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Improved Detection of Transmission Tower Equipment Using YOLOv7 with Saliency Data Augmentation 5

Improved Detection of Transmission Tower Equipment Using YOL...

引用

5th International Conference on Clean Energy and Electric Power Engineering, ICCEPE 2024

作者： Xu, Yingying Song, Chunhe Sun, Yong Yu, Shimao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350390315

Transmission towers are critical infrastructure for power transmission, and the reliable operation of their equipment is essential to ensure electricity supply. However, the detection of transmission tower equipment faces challenges such as target overlap, large variations in size, small targets, and class imbalance due to the characteristics of the equipment. To tackle the identified issues, this study introduces a method based on YOLOv7 for the detection of transmission tower components, utilizing saliency-based data augmentation. Initially, an approach focusing on importance is employed to augment the sample size in categories with limited numbers, thereby enhancing the effectiveness of detection. Secondly, an adaptive adjustment module is introduced into the multi-head self-attention mechanism to dynamically adjust attention allocation based on the actual size of the targets and integrate contextual information, thus better handling targets with large size variations and overlaps. Finally, incorporating dynamic convolution within the backbone network significantly improves the ability to extract features from small objects. The outcomes of the experiments confirm the efficiency of the suggested approach in the smart surveillance of devices within intricate transmission settings, offering a reliable solution for maintaining power supply dependability. © 2024 IEEE.

关键词： Critical infrastructures

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Research on Multiple Automated Guided Vehicles Path Planning to Reduce Default Time 2

Research on Multiple Automated Guided Vehicles Path Planning...

引用

2nd IEEE International Conference on Data Science and Computer Application, ICDSCA 2022

作者： Liu, Yiyang Yan, Shuaihua Hou, Zheng Zhao, Yang Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Kunshan Intelligent Equipment Research Institute Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China College of Information Science and Engineering Northeastern University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

This paper studies the path planning problem of multiple automated guided vehicles (multi-AGV) in a matrix manufacturing workshop. We proposed the multi-AGV path planning problem with default time, which is a variant of the classic path planning problem. In order to ensure the timely completion of each task, the default time of the task is explicitly considered. The goal is to obtain a conflict-free path with the minimum total default time, total time, and total path length. For these three goals, this problem is NP-hard. In order to solve this problem better, we established a multi-objective optimization model of total default time, total time and total path length of multi-AGV in flexible manufacturing environment. This paper proposes a multi-AGV path planning method that integrates game theory and A∗ algorithm. This method regards each AGV as a participant in the game model, through the default time obtained from each game dynamically adjusts the probability of each AGV planning the next path, and uses game theory to help the system dynamically select the most suitable AGV to plan the next path to improve the on-time delivery rate of tasks. Finally, the simulation results show that the multi-AGV path planning method integrating game theory and A∗ algorithm further reduces the total default time, total time and total path length of all AGVs. © 2022 IEEE.

关键词： Motion planning

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A heterogeneous master-slave teleoperation method for 7-DOF manipulator

A heterogeneous master-slave teleoperation method for 7-DOF ...

引用

2021 China automation Congress, CAC 2021

作者： Su, Rong Xu, Ke Zhao, Liang Yu, Peng School of Information and Control Engineering Shenyang Jianzhu University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

Teleoperation is widely used because of its ability to extend human behavior and perception to slave robots. In this paper, we use a wearable exoskeleton as the master device for human-robot interaction, which can obtain the movement posture of the operator naturally and intuitively. The slave device uses a 7-DOF redundant manipulator similar to the human arm structure, which improves flexibility without being over redundant. In addition, an inverse kinematics method is proposed to meet the requirements of anthropomorphic remote operation of master-slave heterogeneity. Simulation experiments are carried out in combination with MATLAB and ROS RVIZ environment to prove the performance of the heterogeneous teleoperation control method. © 2021 IEEE

关键词： Inverse kinematics

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Identification of PMSM Moment of Inertia based on Model Reference Adaptive Algorithm 4

Identification of PMSM Moment of Inertia based on Model Refe...

引用

4th IEEE International Conference on Power, intelligent Computing and Systems, ICPICS 2022

作者： Ding, Fan Zhang, Tao Jia, Danping Zhang, Hualiang Pei, Quanshen Huang, Haoran School of Information Science and Engineering Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Dalian University of Technology Dalian China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

A method that can identify the rotational inertia in a timely manner is proposed because the performance of a permanent magnet synchronous motor servo system is easily affected by the rotational inertia. Firstly, principles of model-referenced adaptive recognition algorithm is introduced, and the influence of the adaptive gain on the identification results is analyzed. Then, design a suitable range of adaptive gain value, use the set error value to compare the identification error with the set value, and then select the appropriate gain for identification. Finally, through simulation verification, this method has fast identification convergence speed, high precision and strong anti-disturbance. © 2022 IEEE.

关键词： Adaptive algorithms

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Dimension Reduction for Dynamic Analysis of Gene Expression Data Using Variational Autoencoder 13

Dimension Reduction for Dynamic Analysis of Gene Expression ...

