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Quantitative analysis of the main components in ceramic raw materials based on the desktop LIBS analyzer

Plasma Science and Technology 2022年第8期24卷 46-54页

作者： Peng ZHANG Lanxiang SUN Haibin YU Peng ZENG State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016People's Republic of China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016People's Republic of China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110016People's Republic of China

The concentrations of SiO,Al2O,KO,NaO,CaO,MgO,Fe2Oand TiO,and loss on ignition(L.O.I.) are the main inorganic components of geological *** of the eight oxides and *** also the main indicators of concern in the production of building *** analysis of the eight oxides and *** performed using fiber-laserbased laser-induced breakdown spectroscopy(LIBS).A combination of continuous background deduction,full width at half maximum(FWHM) intensity integral and spectral sum normalization was proposed for data *** the data processing combined the continuous background deduction,FWHM intensity integral and spectral sum normalization,the mean absolute errors(MAEs) of the calibration of L.O.I.,SiO,Al2O,KO,NaO,CaO,MgO,Fe2Oand TiOwas reduced from 2.03%,12.06%,4.84%,1.10%,0.69%,0.31%,0.11%,0.20%and 0.10% to 1.80%,9.48%,2.12%,0.36%,0.58%,0.11%,0.08%,0.19% and 0.05%,*** multivariate method was further introduced and discussed to improve the analysis *** MAEs of L.O.I.,SiO,Al2O,KO and NaO were further reduced to1.12%,2.07%,1.38%,0.35% and 0.43%,*** results show that the overall prediction error can meet the requirements for the production of building *** LIBS desktop analyzer has great potential in detection applications on geological samples.

关键词： laser-induced breakdown spectroscopy fiber laser quantitative analysis matrix effects geological samples

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Development of a novel hand-eye calibration for intuitive control of minimally invasive surgical robot

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Frontiers of Mechanical Engineering 2022年第3期17卷 91-108页

作者： Yanwen SUN Bo PAN Yili FU State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin150001China

Robotic-assisted surgical system has introduced a powerful platform through dexterous instrument and hand-eye coordination intuitive *** knowledge of laparoscopic vision is a crucial piece of information for robot-assisted minimally invasive surgery focusing on improved surgical *** the transformation with respect to the laparoscope and robot slave arm frames using hand-eye calibration is essential,which is a key component for developing intuitive control *** proposed a novel two-step modified dual quaternion for hand-eye calibration in this *** dual quaternion was exploited to solve the hand-eye calibration simultaneously and powered by an iteratively separate *** obtained hand-eye calibration result was applied to the intuitive control by using the hand-eye coordination *** simulations and experimental studies were conducted to evaluate the proposed method on our surgical robot *** extensively compared the proposed method with state-of-the-art *** demonstrate this method can improve the calibration *** effectiveness of the intuitive control algorithm was quantitatively evaluated,and an improved hand-eye calibration method was *** relationship between laparoscope and robot kinematics can be established for intuitive control.

关键词： minimally invasive surgery hand-eye calibration intuitive control surgical robot dual quaternion

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Short-term load forecasting of long-short term memory neural network based on genetic algorithm 4

Short-term load forecasting of long-short term memory neural...

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4th IEEE Conference on Energy Internet and Energy System Integration, EI2 2020

作者： Li, Wanting Zang, Chuanzhi Liu, Ding Zeng, Peng Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems State Key Laboratory of Robotics Institutes for Robotics and Intelligent Manufacturing Beijing China

ISBN: (纸本)9781728196060

Accurate load forecasting is conducive to the reasonable arrangement of power grid dispatching plans. Traditional load forecasting methods cannot handle the time series and nonlinear characteristics of load well. Long-short term memory (LSTM) neural networks can record long-term and short-term information, which can effectively solve this kind of problem. But the parameters of LSTM network are difficult to determine. For this reason, this paper proposes a long-short term neural network based on genetic algorithm. The learning rate and iteration number of the LSTM network are used as chromosomes, and the genes are continuously selected, crossed, and mutated to obtain more good genes. Comparing this method with the standard LSTM network, the simulation results show that the LSTM network using genetic algorithm for parameter optimization improves the prediction accuracy of the standard LSTM network by 63%. © 2020 IEEE.

