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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Li, Dawei Chen, Xiaolu Song, Chunhe Yu, Shimao Start Grid Shanghai Information Telecommunication Company Shanghai200122 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

There are various types of power Internet of Things terminals, and a unified edge-side information fusion model is the basis of data unification. This article describes the unified modeling method of power Internet of Things terminal information, and describes the construction method of the Internet of Things terminal information model from three aspects: attribute, message and service. Combined with the characteristics of the electric power Internet of Things information model, a model verification method is given. Finally, through the terminal data on the edge side of the power Internet of Things, the feasibility and effectiveness of the model verification method are verified. © 2020 IEEE.

关键词： Internet of things

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Efficient Learning and control of String- Type Artificial Muscle Driven Robotic systems

Efficient Learning and Control of String- Type Artificial Mu...

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American control Conference (ACC)

作者： Jiyue Tao Sunil Kumar Rajendran Yunsong Zhang Feitian Zhang Dexin Zhao Tongsheng Shen The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China The National Innovation Institute of Defense Technology Beijing China The BSS Technologies Inc. Takoma Park MD USA The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper investigates the learning-based control of robotic systems driven by string-type artificial muscles. Due to the highly nonlinear dynamics of the actuators and the complicated mechanical structure, it is typically very challenging to design traditional model-based controllers that exhibit desired control performances. With the rapid development of machine learning techniques, deep reinforcement learning (DRL) algorithms have been utilized to control a variety of complex robotic systems. However, these DRL algorithms usually require a huge amount of training data and iterations to converge, which is generally unacceptable for the robotic systems of interest. Therefore, this paper designs an efficient learning-based control algorithm, aiming to improve the training efficiency for robotic systems driven by string-type artificial muscle actuators. Specially, two training improvements are proposed including imitation learning and data augmentation. This paper applies the proposed learning methods to three popular DRL algorithms and tests the control performances in three case studies using three string-type artificial muscle-driven robots, including a parallel robotic wrist, a two degrees-of-freedom (DOF) robotic eye and a robotic finger. Simulation results show that the proposed learning-based control methods significantly accelerate the convergence speed and improve the data efficiency in all the three case studies.

关键词： Artificial muscles Wrist Training Actuators Machine learning algorithms Imitation learning Fingers

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Edge computing-based defect identification model of power IoT edge side devices

Edge computing-based defect identification model of power Io...

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2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Li, Dawei Jia, Gengtao Song, Chunhe Yu, Shimao Start Grid Shanghai Information Telecommunication Company Shanghai200122 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 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang11016 China

ISBN: (纸本)9781728152240

There are various types of devices on the terminal side of the power Internet of Things, and the data collected through various sensors shows an exponential growth. The current in-depth application of edge computing technology can not only ease the pressure of network transmission, but also improve the timeliness of fault diagnosis. Edge computing technology is A new way to solve the fault diagnosis of equipment on the edge of the power grid. From the perspective of edge fault diagnosis, this paper expounds the fault diagnosis model of power IoT edge devices based on edge computing, and combines the isolated forest algorithm to design smart sensor data-based power IoT edge device fault diagnosis algorithms. Finally, the validity of the proposed framework model is verified by public sample data. © 2020 IEEE.

关键词： Failure analysis

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FMS:Features Motion Statistics for Incorrect Matched-pair Removal

FMS:Features Motion Statistics for Incorrect Matched-pair Re...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Hao Shu Zhitao Liu Hongye Su Yue Xia Polytechnic Institute Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781665476881

The matching of incorrect pairs affects the precision of the SLAM/VO system. Because the calculation is complex and time-consuming, the various limitation methods struggle to meet the system's real-time requirements. FMS is a novel approach that adapts well to both sparse and dense mapping processes, allowing for the verification of matched pairs of features. By displacing features between two consecutive frames, the projection formula contacts motion patterns. This classifier's overall characteristics demonstrated that it was extremely effective at applying suitable matched pairings from mismatched features. The proposed algorithm outperformed the raw technique in terms of accuracy and stability when compared to the ORB-SLAM technique.

关键词： Frequency modulation Simultaneous localization and mapping Automation Power system stability Real-time systems Classification algorithms Robots

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Efficient Large Scale Stereo Matching based on Cross-Scale

Efficient Large Scale Stereo Matching based on Cross-Scale

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Yue Xia Zhitao Liu Hongye Su Hao Shu Polytechnic Institute Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781665476881

We propose a binocular stereo matching algorithm CS-ELAS. This is a cross-scale ELAS algorithm that improves the accuracy and robustness of parallax in weakly textured regions and edge regions. Our approach focuses on improving the accuracy and number of support point sets. We uniformly sample the stereo images to obtain candidate support point sets and determine robustly matched support point sets based on an adaptive cross skeleton. In this way a richer and more accurate set of support points can be obtained in the weakly textured areas near the edges. In addition, our method uses the parallax and confidence maps of low-resolution images as a priori for high-resolution images and adds high-confidence pixel information to the set of high-resolution support points. In this way, it not only increases the number of support points in weak texture regions, but also narrows the search range of candidate support points and reduces the computational cost.

