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5th International Conference on Power and Energy Technology, ICPET 2023

作者： Chen, Kaiwen Wang, Qingyi Kang, Kai Wang, Hao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China PowerChina Hubei Electric Engineering Co. Ltd Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

ISBN: (纸本)9798350339673

Based on the background of photovoltaic development in the whole county and the demand for energy storage on the user-side, this paper establishes an economic evaluation model of user-side photovoltaic energy storage system considering shared energy storage. Firstly, three schemes of no energy storage, independent energy storage and shared energy storage are proposed, and the influence of photovoltaic energy storage system loss on annual power generation is considered. Secondly, based on the whole life cycle theory, the cost and benefit model of the user-side photovoltaic energy storage system is established, and the unit electricity cost of the user is used as the evaluation index. Finally, the relevant data of user in Hubei area are analyzed as an example to verify the effectiveness of the model and provide reference for the investment of related projects. © 2023 IEEE.

关键词： photovoltaic energy storage system shared energy storage the unit electricity cost of the user user-side

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Coordinated Motion control for a Six-wheel-drive Skid-steering Mobile Robot

Coordinated Motion Control for a Six-wheel-drive Skid-steeri...

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2021 China automation Congress, CAC 2021

作者： Xu, Yongkang Wang, Shoukun Zhao, Hongqiang Chen, Zhihua State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China State Key Laboratory of High Performance and Complex Manufacturing Central South University Changsha China

ISBN: (纸本)9781665426473

A speed consensus control strategy for a six-wheel-drive skid-steering mobile robot was proposed based on a hierarchical controller in a complex environment with unknown disturbances. The effective method of reliable speed consensus control strategy should consider some disturbances, such as the internal-robot assembly error, which including instability of motor control system and external physical interference in robot dynamic system. In this paper, a driving anti-slip control system with PID and the stability control system with sliding mode by variable structure is designed and integrated to elevate the stability of the robot. And, a G-vector control algorithm based on sliding mode control is proposed to reduce the yaw angle as much as possible during the process of differential steering of robots. The effectiveness of the proposed control strategy is verified by an example of a *** results show that the method has good control performance in control accuracy and stability. © 2021 IEEE

关键词： Motion control

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Gradient Projection Differential Neural Solution for Quadratic Optimization with Quadratic Constraints: An ACP Perspective

Gradient Projection Differential Neural Solution for Quadrat...

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IEEE International Conference on systems, Man and Cybernetics

作者： Ying Liufu Mei Liu Long Jin Feiyue Wang School of Information Science and Engineering Lanzhou University Lanzhou China College of Computer Qinghai Normal University Xining China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In recent years, quadratic optimizations have become increasingly popular in engineering. However, conventional methods that investigate this problem from the perspective of a canonical form with linear constraints are not effective in dealing with the significant challenges posed by quadratic constraints in practice. This paper proposes a solution framework for the quadratic optimization with quadratic constraints (QOQC) based on innovative artificial societies, computational experiments, and parallel execution (ACP) framework. Then, a gradient projection differential neural solution (GPDNS) is proposed to address this. To illustrate the effectiveness of the GPDNS model in solving the QOQC system, numerical simulations are provided. Overall, this paper presents the potential of innovative approaches like the ACP framework to enhance our capabilities in addressing challenging optimization systems.

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PVTimesNet: A Hybrid Deep Learning Method for Enhancing Ultra-Short-Term PV Power Forecasting

PVTimesNet: A Hybrid Deep Learning Method for Enhancing Ultr...

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Chinese automation Congress (CAC)

作者： Ji Wu Kai Wang Wenwen Ma Jingxin Zhang Shibo Wang China Electric Power Research Institute Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate ultra-short-term photovoltaic (PV) power forecasting is crucial for the real-time scheduling of grid systems. However, the inherent variability of solar energy makes this task extremely challenging. To enhance PV power forecasting, this paper introduces a novel hybrid model named PVTimesNet, designed to harness the strengths of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for effective feature extraction. PVTimesNet consists of two parallel branches: the CNN1d branch, which captures correlations between multiple variables and adjacent time steps, and the LSTM branch, which learns the temporal dependencies within the PV power sequences. The features extracted by both branches are then concatenated and passed through a fully connected layer to generate multi-step PV power forecasts. Experimental results demonstrate that for forecast horizons of 1 to 4 hours, the proposed model significantly outperforms individual models and other CNN-LSTM hybrid structures. Additionally, it exhibits superior performance compared to related methods for longer forecast horizons.

关键词： Photovoltaic systems Deep learning Wind speed Predictive models Feature extraction Hybrid power systems Convolutional neural networks Forecasting Wind forecasting Long short term memory

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Parallel control for Continuous-Time Linear systems:A Case Study

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 919-928页

作者： Qinglai Wei Hongyang Li Fei-Yue Wang the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 Qingdao Academy of Intelligent Industries Qingdao 266109China the Institute of Systems Engineering Macao University of Science and Technology

In this paper,a new parallel controller is developed for continuous-time linear *** main contribution of the method is to establish a new parallel control law,where both state and control are considered as the *** structure of the parallel control is provided,and the relationship between the parallel control and traditional feedback controls is *** the situations that the systems are controllable and incompletely controllable,the properties of the parallel control law are *** parallel controller design algorithms are given under the conditions that the systems are controllable and incompletely ***,numerical simulations are carried out to demonstrate the effectiveness and applicability of the present *** Terms-Continuous-time linear systems,digital twin,parallel controller,parallel intelligence,parallel systems.

