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Ocean Studies (ICOS), International Conference on

作者： Irina Nemiro Anton Gubankov Laboratory of Marine Unmanned Aviation and Marine Aviation Systems Maritime State University Named after Admiral G.I. Nevelskoy Far Eastern Federal University Vladivostok Russia Robotics Laboratory Institute of Automation and Control Processes Maritime State University Named after Admiral G.I. Nevelskoy Far Eastern Federal University Vladivostok Russia

The paper considers the comparative study of different methods for state vector estimation of moving objects in conditions when data from sensors received with a low frequency, and the measurements themselves are very noisy. The Kalman filter is used for state vector estimation. Due to the low frequency of updating the measurement vector, it is proposed to increase the frequency of predicting the state of a moving object between these updates. The problem of identifying a moving object at noisy measurements is solved using three different methods: the nearest neighbor method, the probabilistic method of data association, and fuzzy logic. A comparative of the simulation results of the developed algorithm using the three considered methods is presented. When using fuzzy logic, significantly fewer false measurements are selected than with the other two methods, since the measurement of a moving object is selected based on several parameters of the object, but not only based on one parameter.

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Accelerated identification method of industrial instrument based on yolov5

Accelerated identification method of industrial instrument b...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Shimao Yu Haiyang Zheng Chunhe Song Pengpei Gao Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665464697

There are many problems in industrial instrument detection, such as complex background and visual Angle, etc. Traditional machine learning methods are difficult to achieve high-precision object detection and real-time monitoring applications. This paper proposes an accelerated identification method of industrial instruments based on YOLOv5. First, sample data are collected on site for image annotation, and YOLOv5 network structure is designed for transfer learning training, so as to realize image recognition of four types of industrial instruments. After that, the model is accelerated based on TensorRT on Jetson TX2 platform. The speed of instrument online detection reached 30 fps and mAP0.5 is 0.989.

关键词： Training Visualization Computer vision Image recognition Instruments Computational modeling Transfer learning

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Design of Cascaded Model Predictive controller for Modular Robot Joints

Design of Cascaded Model Predictive Controller for Modular R...

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第34届中国控制与决策会议

作者： Wang Bolun Zhang Tao Shi Zhengjin Zhang Hualiang School of Automation and Electrical Engineering Shenyang University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences

As the core driving force providers in the robot system of next generation,modular robot joint's motion and power performance directly affects the overall motion control effect of robot *** design starts from the servo control of the modular robot joint and follows the principle of model predictive control to devise a cascaded model predictive controller based on the mathematical model of a permanent magnet synchronous motor for the joint to substitute for the traditional dual-loop PI controller with current and velocity loops in the closed-loop vector control *** design also employs the MATLAB/Simulink platform to simulate the control *** simulation experiments illuminate that the design of the cascaded model predictive controller has better dynamic response performance than the traditional PI controller and effectively enhances system robustness.

关键词： Modular Robot Joints Motor servo control Model predictive control

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Intelligent electricity consumption forecasting and electricity-theft analysis method based on deep learning

Intelligent electricity consumption forecasting and electric...

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Electronic Information Engineering and Computer Technology (EIECT), International Conference on

作者： Shimao Yu Chunhe Song Pengpei Gao Haiyang Zheng Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

Residential electricity consumption data have great mining value for electricity-theft analysis and electricity consumption forecasting. This paper designs an intelligent electricity consumption forecasting and electricity-theft analysis method based on deep learning. Firstly, the power consumption data of a single user is converted into multi-period data through preprocessing, and the data forecasting is realized by designing a deep neural network model. The electricity-theft data are first amplified through user’s power data and electricity-theft labels. And then the time series are converted into two-dimensional tensors for classification and recognition by deep CNN. The classification accuracy of electricity-theft reaches 92.2% compared with RNN method.

关键词： Deep learning Training Analytical models Power demand Tensors Computational modeling Time series analysis

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Corrosion fault identification model of substation grounding grid based on PSO-LS-SVM 4

Corrosion fault identification model of substation grounding...

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4th International Conference on Mechanical, Electric and Industrial Engineering, MEIE 2021

作者： Qin, Jun Miao, Jin Wu, Xi Wang, Zibo Shao, Shuai State Grid Wuxi Power Supply Company Wuxi Jiangsu China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

As one of the important equipment to ensure the normal operation of substation, the performance of grounding grid has been highly concerned. In recent years, researchers propose that the theory of electromagnetic induction can be used to diagnose and identify the corrosion fault of substation grounding grid. In this paper, a fault classification model based on LS-SVM optimized by PSO is proposed to identify the corrosion fault of grounding grid. Firstly, the wavelet packet transform principle is used to filter the original data, and the wavelet packet energy is used to construct the fault eigenvalue as the input of the fault classification model. Based on LS-SVM, a corrosion fault classification model is constructed, and particle swarm optimization method is used to optimize the parameters of the model, which solves the problem of traditional SVM parameter optimization. Through the practical application in substation, the model proposed in this paper can identify the corrosion of grounding grid conductor without excavation and power failure, which provides an effective scheme for engineering application. © Published under licence by IOP Publishing Ltd.