关键词： Genetic algorithms

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Sagittal SLIP-anchored task space control for a monopode robot traversing irregular terrain

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Frontiers of Mechanical Engineering 2020年第2期15卷 193-208页

作者： Haitao YU Haibo GAO Liang DING Zongquan DENG State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China

As a well-explored template that captures the essential dynamical behaviors of legged locomotion on sagittal plane,the spring-loaded inverted pendulum(SLIP)model has been extensively employed in both biomechanical study and robotics *** at fully leveraging the merits of the SLIP model to generate the adaptive trajectories of the center of mass(CoM)with maneuverability,this study presents a novel two-layered sagittal SLIP-anchored(SSA)task space control for a monopode robot to deal with terrain *** work begins with an analytical investigation of sagittal SLIP dynamics by deriving an approximate solution with satisfactory apex prediction accuracy,and a two-layered SSA task space controller is subsequently developed for the monopode *** higher layer employs an analytical approximate representation of the sagittal SLIP model to form a deadbeat controller,which generates an adaptive reference trajectory for the *** lower layer enforces the monopode robot to reproduce a generated CoM movement by using a task space controller to transfer the reference CoM commands into joint torques of the multi-degree of freedom monopode ***,an adaptive hopping behavior is exhibited by the robot when traversing irregular *** results have demonstrated the effectiveness of the proposed method.

关键词： legged robots spring-loaded inverted pendulum task space control apex return map deadbeat control irregular terrain negotiation

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Light weight design and integrated method for manufacturing hydraulic wheel-legged robots

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2024年第9期25卷 701-715页

作者： Xu LI Haoyang YU Huaizhi ZONG Haibo FENG Yili FU State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310058China

Design and manufacturing play pivotal roles in hydraulic-driven robotic ***,previous studies have emphasized mainly results and performance,often overlooking the specifics of the design and manufacturing *** paper introduces a novel approach known as light weight design and integrated manufacturing(LD&IM)for hydraulic wheel-legged *** LD&IM method leverages topology optimization and generative design techniques to achieve a substantial 45%weight reduction,enhancing the robot’s dynamic motion *** innovative design method not only streamlines the design process but also upholds the crucial attributes of light weight construction and high strength essential for hydraulic wheel-legged ***,the integrated manufacturing method,incorporating selective laser melting(SLM)and high-precision subtractive manufacturing(SM)processes,expedites the fabrication of high-quality *** the LD&IM approach,a hydraulic-driven single wheel-legged robot,denoted as WLR-IV,has been successfully *** robot boasts low mass and inertia,high strength,and a simplified component *** assess its dynamic jumping capabilities,the control loop integrates a linear quadratic regulator(LQR)and zero dynamic-based controller,while trajectory planning uses the spring-loaded inverted pendulum(SLIP)*** jumping results confirm the WLR-IV single-legged robot’s exceptional dynamic performance,validating both the effectiveness of the LD&IM method and the rationale behind the control strategy.

关键词： Wheel-legged robot Hydraulic driven Topology optimization Wheel-legged robot Hydraulic driven Topology optimization Additive manufacturing(AM) Jump control

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Joint Task Offloading and Uav Trajectory Optimization in Dynamic Edge Computing systems: A Deep Reinforcement Learning Approach

SSRN

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SSRN 2025年

作者： Wang, Yitian Wang, Hui Ding, Jingfang Yu, Haibin School of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

This paper addresses latency challenges in cloud-based task offloading caused by geographical server-user disparities by integrating Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC). We propose a dynamic UAV-assisted framework that optimizes real-time parameter adjustments to minimize system latency. key challenges include joint task offloading, UAV trajectory optimization, and User Equipment (UE) occlusion detection under mobility constraints. To resolve these, we transform the problem into a Markov Decision Process (MDP) and develop an enhanced Adaptive Delayed Deep Deterministic Policy Gradient (AD3PG) algorithm, which improves upon DDPG by incorporating delayed updates and neural network tuning. The algorithm dynamically optimizes three critical aspects: UAV-UE connectivity establishment, occlusion-aware dual noise power configuration, and adaptive task offloading ratios. Extensive simulations demonstrate AD3PG’s superiority over baseline methods such as DDPG and Twin-Delayed DDPG (TD3) in reducing total system latency by 13.2–35.3% under dynamic scenarios. These results validate the framework’s efficacy for latency-sensitive applications in UAV-MEC systems. © 2025, The Authors. All rights reserved.

关键词： Drones

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Industrial Wireless control Networks:From WIA to the Future

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Engineering 2022年第1期8卷 18-24页

作者： Haibin Yu Peng Zeng Chi Xu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China

*** Industrial automation is undergoing a significant innovation as information,communication,and operation technologies are deeply integrating with each *** this trend,industrial wireless control networks(IWCNs)are becoming increasingly attractive to industrial automation since they can help speed up production efficiency,reduce cost,enhance safety,and finally realize intelligent manufacturing[1].

