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4th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Guo, Yalong Ling, Xiaoming Xiang, Yu Wang, Yaya Key Lab of Opto-Electronic Technology and Intelligent Control of Ministry of Education Lanzhou Jiaotong University Lanzhou730070 China National Engineering Research Center for Technology and Equipment of Environmental Deposition Lanzhou Jiaotong University Lanzhou730070 China School of logistics and transportation Central South University of Forestry and Technology Changsha410004 China

ISBN: (纸本)9781510666412

Session recommendation (RS) is a research focus and hotspot in the field of recommender systems in recent years. Previously, some traditional methods focus on sequence modeling based on user click sequences, but this approach may not be able to fully capture the complex transformation relationships between items. In recent years, graph neural session recommendation introduces session graph topology information to improve the accuracy of item and session feature representation, thus, improving the performance of session recommendation to a certain extent, in addition, this paper also uses graph attention aggregation neighbor nodes to obtain higher-order semantic relationships between items. Based on the item semantic features, the local attention mechanism is used to generate the session features, and finally, based on the item and session semantic features, this paper learns the click probability distribution of different items under a given session by cross-entropy loss. Extensive experiments are conducted on two publicly availab.e and popular datasets, Yoochoose and Diginetica, and the experimental results show that the method has a higher recommendation accuracy. © 2023 SPIE.

关键词： Graph neural networks

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Fault Analysis of Bearings in Various States Based on Continuous Wavelet and Improved Residual Network 2

Fault Analysis of Bearings in Various States Based on Contin...

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2nd International Conference on electronic Information technology and Smart Agriculture, ICEITSA 2022

作者： Wang, Qingnan Tu, Jihui School of Intelligent Manufacturing Huaihua University China Key Lab. of Intelligent Control Technology for Wuling Mountain Ecological Agriculture Hunan Province Huaihua China School of Electronic and Information Yangtze University Jingzhou China

ISBN: (纸本)9781665464017

The bearing fault signals are complex and difficult to collect, which leads to a limited number of fault samples, and it is difficult to analyze the changeable bearing faults with conventional methods. In this paper, an automatic signal feature extraction method by improved residual network and wavelet transform is presented, which is suitable for bearing fault analysis in various states. Continuous wavelet transform (CWT) can obtain enough time-frequency images by superposing the original vibration signals. Improve the residual network, increase the attention mechanism and better deal with complex features. Transfer learning is applied to various working states, and the common features of bearing faults concealment are extracted and learned, so as to improve the applicability of diagnosis. The prediction ability of the bearing is tested in various working states, which proves the applicability of our fault analysis method. The problem of low accuracy of small sample identification caused by lack of actual fault data is solved. © 2022 IEEE.

关键词： Wavelet transforms

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Evolution of Cooperation in Snowdrift Game with Multi-Memory Mechanism 42

Evolution of Cooperation in Snowdrift Game with Multi-Memory...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Yaojun Liu, Xingwen Tashi, Nyima College of Electrical Engineering Southwest Minzu University Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission Sichuan Chengdu610225 China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa850000 China

ISBN: (纸本)9789887581543

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known 'Stanford marshmallow experiment' (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Behavioral research

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Hierarchy-based extended CBBA for multi-robot task assignment

Hierarchy-based extended CBBA for multi-robot task assignmen...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Jiajun Xia, Weiguo Li, Fuqiang Wang, Peng China National Aeronautical Radio Electronic Research Institute National Key Laboratory of Science and Technology on Avionics System Integration China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering China

ISBN: (纸本)9781665465335

This paper studies the task assignment problem of a multi-robot system with complex constraints including the robot speed limit, the task execution time window constraints, the arrival time, the trip cost, and different priorities of the tasks. A new bidding function is then designed for each robot. Based on the k-means algorithm, a hierarchy-based extended consensus-based bundle algorithm (CBBA) is proposed to find the optimal solution for the task allocation problem. Finally, simulation examples are presented to show the feasibility of the proposed algorithm and comparisons with existing algorithms show that the proposed algorithm can reduce the task assignment time with guaranteed reward values. © 2022 IEEE.