关键词： Index Terms—Continuous-time linear systems digital twin parallel controller parallel intelligence parallel systems

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Visual fatigue assessment based on multi-task learning 31

Visual fatigue assessment based on multi-task learning

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31st Stereoscopic Displays and Applications Conference, SD and A 2020

作者： Wang, Danli Wang, Xueyu Song, Yaguang Xing, Qian Zheng, Nan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In recent years, with the rapid development of stereoscopic display technology, its applications have become increasingly popular in many fields, and, meanwhile, the number of audiences is also growing. The problem of visual fatigue is becoming more and more prominent. Visual fatigue is mainly caused by vergence-accommodation conflicts. An evaluation experiment was conducted, and the electroencephalogram (EEG) data of the subjects were collected when they were watching stereoscopic content, and then the stereoscopic fatigue state of the subjects during the viewing process was analyzed. As deep learning is proved to be an effective end-to-end learning method and multi-task learning can alleviate the problem of lacking annotated data, the authors establish a user visual fatigue assessment model based on EEG by using multi-task learning, which can effectively obtain the user's visual fatigue status, so as to make the comfort designs to avoid the harm caused by user's visual fatigue. © Society for Imaging Science and Technology 2019

关键词： Electroencephalography

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Data-based Stabilization of Unknown Discrete-time 2-D Roesser systems 23

Data-based Stabilization of Unknown Discrete-time 2-D Roesse...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Yang, Runmin Yang, Rongni Ma, Renjie Zhu, Yanzheng Shi, Peng School of Control Science and Engineering Shandong University Jinan250061 China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150001 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China School of Electrical and Mechanical Engineering University of Adelaide AdelaideSA5005 Australia

ISBN: (纸本)9798331528041

This paper studies the stabilization problem of discrete-time two-dimensional (2-D) systems represented by Roesser based on available data. First of all, based on the pre-collected input-state data, the original system is transformed into a data-based form. Then a set is established to represent all systems capable of generating the given data, thereby indirectly stabilizing the original system by stabilizing all systems within this set. Next, by utilizing the S-procedure, sufficient conditions are established for the resultant closed-loop systems that incorporate a state feedback controller based on collected data. Finally, an example is given to demonstrate the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Discrete time control systems

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Study on Landslide Susceptibility Evaluation Based on Improved Multivariable Weighted Model: A Case Study of Lushan County, China

Study on Landslide Susceptibility Evaluation Based on Improv...

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Software Engineering and Artificial Intelligence (SEAI), IEEE International Conference on

作者： Li Zhao Gonghao Duan School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

The evaluation of regional geological hazard susceptibility is of great significance to the prevention and control of geological hazard. In this paper, the "4-20" Lushan earthquake disaster area as the research area, combined with GIS and characteristics of the research area, through correlation analysis, selected 9 influencing factors as the evaluation factors. The study was conducted using a weighted information volume-logistic regression model (WI-LR). The results were classified into five sensitivity levels: very low, low, medium, high and very high. The results show that the landslide prone areas in Lushan County are concentrated in the area below the middle of the county seat, mostly near rivers, faults and areas with peak acceleration greater than 0.4. Among them, WI-LR model (AUC=0.918) > deterministic factor model (CF =0.908) > weighted information quantity model (WI) (AUC=0.896) > information quantity model I (AUC=0.853) > information quantity logistic regression model I-LR (AUC=0.799). It indicates that WI-LR model has high evaluation accuracy.

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Sector Patch Embedding: An Embedding Module Conforming to The Distortion Pattern of Fisheye Image

arXiv

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arXiv 2023年

作者： Yang, Dianyi Tang, Jiadong Gao, Yu Yang, Yi Fu, Mengyin School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China

Fisheye cameras suffer from image distortion while having a large field of view(LFOV). And this fact leads to poor performance on some fisheye vision tasks. One of the solutions is to optimize the current vision algorithm for fisheye images. However, most of the CNN-based methods and the Transformer-based methods lack the capability of leveraging distortion information efficiently. In this work, we propose a novel patch embedding method called Sector Patch Embedding(SPE), conforming to the distortion pattern of the fisheye image. Furthermore, we put forward a synthetic fisheye dataset based on the ImageNet-1K and explore the performance of several Transformer models on the dataset. The classification top-1 accuracy of ViT and PVT is improved by 0.75% and 2.8% with SPE respectively. The experiments show that the proposed sector patch embedding method can better perceive distortion and extract features on the fisheye images. Our method can be easily adopted to other Transformer-based models. Source code is at https://***/IN2-ViAUn/Sector-Patch-Embedding. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and Checking

arXiv

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arXiv 2023年

作者： Gao, Yu Xu, Xi Jiang, Tianji Chen, Siyuan Yang, Yi Yue, Yufeng Fu, Mengyin School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China

Object detection and pose estimation are difficult tasks in robotics and autonomous driving. Existing object detection and pose estimation methods mostly adopt the same-dimensional data for training. For example, 2D object detection usually requires a large amount of 2D annotation data with high cost. Using high-dimensional information to supervise lower-dimensional tasks is a feasible way to reduce datasets size. In this work, the DR-WLC, a dimensionality reduction cognitive model, which can perform both object detection and pose estimation tasks at the same time is proposed. The model only requires 3D model of objects and unlabeled environment images (with or without objects) to finish the training. In addition, a bounding boxes generation strategy is also proposed to build the relationship between 3D model and 2D object detection task. Experiments show that our method can qualify the work without any manual annotations and it is easy to deploy for practical applications. Source code is at https: //***/IN2-ViAUn/DR-WLC. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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