关键词： attractive integrating becoming

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Multi-hop Reasoning over Sparse Knowledge Graphs with Deep Reinforcement Learning

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Expert systems with Applications 2025年

作者： Yiyang Liu Lejun Fu Yukai Fu Tianxing Wu Hongfei Bai State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang 110016 China College of Information Engineering Shenyang University of Chemical Technology Shenyang 110142 China School of Computer Science and Engineering Southeast University Nanjing 211189 China

In recent years, knowledge reasoning has demonstrated substantial progress in inferring missing information, effectively alleviating the limitations caused by the incompleteness of knowledge graphs (KGs). Dealing with entities that have sufficient paths between them, multi-hop reasoning methods have achieved notable success and demonstrated interpretability, but they still exhibit deficiencies when dealing with sparse KGs. To overcome the insufficiency of edges in sparse KGs and the absence of effective guidance in multi-hop reasoning methods, a hybrid multi-hop collaborative Multi-Agent Sparse Knowledge Graph Reasoning model (MASKGR) is proposed. Attention-based dynamic completion (ADC) and variable rule guidance(VRG) are used to enhance the reasoning capability of deep reinforcement learning (RL) in sparse KGs. ADC focuses on high-value relations based on attention weights and combines embedding-based methods to predict the tail entities of these relations, expanding the original action space and improving the exploration capability of agents. Moreover, VRG greatly improves the ability of MASKGR to use rule-guided agent learning by utilizing the variable rule ratio and the variable loss reshaping. ADC can be applied to both single-agent and multi-agent in other models, while VRG is specifically designed for multi-agent. Comprehensive experiments have been conducted on four datasets: FB15K-237-10%, FB15K-237-50%, NELL23K, and WD-singer. Specifically, MASKGR achieves relative improvements of 1.3% in Mean Reciprocal Rank (MRR) and 1.1% in the proportion of correct tail entity rankings in the top 3 (Hit@3) on the FB15K-237-10%, 2.4% in MRR and 1.3% in Hit@3 on the NELL23K, and 2.7% in MRR and 3.6% in Hit@3 on the WD-singer. The results indicate that MASKGR outperforms state-of-the-art multi-hop reasoning models based on reinforcement learning.

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A Braille Reading System Based on Electrotactile Display With Flexible Electrode Array

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 735-737页

作者： Ziliang Zhou Yicheng Yang Honghai Liu IEEE the State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University the State Key Laboratory of Robotics and Systems Harbin Institute of Technology Shenzhen

Dear editor,Due to visual impairment and lack of social attention,the reading problem of the blind has not been well *** existing Braille reading devices still have many problems and are not accepted by the *** paper developed a portable Braille reading system based on electrotactile display *** proposed system is composed of a six-channel electrotactile stimulator,a flexible electrode array for Braille display and a graphical user interface(GUI)for monitoring and *** on the flexibility of the system,two Braille reading strategies have been designed:spatial mode and sequential *** system was preliminary tested by six sighted *** results showed that subjects were able to recognize Braille characters with more than 80%accuracy within less than 10 seconds for the initial *** to existing Braille reading systems,this system is portable,wearable and flexible in control.

关键词： portable editor Bra

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A Nonlinear MPC-Net Optimization Framework for Wheeled Humanoid Robots with Whole Body Dynamics

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IEEE Transactions on Automation Science and Engineering 2025年

作者： Li, Guoxin Liu, Xingjian Wu, Xiaoyu Tang, Qirong Zhang, Hao Li, Zhijun Shi, Peng Tongji University School of Mechanical Engineering Translational Research Center Shanghai YangZhi Rehabilitation Hospital Shanghai Sunshine Rehabilitation Center Shanghai200092 China Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Shanghai201804 China State Key Laboratory of High-Performance Precision Manufacturing Dalian116024 China Tongji University School of Mechanical Engineering Shanghai201804 China National University of Singapore Department of Biomedical Engineering 117583 Singapore Tongji University Department of Control Science and Engineering Shanghai201804 China The University of Adelaide School of Electrical and Mechanical Engineering SA5005 Australia Obuda University Budapest1034 Hungary

The task performance of mobile manipulators can be significantly enhanced by whole-body control and optimization in complex scenarios. Due to the nonlinear properties of whole-body dynamics and parameter uncertainty, modeling accurate system dynamics is essential in addition to designing an effective control strategy. However, traditional control methods have high computational costs and fail to deal with the parameter errors caused by model linearization. To address these issues, we propose a model predictive control (MPC)-Net, a learning-based approach that facilitates rapid online optimization by combining deep learning with multiple MPCs. Firstly, we develop a parameter identification algorithm based on a deep neural model to estimate the unknown dynamics parameters. Although the control performance of the MPC approach positively correlated with the prediction horizon, a long horizon would result in additional computational costs. Thus, MPC-Net is constructed by combining multiple sub-MPC issues, and the nonlinear coefficients are obtained by using a deep neural network-based optimization framework. Furthermore, MPC-Net generates the solution by combining the outputs of multiple sub-MPC problems using the nonlinear transformation of learned coefficients. Experiments are conducted on a mobile manipulator, which demonstrates the proposed MPC-Net-based optimization control offers fast efficient computation and low tracking error performance. © 2004-2012 IEEE.

关键词： Predictive control systems

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