关键词： Multipurpose robots

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Occupational Exposure of Therapeutic Staff in Deep Transcranial Magnetic Stimulation

Occupational Exposure of Therapeutic Staff in Deep Transcran...

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General Assembly and Scientific Symposium, URSI

作者： Mai Lu Shoogo Ueno Key Lab. of Opto-Electronic Technology and Intelligent Control of Ministry of Education Lanzhou Jiaotong University Lanzhou P.R. China Department of Biomedical Engineering Graduate School of Medicine The University of Tokyo Tokyo Japan

This study presented dosimetry analysis for therapeutic staff exposed to magnetic pulses from double cone and Halo-circular assembly (HCA) coils during deep transcranial magnetic stimulation (dTMS) applications. The magnetic field and the induced electric field in realistic human body model exposure to dTMS coils have been obtained by employing the impedance method. The exposure level by both double cone and HCA coils were assessed according to the limits defined by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines. The HCA coil generates higher exposure to therapeutic staff compared to that of double cone coil.

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SA-U-Net++: SAR marine floating raft aquaculture identification based on semantic segmentation and ISAR augmentation

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Journal of Applied Remote Sensing 2021年第1期15卷

作者： Wang, Deyi Han, Min Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Ganjingzi District Dalian China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Ganjingzi District Dalian China

Marine floating raft aquaculture (FRA) monitoring is vital for environment protection and mariculture management. Synthetic aperture radar (SAR) could provide high-quality remote sensing images under all weather conditions compared with the existing optical remote-sensing-based methods. Traditional SAR monitoring methods extract the pixel feature of marine FRA in single patches, which commonly leads to poor generalization. We propose a self-Attention semantic segmentation method based on modified U-Net++ (SA-U-Net++) for FRA segmentation, which could automatically extract semantic feature information, and provide superior performance under complicated scenes. The proposed self-Attention backbone could help to extract more precise features and enhance the overall accuracy. Furthermore, we propose a FRA-ISAR data generation method based on inverse SAR (ISAR) imaging to alleviate the sample shortage problem. We introduce the semantic segmentation method into FRA-SAR segmentation for the first time. The experiments verify the effectiveness and superiority of SAR FRA segmentation based on the proposed SA-U-Net++ model compared with the existed semantic segmentation approaches. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Deep learning

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intelligent Mobile Power Sharing and Rental System Based on BeiDou Navigation 2

Intelligent Mobile Power Sharing and Rental System Based on ...

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2nd International Conference on electronic Information technology and Smart Agriculture, ICEITSA 2022

作者： Hu, Hui Li, Xinhu Wu, Jiaxin Zeng, Qingli Zhang, Renmin College of Communication and Electronic Engineering Jishou University Hunan Jishou416000 China Hunan Provincial Key Lab. of Intelligent Control Technology for Ecological Agriculture in Wuling Mountain Area Huaihua University Hunan Huaihua418000 China

ISBN: (纸本)9781665464017

Aiming at the problems that the mobile power supply in the market cannot output AC voltage and manually adjustable DC voltage at the same time, expensive and cannot be accurately located, an intelligent mobile power sharing and rental system based on BeiDou navigation is designed. The device mainly consists of three major parts: power circuit module, controller module and mobile APP, which solves a series of problems such as location finding, rental use, visual power management and positioning return of shared power by users. The power circuit module converts the battery voltage into 220V AC output and adjustable DC precision output. The controller module realizes the functions of power supply DC output voltage collection, Battery power Detection, Beidou positioning and data uploading to the server. The mobile APP realizes the functions such as displaying the location, voltage, power and other data of multiple power supply units and sharing the rental under the shared rental mode by the APP. The system realized in this paper can greatly promote the development of China's shared energy storage industry and effectively change the current dilemma that people have difficulties in using electricity outdoors. © 2022 IEEE.