引用

13th IEEE International Conference on CYBER Technology in automation, control, and intelligent Systems, CYBER 2023

作者： Yuan, Shuai Chen, Shipeng Yang, Yongliang School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Shenyang110016 China Shenyang Institute of Automation State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110106 China

ISBN: (纸本)9798350315196

Single-cell RNA sequencing technology captures gene expression at the whole-genome scale and single-cell level in many cells. Thus, the cellular states during a physiological process would be captured in detail. These data provide new opportunities and challenges for systems biology. Previously, the models in systems biology dealt with the dynamics of specific signaling pathways. However, it is almost impossible to directly model the dynamics of the whole genome due to the ultrahigh dimensionality of the data. Here, to model its gene regulatory dynamics, the dimensionality of the whole genome was reduced significantly using the latent space of a variational autoencoder, a generative deep learning model. In the low-dimensional latent space, an ordinary differentiation equation (ODE) system has been established to analyze the dynamic characteristics of the whole genome system. To demonstrate the effectiveness of this method, we generated a low-dimensional representation of a systems biology model we previously developed. The variational autoencoder reduced the dimension from 13 to 2 in latent space. An ODE model was developed to analyze the dynamics of this two-dimensional system. The results indicated that the 2-dimensional model captured the essence of the original systems biology model. © 2023 IEEE.

关键词： Dynamics

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intelligent electricity consumption forecasting and electricity-theft analysis method based on deep learning 2

Intelligent electricity consumption forecasting and electric...

引用

2nd International Conference on Electronic Information Engineering and Computer Technology, EIECT 2022

作者： Yu, Shimao Song, Chunhe Gao, Pengpei Zheng, Haiyang Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang Institute of Automation 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)9798350399561

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. © 2022 IEEE.

关键词： Forecasting

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The Surface Ship Torpedo Defense Simulation System 3

The Surface Ship Torpedo Defense Simulation System

引用

3rd IEEE International Conference on Image, Vision and Computing, ICIVC 2018

作者： Xiaoheng, Dong Yiqi, Zhang Minghang, Li Zhengyu, Wang Shanghai Marine Electronic Equipment Research Institute Shanghai China School of Mechatronic Engineering and Automation Shanghai University Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China Shanghai Aerospace Control Technology Institute Shanghai China

ISBN: (纸本)9781538649916

Surface ship torpedo defense simulation system plays a guiding role in the development of new surface ships underwater acoustic warfare system. Through the study of the functional analysis of the simulation system, the surface ship torpedo defense simulation system is proposed and the hardware composition, topological structure, data structure, data transmission method of the surface ship torpedo defense simulation system is determined. According to the role of combat maneuvers, the torpedo defense simulation evaluation system is divided into attack, defense and public parts, and surface ship torpedo defense system simulation running instance is given, which verified the feasibility of the surface ship torpedo defense system simulation. © 2018 IEEE.

关键词： Network security

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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED 14

A High-Precision Edge Detection Algorithm Based on Improved ...

引用

14th IEEE International Conference on Cyber Technology in automation, control, and intelligent Systems, CYBER 2024

作者： Yuan, Yeborui Chen, Wenyuan Tang, Jie Yang, Jia School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics 110016 China University of Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin300384 China

ISBN: (纸本)9798331506056

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology. © 2024 IEEE.

关键词： Contrastive Learning

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Air-Ground Robots’ Cooperation-Based Mountain Glaciers Thickness Continuous Detection: Systems And Applications 16th

Air-Ground Robots’ Cooperation-Based Mountain Glaciers Thic...

引用

16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Zhong, Jikang Li, Peng Liu, Xu Zhao, Pinhui Jiang, Han Yang, Liying Li, Decai Bu, Chunguang He, Yuqing 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 Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789819964826

In this article, we present a multi-robot continuous ice thickness measurement system that can operate in plateau glacial environments with natural slopes up to 30°, large crevasses of glacier, slippery snow/ice ground surface, and inevitable terrain undulations. The ground station operator can operate the unmanned ground vehicle (UGV) with improved safety driving ability by following the optimal driving advice provided by the driving skill learning model. The air-ground robot collaboration algorithm coordinated the operation of the UGV with the unmanned aerial vehicle (UAV) to improve the mobility of the UGV in complex glacier environments. We obtain multi-scale and multi-aspect environmental information through air-ground collaboration and use a lightweight environmental modeling method to obtain a three-dimensional model of the glacier surface and subglacial terrain. The system was applied in the Korchung Gangri Glacier in the Tibet Plateau in June 2022. The experimental results show that the system can operate in the extreme environment of plateau glaciers and collect high-precision continuous ice thickness distribution data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Thickness measurement

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An Improved Deep Learning Method-based Detection of Transmission Line Insulator Defects

An Improved Deep Learning Method-based Detection of Transmis...

引用

2023 International Conference on Computer, Artificial Intelligence, and control Engineering, CAICE 2023

作者： Gao, Pengpei Song, Chunhe Yu, Shimao Wu, Tingting 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 Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510665040

The insulator is a vital component of power equipment that aids in power transmission. Aiming at transmission line faults caused by missing insulator pieces, this paper proposed to divide insulator detection and defect location into two steps. Additionally, we add Squeeze-and-Excitation (SE) module into the Faster R-CNN model at the insulator detection stage, which could enhance insulator detection accuracy under similar model complexity and also improve the detection efficiency of the YOLOv3 model on insulator defect location. © 2023 SPIE.

关键词： Object detection

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