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Chinese Automation Congress (CAC)

作者： JiaWei Wang Xiwei Liu Xiaoyan Gong The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China

Introduction of virtual reality technology enriches the expression of traditional games and greatly improves the sense of substitution and interactive experience of users. Realization of virtual reality is inseparable from artificial intelligence technology. In order to improve immersion and interactivity of virtual reality games, it is necessary to propose more intelligent solutions to existing technologies. This paper aims to discuss some intelligent methods of virtual reality games. Firstly, it discusses artificial intelligence means to enhance the virtual reality experience, which can improve the tracking and force / tactile interaction system of existing virtual reality games from software and hardware aspects; Then it proposes the concept of human-machine hybrid enhanced intelligence: by introducing human cognitive model to improve perception and decision-making ability of virtual reality games, so as to make interaction of virtual reality games more friendly and more real.

关键词： Solid modeling Human intelligence Force Virtual reality Games Reinforcement learning Software

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Reinforcement Learning-Enhanced Dynamic Multi-Objective Evolutionary Algorithm for Cooperative Shovel Scheduling in Open-Pit Mining

Reinforcement Learning-Enhanced Dynamic Multi-Objective Evol...

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Chinese Automation Congress (CAC)

作者： Zihao Zhang Changhe Li Shengxiang Yang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation China University of Geosciences Ministry of Education Wuhan China School of Artificial Intelligence Anhui University of Science & Technology Hefei China School of Computer Science and Informatics De Montfort University Leicester UK

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm.

关键词： Q-learning Ores Heuristic algorithms Process control Evolutionary computation Production Dynamic scheduling Real-time systems Windows Optimization

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Fixed-Time Sliding Mode Tracking Control for Two-Dimensional Directional Drilling System

Fixed-Time Sliding Mode Tracking Control for Two-Dimensional...

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Asian Control Conference

作者： Jinyu Li Hongchao Wei Chengda Lu Wangnian Li Quanxin Li Min Wu 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 Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, the effect of the initial state of the drilling system on trajectory tracking is taken into account in the directional drilling trajectory tracking control. Firstly, the trajectory model describing the two-dimensional drilling trajectory of the rotary steering system (RSS) is introduced. To improve the accuracy of trajectory tracking control and reduce the trajectory deviation, a fixed-time sliding mode control (FTSMC) method is introduced. Then, the stability of the closed-loop system is analyzed, and the range of controller parameters satisfying the stability condition is obtained. Finally, the effectiveness of the designed control strategy is verified by simulation experiments.

关键词： Drilling Upper bound Trajectory tracking Steering systems Stability analysis Trajectory Closed loop systems

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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Photovoltaic Data Cleaning Method Based on DBSCAN Clustering, Quartile Algorithm and Pearson Correlation Coefficient Interpolation Method

Photovoltaic Data Cleaning Method Based on DBSCAN Clustering...

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Energy, Electrical and Power Engineering (CEEPE), International Conference on

作者： Biyuan Wang Xingye Deng Taowei Chen Yidong Li Huizhou Power Supply Bureau Guangdong Power Grid Co Guangdong China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China

The proportion of photovoltaic power generation in the global energy structure is increasing year by year. Because of its volatility and periodicity, grid-connected photovoltaic challenges the safe and stable operation of power grid. One of the ways to solve this problem is data mining. Data mining needs high-quality data support, and data cleaning is one of the important means to improve data quality. Aiming at the problem of poor quality of original PV data, this paper analyzes two typical outliers of PV data and proposes a PV output data cleaning method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, quartile algorithm and Pearson correlation coefficient interpolation method. Comparison of DBSCAN clustering algorithm combined with the quartile method of outlier identification with the quartile method alone outlier identification, the effectiveness of the method proposed in this paper is verified. Then using the method proposed in this paper for outlier identification based on Pearson correlation coefficient interpolation and cubic spline interpolation to verify the data filling effect, the results show that Pearson correlation coefficient interpolation is superior to cubic spline interpolation. Finally, two sets of data with different data cleaning methods are substituted into the Long short-term memory (LSTM) Model to verify accuracy of the cleaning method proposed in this paper.

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A Trajectory Tracking Method using Dynamic Sliding Mode Control with Parameter Optimization for Autonomous Underwater Vehicles

A Trajectory Tracking Method using Dynamic Sliding Mode Cont...

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Industrial Cyber-Physical systems (ICPS)

作者： Weiliang Li Xuzhi Lai Sheng Du Chengda Lu Yawu Wang Zonghuan Chen Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Guangzhou Marine Geological Survey Guangzhou China

Autonomous underwater vehicles (AUVs) must be able to track a particular trajectory when performing various tasks. Sometimes, the widely used control method in actual marine engineering, the proportional integral derivative (PID) control method cannot meet the accuracy requirements of AUVs' trajectory tracking. In this paper, a dynamic surface sliding mode controller for trajectory tracking is designed. A nonlinear disturbance observer is used to compensate for environmental interference, and an improved particle swarm optimization with dynamic inertia weight is applied to optimize the control parameter. Simulation experiments are based on the mathematical model of an underwater robot BLUEROV2, and the control and optimization algorithms are designed.

