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Mediterranean Conference on control and Automation

作者： Tomislav Haus Nikola Prkut Katarina Borovina Bruno Maric Matko Orsag Stjepan Bogdan University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems

ISBN: (纸本)9781467383479

In this work we aim to explore the concept of using the shift in the center of gravity, which in turn produces roll and pitch moments, to control the attitude of the quadrotor. We propose constructing a UAV with moving masses within each rotor arm. In this paper we show the mathematical equations describing the dynamics of the proposed system, propose a classical PID based control approach and analyze and confirm its stability. Furthermore, we analyze the effectiveness of the proposed controller in a Gazebo based simulation environment. Finally, we build a laboratory testbed that emulates the dynamics of the system and test our proposed control strategy.

关键词： Mathematical model Unmanned aerial vehicles Gravity Attitude control Robots

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Effect of additional cylinders on power-extraction performance of a Savonius vertical-axis wind turbine

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2023年第6期24卷 531-542页

作者： Yangyang YAN Jintao ZHANG Junao SHE Wenhao LIU Jianghong DENG Jianyang ZHU Institute of Robotics and Intelligent Systems Wuhan University of Science and TechnologyWuhan 430081China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of EducationWuhan University of Science and TechnologyWuhan 430081China

To improve the power-extraction performance of the Savonius vertical-axis wind turbine(S-VAWT),additional cylinders,which are used to control the fluid flow around the wind turbine blade,were introduced into the blade *** contrast to the traditional numerical method,a mathematical model in the form of a dynamical system was used in this study.A numerical calculation program that could effectively solve the equations of wind-induced rotation of S-VAWT was developed,and combined with the Taguchi experimental method to investigate the influence of additional cylinders on the power-extraction characteristics of the *** results showed that the additional cylinders have a significant impact on the power-extraction performance of the *** 4-m/s wind speed,the average power coefficient of the S-VAWT with additional cylinders is 15%higher than that of the conventional *** construction of the wind turbine prototype and power-extraction tests,the results showed that compared with a conventional S-VAWT,the output power was 29%higher for the S-VAWT with additional cylinders under the same particular conditions.

关键词： Additional cylinders Taguchi experimental method Savonius vertical-axis wind turbine(S-VAWT) Average power coefficient Wind-induced rotation

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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...

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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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Stochastic search strategies in 2D using agents with limited perception

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IFAC Proceedings Volumes 2012年第22期45卷 650-654页

作者： David Puljiz Maja Varga Stjepan Bogdan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering LARICS–Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne 1015 Lausanne Switzerland

Animals use stochastic search strategies to find a potential nest or to localize the food source. Animals do not rely much on the environmental cues or their sensory inputs. Since their cognitive capabilities are rather limited, search strategies are usually simplistic and stochastic. And yet such simple search strategies result in high efficiency in finding potential targets in limited time intervals. Similar problems with perception of the environment and limited computational power and sensory inputs are observed on the mobile robots used in search scenarios. In spite of their limitations, such robotic systems usually perform deterministic search strategies which heavily depend on environmental cues and sensory input. The research question we are addressing in this paper is which stochastic search strategy has the best performance in terms of area coverage and can stochastic search strategies outperform deterministic search strategies.

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

Intelligent electricity consumption forecasting and electric...

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

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

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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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AGV semantic attack detection based on extended stochastic Petri net

AGV semantic attack detection based on extended stochastic P...

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作者： Zhang, Sichao Liang, Wei Zhang, Yinlong Wang, Kai 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

Semantic attack, as a covert and highly destructive network attack, seriously threatens the security of automatic guided vehicle (AGV) system. To make it worse, the dynamic change of wireless channel makes semantic attack detection more non-intractable. In order to solve the issues on the current semantic attack detection in wireless network environment, this paper proposes an extended stochastic Petri net (ESPN)-based detection method. It builds a stochastic Petri net (SPN) model for the normal operation process of AGV system, and detects the semantic attack by comparing the characteristics of relevance matrix, average dwell time and token density. Additionally, we innovatively add the wireless channel evaluation parameter in the SPN model, to estimate the current wireless channel state and eliminate the impact of wireless interference on attack detection. Eventually, an AGV attack detection system experimental platform has been developed to verify the effectiveness of the proposed method. The experimental results show that the proposed algorithm can effectively eliminate the impact of wireless channel interference on semantic attack detection, and has impressive detection performance. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Stochastic systems

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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...

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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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Advances and Challenges in Large Model Compression: A Survey 24

Advances and Challenges in Large Model Compression: A Survey

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Xu, Yingying Song, Chunhe Sun, Yong Yu, Shimao 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 Shenyang China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798400717147

With the development of computer technology, large-scale models have gradually evolved from early simple algorithms to today's complex deep neural networks with huge parameter scale and high computational complexity. These large-scale models have greatly advanced the artificial intelligence technology, which has realized revolutionary progress in the fields of image, speech, text and so on. However, the computational and storage requirements of large-scale models are also growing rapidly, which seriously constrains their deployment and application in computing resource-constrained environments. In order to solve this problem, model compression technology has emerged and has been greatly developed. In this paper, we systematically review the key technologies, application scenarios, challenges and future directions of large-scale model compression from the perspective of model compression technology. Our work aims to provide practitioners and researchers with detailed references on the development of the technology, and emphasizes the important role of model compression technology in promoting the development of lightweight artificial intelligence. © 2024 Owner/Author.

关键词： Deep neural networks

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A Framework for Automatic Identification of Neural Network Structural Redundancy Based on Reinforcement Learning 5

A Framework for Automatic Identification of Neural Network S...

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5th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Wu, Tingting Song, Chunhe Zeng, Peng 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)9781510662520

The increasing structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works usually compress models by removing unimportant filters based on the importance. However, the importance-based algorithms tend to ignore the parameters that extract edge features with small criterion values. And recent studies have shown that the existing criteria rely on norm and lead to similar model compression structures. Aiming at the problems of ignoring edge features and manually specifying the pruning rate in current importance-based model pruning algorithms, this paper proposes an automatic recognition framework for neural network structure redundancy based on reinforcement learning. First, we perform cluster analysis on the filters of each layer, and map the filters into a multidimensional space to generate similar sets with different functions. We then propose a criterion for identifying redundant filters within similar sets. Finally, we use reinforcement learning to automatically optimize the cluster dimension, and then determine the optimal pruning rate for each layer to reduce the performance loss caused by pruning. Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework. © 2023 SPIE.

关键词： Reinforcement learning

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