Then, we propose an IK simplified method capable of significantly reducing the computational load of the complex inverse differential kinematic (IDK) equations corresponding to the two operation modes. Next, to solve the resulting simplified IDK equations efficiently, we present an IK solution method based on the closed-loop algorithm. This method is developed to resolve the redundancy at the velocity level and can generate highly accurate and smooth trajectory IK solutions that satisfy the joint limit constraints. Finally, we design two typical case studies corresponding to working scenarios of the manipulator during two operation modes. Their simulation results validate the proposed methods' effectiveness. © 2023 IEEE.

关键词： Inverse kinematics

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For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation technology

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China Communications 2024年第2期21卷 85-95页

作者： Chengjie Li Lidong Zhu Zhen Zhang The Key Laboratory for Computer Systems of State Ethnic Affairs Commission School of Computer Science and Technology(Southwest Minzu University)Chengdu 610000China National Key Laboratory of Science and Technology on Communications(University of Electronic Science and Technology of China) Chengdu 610000China College of Computer Science(Sichuan University) Chengdu 610000China

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

关键词： blind source separation greedy optimization algorithm interference sensing LEO satellite communication networks signal reconstruction

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