关键词： Autonomous underwater vehicles Trajectory tracking Heuristic algorithms Mathematical models Trajectory Vehicle dynamics Particle swarm optimization

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Distance- And velocity-based collision avoidance for time-varying formation control of second-order multi-agent systems

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IEEE Transactions on Circuits and systems II: Express Briefs 2021年第4期68卷 1253-1257页

作者： Pang, Zhong-Hua Zheng, Chang-Bing Sun, Jian Han, Qing-Long Liu, Guo-Ping Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology Beijing100144 China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia Department of Artificial Intelligence and Automation Wuhan University Wuhan430072 China

This brief addresses the time-varying formation control problem with collision avoidance for second-order multi-agent systems. By taking both distances and velocities between agents into account, a novel collision avoidance scheme is proposed on the basis of conventional distance-based artificial potential field methods. Compared with the latter, the proposed scheme can more accurately determine possible collision situations and thus significantly reduce possible collision areas, which consequently not only can reduce energy consumption, but also has as few adverse effects on formation maintenance as possible. Then a time-varying formation control protocol is designed so as to achieve zero steady-state position and velocity formation tracking errors without collision. Finally, theoretical analysis, numerical demonstration, as well as comparative simulation are provided to verify the effectiveness of the proposed method. © 2004-2012 IEEE.

关键词： Multi agent systems

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Symbolic Execution of MPI Programs with One-Sided Communications

Symbolic Execution of MPI Programs with One-Sided Communicat...

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Asia-Pacific Conference on Software Engineering

作者： Nenghui Hu Zheng Bian Ziqi Shuai Zhenbang Chen Yufeng Zhang College of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China State Key Laboratory of High Performance Computing National University of Defense Technology China College of Computer Science and Electronic Engineering Hunan University Changsha China

Message-passing interface (MPI) programs are non-deterministic and challenging to ensure correctness. The introduction of one-sided communications makes the problem of non-determinism more severe for MPI programs. This paper reports our in-progress work of symbolic execution for the MPI programs with one-sided communications. Our approach can cover the non-determinism caused by the inputs, one-sided communication, and message- passing operations of MPI programs. The preliminary evaluation's results indicate the promising of our approach.

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The Separate Meter in Separate Meter Out Control System Using Dual Servo Valves Based on Indirect Adaptive Robust Dynamic Surface Control

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Journal of systems Science & complexity 2019年第2期32卷 557-576页

作者： CHEN Guangrong WANG Junzheng WANG Shoukun ZHAO Jiangbo SHEN Wei The Key Laboratory of Drive and Control of Servo Motion Systems Beijing Institute of Technology The Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Due to the demand for energy efficiency in electro-hydraulic systems, the separate meter in and separate meter out(SMISMO) control system attracts vast attention. In this paper, the SMISMO control system was configured with two servo valves to control the meter in and meter out *** designing two of the proposed indirect adaptive robust dynamic surface controllers(IARDSC)for the working-side and off-side system and setting the coupled items as estimated parameters, the SMISMO control system was decoupled into two subsystems completely. Here, indirect adaptive robust control(IARC) was employed to address the internal parameter uncertainties and external *** surface control(DSC) was utilized in the backstepping design procedure of IARC to deal with the inherent ‘explosion of terms' problem. As thus, the proposed IARDSC could simplify the design procedure, decrease the computational cost, and achieve an improved control performance in practical use. Finally, experimental results validated the effectiveness of proposed IARDSC and showed that the proposed SMISMO control system could provide a possibility to save more energy.

关键词： Dynamic surface control indirect adaptive robust control parameter estimation SMISMO stability analysis

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Large-Scale Hyperspectral Image Clustering Using Contrastive Learning

Large-Scale Hyperspectral Image Clustering Using Contrastive...

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2021 International Conference on Information and Knowledge Management Workshops, CIKMW 2021

作者： Cai, Yaoming Liu, Yan Zhang, Zijia Cai, Zhihua Liu, Xiaobo School of Computer Science China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China

Unsupervised hyperspectral image (HSI) classification is an important but challenging task in the hyperspectral processing community. Despite great success, previous HSI clustering approaches belong to offline clustering which is often performed in a transductive scheme, thus failing to generalize to large-scale and unseen scenes. In this paper, we propose an online and deep clustering model for large-scale HSI clustering, termed spectral-spatial contrastive clustering (SSCC). Specifically, SSCC performs contrastive learning based on a series of semantic-preserving spectral-spatial augmentation to simultaneously maximize the intra-class agreement and inter-class variation, which are implemented by an instance-level contrastive loss and a cluster-level contrastive loss, respectively. The SSCC model is trained in an end-to-end fashion with minibatch, allowing it to efficiently handle large-scale HSIs. We assess the performance of SSCC on real HSI and show that SSCC significantly advances the state-of-the-art results with 8.41% improvement on accuracy. © 2021 CEUR-WS. All rights reserved.

关键词： Spectroscopy

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