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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Li, Yuke Zhou, Yongxing Li, Xueru Wang, Junbo Wang, Yutong Fan, Lili Wang, Fei-Yue Waytous Co. Ltd. Beijing China Consumer BG Huawei Technologies Co. Ltd Beijing China School of Software Northwestern Polytechnical University Xi'an China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China

It is difficult to design unimodular sequences with both good auto-ambiguity functions (AF) and cross-ambiguity functions (CAF), due to its nonconvex and higher cardinality property. To address this problem, we propose a Pareto optimization framework to reach an optimal tradeoff between AF and CAF. Besides, we also propose a novel convolution-based multi-objective optimization algorithm to optimize the conventional metrics including peak sidelobe level (PSL) and integrated sidelobe level (ISL). In this way, the generated sequences are Doppler resilient with the desired shape of aperiodic or periodic AFs. Compared with the state-of-the-art methods, simulation experimental results indicate the PSL metric of AF and CAF can be both suppressed to -72dB under the same system parameters, obtaining a minimum of 7 dB gain. IEEE

关键词： Multiobjective optimization

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System Dynamics-Based Analysis on Factors Influencing Comprehensive Competence of Primary and Secondary School Students

System Dynamics-Based Analysis on Factors Influencing Compre...

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Chinese automation Congress (CAC)

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

With rapid progress of artificial intelligence (AI) , it is urgent to promote educational reform to improve comprehensive competence level of primary and secondary school students (CCLPSSS). However, educational reform is a system engineering involving multiple and intertwined factors. In this research, a system dynamics-based model of education reform is developed. Then, based on this model, a series of simulation experiments are carried out to find main factors affecting improvement of CCLPSSS. Results show that: 1) this model could simulate trends of CCLPSSS under different educational reform measures; 2) Measures including teaching mode reform, development and application of AI-based new teaching method, implementation of Double Reduction in compulsory education, implementation of deepening education evaluation reform policy, and development and application of AI-based education evaluation technology could play significant roles in promoting CCLPSSS and further strengthening cultivation of AI compound talents.

关键词： Analytical models Text analysis System dynamics Education Decision making Predictive models Market research

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An Immersive Learning System with Multimodal Cognitive Processes Inference

An Immersive Learning System with Multimodal Cognitive Proce...

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Chinese automation Congress (CAC)

作者： Siyu Bian Xiwei Liu Hongxia Zhao Xiaoyan Gong Sifeng Jing The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao China

Using multimodal technology to infer learners’ cognitive processes is an important means to support personalized learning. Previous cognitive process analysis is more about subjective reasoning, and less about combining physiological signals. With the rapid development of virtual reality technology, new requirements and more immersive schemes for learner’s cognitive process inferring have been proposed. Based on this, this paper proposes an immersive learning system with multimodal cognitive processes inference, which is composed of macro, mesoscopic and micro environments. The system analyzes learner’s actions, emotions and attention to infer learner’s cognitive process through more comprehensive and objective multimodal data. The system also combines eye movement, brainwaves and other data, and proposes a multimodal data acquisition and analysis scheme using HMD-based VR. This paper also provides an immersive learning cognitive processes inference scheme for individual online learning, offline classroom learning scenarios.

关键词： Learning systems automation Cognitive processes Data acquisition Virtual reality Physiology

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Research on intelligent methods in virtual reality games

Research on intelligent methods in virtual reality games

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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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Testing Intelligence: Accelerating the Verification and Validation of Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-14页

作者： Wang, Fei-Yue Hu, Jia Lai, Jintao State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China ZhongTe Distinguished Chair in Cooperative Automation Professor Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China

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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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Event-Triggered Energy Optimization of Wireless Sensor Networks

Event-Triggered Energy Optimization of Wireless Sensor Netwo...

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IEEE International Conference on Networking, Sensing and control

作者： Lu Liu Ruizhuo Song Qinglai Wei School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665472449

Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste.

关键词： Wireless sensor networks Energy consumption Adaptive systems System performance Optimal control Optimal scheduling Switches

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