关键词： Digital storage

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A Quantized Kernel Least-mean-square Algorithm Based on Echo State Network for Online Time-series Prediction 11

A Quantized Kernel Least-mean-square Algorithm Based on Echo...

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11th International Conference on Information Science and technology, ICIST 2021

作者： Zhao, Chaochao Han, Min Wang, Jun Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China

ISBN: (纸本)9781665412667

Kernel least mean square (KLMS) algorithm is a popular method for online time-series prediction. It has the advantages of high robustness, low computational complexity, model simplicity, and online learning ability. Unfortunately, as input data grows, its dictionary size increases and the computational complexity rises significantly. In addition, how to improve the adaptability in uncertain environments with noise is also one of the main challenges. In this paper, we propose a Quantized Kernel Least-mean-square Based on Echo State Network (ES-QKLMS) algorithm in response to the above problems. In the new algorithm, the reservoirs of the echo state network (ESN) are integrated with the KLMS algorithm for online prediction. The ESN reservoir can store the associated information of historical data, which improves the algorithm's ability to track temporal characteristics. Then, the vector quantization method is adopted to suppress the size of the KLMS dictionary. Moreover, the adaptive weight adjustment is used to improve the noise immunity in an uncertain environment. At last, The Lorenz time series and the El Nino-Southern Oscillation (ENSO) time series are discussed to demonstrate the efficacy of the ES-QKLMS algorithm. © 2021 IEEE.

关键词： Time series

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An Aerial Image Stitching Algorithm Based on Long-distance Features 11

An Aerial Image Stitching Algorithm Based on Long-distance F...

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11th International Conference on Information Science and technology, ICIST 2021

作者： Liu, Qiang Han, Min Wang, Jun Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China

ISBN: (纸本)9781665412667

Aerial image stitching is very important in obtaining UAV information. The Speeded Up Robust Features Algorithm (SURF) is an image matching method with high robustness. However, the SURF algorithm subjects to the problems of inexact edge positioning and low matching accuracy. In order to obtain high-precision aerial stitching images with high efficiency, an aerial image stitching algorithm based on long-distance features is proposed in this paper. First, the Canny-SURF algorithm is used for feature detection. After that, the Long-Fast Retina Keypoint (L-FREAK) binary symbol is used to describe and match the feature points. Finally, the Random Sample Consensus Algorithm (RANSAC) is used to compute the projection transformation model, and the weighted average fusion algorithm is used to fuse the pixels. Experimental results show that the proposed algorithm is outperform the SIFT, SURF, ORB, and BRISK algorithms. The proposed algorithm has good stitching accuracy, and can stitch aerial images with wide rotation characteristics. © 2021 IEEE.

关键词： Antennas

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Reconstructing Three-Degree-of-Freedom Pose through Temporal Phase-Shifted Low-Coherence Spatial Interferograms

Reconstructing Three-Degree-of-Freedom Pose through Temporal...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Liheng Shi Jinxu Zhang Yingying GU Fangqin Gai Jing Liu Guanhao Wu Department of Precision Instrument State Key Laboratory of Precision Measurement Technology and Instruments Tsinghua University Beijing China Research Center of Opto-electronic Sensing Yangtze Delta Region Institute of Tsinghua University Zhejiang Zhejiang China Space Optoelectronic Measurement and Perception Lab. Beijing Institute of Control Engineering Beijing China National Key Laboratory of Space Intelligent Control China Academy of Space Technology Beijing China

We introduce a method for measuring three degrees of freedom using low-coherence light and temporal phase-shifting to discern position and orientation from the coherence envelope shape, enabling direct measurement wit... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Phase measurement Shape Shape measurement Measurement by laser beam Coherence Position measurement Lasers and electrooptics

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