关键词： Three-dimensional displays Image resolution Automation Image edge detection Benchmark testing Skeleton Robustness

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Optimized Configuration of Location and Size for DGs and SCs in Radial Distributed Networks Based on Improved Butterfly Algorithm

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IAENG International Journal of Applied Mathematics 2022年第1期52卷 1页

作者： Chen, Gonggui Zhao, Xinxin Peng, Kang Zhou, Ping Zeng, Xianjun Long, Hongyu Zou, Mi Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing400065 China Economic and Technology Research Institute State Grid Chongqing Electric Power Company Chongqing401120 China State Grid Chongqing Electric Power Company Chongqing401120 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

Abstract—The optimal configuration problem of distributed generators (DGs) and shunt capacitors (SCs) in a radial distributed network (RDN) is to find the best installation locations and optimal capacities of DGs and SCs for optimizing a certain performance indicator. The discontinuity characteristic and huge computation of optimizing DGs and SCs in RDN make it no longer applicable by traditional methods. In this paper, a new bus processing method and an improved butterfly algorithm are proposed to solve the optimal configuration of DGs and SCs. The method of processing nodes considers not only the power loss index (PLI) but also the voltage amplitude of the original system, forming a sequence of candidate nodes to guide algorithm to optimizing, which can simplify the algorithm search space and improve search efficiency. Meanwhile, the butterfly algorithm with constriction factor (BF-CF) combines the inertia coefficient to introduce a constriction factor, improves the speed update model and the local search pattern, and overcomes the disadvantages of the original butterfly algorithm which is easy to fall into the local optimum and the precision of result is not high. To verify the performance of the proposed method, minimizing the active power loss and voltage deviation are selected as the objective function and reactive power loss and the worst voltage are taken as reference targets, which are performed in three standard test systems of 33-bus, 69-bus and 119-bus, respectively. The simulation results illustrate that, compared with the original butterfly algorithm and other intelligent algorithms, the method proposed in this paper has more obvious advantages and performance in solving the configuration problem of DGs and SCs in systems of various scales. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Distributed power generation

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Mechanical Characterization Analysis of Kirigami Structure for Soft Robotic Design

Mechanical Characterization Analysis of Kirigami Structure f...

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Automation, control and robotics Engineering (CACRE), International Conference on

作者： Muhammad Niaz Khan Amin Lotfiani Umair Javaid Zhufeng Shao Department of Mechanical Engineering State Key Laboratory of Tribology Institute of Manufacturing Engineering Tsinghua University Department of Mechanical Engineering Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control Tsinghua University Beijing China Faculty of Engineering Technology University of Twente Enschede Netherland Robotics Institute Ningbo University of Technology Ningbo China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

This paper provides a detailed mechanical characterization and stress analysis of kirigami sheets across various structural configurations, highlighting their potential applications in soft robotics. The study examines linear, circular, triangular, and trapezoidal kirigami patterns through finite element simulations, complemented by experimental validation. Deformation parameters are systematically applied to both single and multiple units of kirigami sheets to assess their capacity for mechanical elongation and load sustainability. Additionally, friction analyses are discussed via simulation to explore the anchoring effects of these kirigami structures when applied to soft robots. The results of this study enhance our understanding of the mechanical properties of kirigami structures and their practical implications for soft robotics design.

关键词： Automation Deformation Friction Soft robotics Finite element analysis Sustainable development Stress

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Short-term Load Forecasting of Long-short Term Memory Neural Network Based on Genetic Algorithm

Short-term Load Forecasting of Long-short Term Memory Neural...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

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

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

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%.

关键词： Load forecasting Neural networks Time series analysis System integration Standards Biological cells Genetic algorithms

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Event-aided Semantic Scene Completion

arXiv

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arXiv 2025年

作者： Guo, Shangwei Shi, Hao Wang, Song Yin, Xiaoting Yang, Kailun Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Autonomous driving systems rely on robust 3D scene understanding. Recent advances in Semantic Scene Completion (SSC) for autonomous driving underscore the limitations of RGB-based approaches, which struggle under motion blur, poor lighting, and adverse weather. Event cameras, offering high dynamic range and low latency, address these challenges by providing asynchronous data that complements RGB inputs. We present DSEC-SSC, the first real-world benchmark specifically designed for event-aided SSC, which includes a novel 4D labeling pipeline for generating dense, visibility-aware labels that adapt dynamically to object motion. Our proposed RGB-Event fusion framework, EvSSC, introduces an Event-aided Lifting Module (ELM) that effectively bridges 2D RGB-Event features to 3D space, enhancing view transformation and the robustness of 3D volume construction across SSC models. Extensive experiments on DSEC-SSC and simulated SemanticKITTI-E demonstrate that EvSSC is adaptable to both transformer-based and LSS-based SSC architectures. Notably, evaluations on SemanticKITTI-C demonstrate that EvSSC achieves consistently improved prediction accuracy across five degradation modes and both In-domain and Out-of-domain settings, achieving up to a 52.5% relative improvement in mIoU when the image sensor partially fails. Additionally, we quantitatively and qualitatively validate the superiority of EvSSC under motion blur and extreme weather conditions, where autonomous driving is challenged. The established datasets and our codebase will be made publicly at https://***/Pandapan01/EvSSC. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Robust Iterative Learning control of Dual-Driven Crossbeam System

Robust Iterative Learning Control of Dual-Driven Crossbeam S...

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Annual Conference of Industrial Electronics Society

作者： Yanbin Liu Kai Che Jianing Liu Jue Wang Huihui Pan Weichao Sun School of Astronautics Harbin Institute of Technology Harbin China Aeronautics Computing Technique Research Institute Aviation Industry Corporation of China Xi'an China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

Dual-drive crossbeam system has been an significant part of high-end equipment, whose synchronous accuracy and robustness determine the performance if equipment. Thus, a robust iterative learning control method is proposed, which is based on the cross-coupling model. Robust term is to deal with the non-repetitive disturbance and iterative learning term is to compensate the unknown dynamics by repeating the same task. Finally, different control methods are conducted to verify the effectiveness of the proposed method.

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