关键词： Corrosion

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On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification

arXiv

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arXiv 2022年

作者： Mu, Biqiang Chen, Tianshi Kong, He Jiang, Bo Wang, Lei Wu, Junfeng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Key Laboratory for NSLSCS of Jiangsu Province School of Mathematical Sciences Nanjing Normal University Nanjing China College of Control Science and Engineering Zhejiang University Hangzhou China

Input design is an important problem for system identification and has been well studied for the classical system identification, i.e., the maximum likelihood/prediction error method. For the emerging regularized system identification, the study on input design has just started, and it is often formulated as a non-convex optimization problem that minimizes a scalar measure of the Bayesian mean squared error matrix subject to certain constraints, and the state-of-art method is the so-called quadratic mapping and inverse embedding (QMIE) method, where a time domain inverse embedding (TDIE) is proposed to find the inverse of the quadratic mapping. In this paper, we report some new results on the embeddings/inverse embeddings of the QMIE method. Firstly, we present a general result on the frequency domain inverse embedding (FDIE) that is to find the inverse of the quadratic mapping described by the discrete-time Fourier transform. Then we show the relation between the TDIE and the FDIE from a graph signal processing perspective. Finally, motivated by this perspective, we further propose a graph induced embedding and its inverse, which include the previously introduced embeddings as special cases. This deepens the understanding of input design from a new viewpoint beyond the real domain and the frequency domain viewpoints. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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A Non-intrusive Appliances Load Monitoring Method Based on Hourly Smart Meter Data 9th

A Non-intrusive Appliances Load Monitoring Method Based on H...

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9th International Conference on Computer Engineering and Networks, CENet2019

作者： Song, Chunhe Wang, Zhongfeng Liu, Shuji Xu, Libo Zhou, Dapeng Zeng, Peng Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China State Grid Liaoning Electric Power Co. Ltd. Shenyang110000 China

ISBN: (纸本)9789811537523

Peak load management is very important for the electric power system. This paper analyzes the impact of residential swimming pool pumps (RSPPs) on the peak load. First, this paper analyzes the challenges of non-intrusive energy consumption estimation for SPPs. Second, a novel reference-based change-point (RCP) model is proposed for non-intrusive SPPs energy consumption estimation. The advantages of the proposed RCP model are that it does not require high sampling rate data or prior information of the appliance. We show that during pool season, under the assumption that the ratio of base loads (defined as the power consumption which is independent of the outdoor temperature) of houses with and with PPs remains the same during no-pool season and pool season, 6.3% of the total energy is consumed by PPs, while under the assumption that for houses with and without PPs, the ratio of base loads is equal to the ratio of the temperature-dependent power consumption during pool season, 9.08% of the total energy is consumed by PPs. Furthermore, we show that by shifting PPs activity period, under the first assumption, at least 1.27% of peak demand can be reduced, while under the second assumption, at least 4.53% of peak demand can be reduced. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Electric power utilization

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Research on Distributed Intelligent Protection Strategy of distribution network based on Reinforcement Learning 2

Research on Distributed Intelligent Protection Strategy of d...

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2nd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Cui, Shijie Zeng, Peng Hu, Ende 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 University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China State Grid Shaoxing Power Supply Company Shaoxing312099 China

ISBN: (纸本)9781665428767

In the context of building a new power system with new energy as the main part, the operation mode of distribution network is more complex, and the traditional distribution network protection strategy can hardly meet the demand, so this paper proposes a distributed intelligent protection strategy for distribution network based on reinforcement learning. This paper analyzes the impact of new energy resources access on distribution network relay protection, and presents an intelligent protection algorithm model based on deep reinforcement learning. The structure of intelligent protection device is designed, and the distribution network distributed protection scheme based on intelligent protection device is proposed. Finally, the algorithm training process is given, and the experimental environment is built. © 2021 IEEE.

关键词： Reinforcement learning

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Self Recovery of Localization Loss for Indoor Mobile Robot

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Journal of Harbin Institute of Technology(New Series) 2020年第2期27卷 46-57页

作者： Lin Jiang Han Wang Bin Lei Jianyang Zhu Huaiguang Liu Hui Zhao Key Laboratory of Metallurgical Equipment and Control Technology Wuhan University of Science and TechnologyWuhan 430081China Institute of Robotics and Intelligent Systems Wuhan University of Science and TechnologyWuhan 430081China

In order to solve the problem of localization loss that an autonomous mobile robot may encounter in indoor environment,an improved Monte Carlo localization algorithm is proposed in this *** algorithm can identify the state of the robot by real time monitoring of the mean weight changes of the particles and introduce more high weight particles through the divergent sampling function when the robot is in the state of localization *** observation model will make the particle set slowly approach to the real position of the robot and the new particles are then sampled to reach the *** loss self recovery experiments of different algorithms under different experimental scenarios are presented in this paper.

关键词： indoor mobile robot self recovery localization loss improved Monte Carlo localization algorithm

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CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination

arXiv

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arXiv 2022年

作者： Zeng, Guangyang Chen, Shiyu Mu, Biqiang Shi, Guodong Wu, Junfeng School of Data Science Chinese University of Ho ng Kong Shenzhen Shenzhen China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW2008 Australia

The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies. With the development of feature extraction techniques, a large number of feature points might be available in a single shot. It is promising to devise a consistent estimator, i.e., the estimate can converge to the true camera pose as the number of points increases. To this end, we propose a consistent PnP solver, named CPnP, with bias elimination. Specifically, linear equations are constructed from the original projection model via measurement model modification and variable elimination, based on which a closed-form least-squares solution is obtained. We then analyze and subtract the asymptotic bias of this solution, resulting in a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed to refine the consistent solution. Our proposed estimator is efficient in terms of computations—it has O(n) computational complexity. Experimental tests on both synthetic data and real images show that our proposed estimator is superior to some well-known ones for images with dense visual features, in terms of estimation precision and computing time. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear equations

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