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Australian and New Zealand control Conference (ANZCC)

作者： Tang, Yiqing Dai, Xingyuan Zhao, Chen Cheng, Qi Lv, Yisheng Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China China Univ Min & Technol Sch Mech & Elect Engn Beijing Peoples R China Univ Min & Technol Beijing Inner Mongolia Res Inst Ordos 017000 Peoples R China

ISBN: (纸本)9798350314977

In recent years, large language models (LLM) have received a lot of attention for their ability to understand, generate and process natural language. By fine-tuning the models on specific domains or using prompts, LLMs can generate summaries tailored to different contexts and requirements. We hope to utilize these advantages of LLM in urban traffic signal control and provide a new paradigm for traffic control. In this paper, we propose a large language model-driven urban traffic control method, which is designed based on artificial systems, computational experiments, parallel execution (ACP) method. There are three traffic control modes, autonomous mode, feedback mode, and human takeover mode. In autonomous mode, we adopt recommendations based on inventory strategy, which can to some extent reduce the acquisition time of traffic control strategies. With the continuous construction and improvement of the strategic library, the efficiency of the system will be further improved. The feedback mode is mainly aimed at traffic scenarios that are not in the library, generating new control strategies and introducing human feedback. The human takeover model ensures that human engineers have direct control over the actual transportation system. LLM drives the interaction between human engineers and computers in the process of traffic control. Assist human engineers in achieving traffic control and improving work efficiency.

关键词： Feedback

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Deep reinforcement learning-based optimal bidding strategy for real-time multi-participant electricity market with short-term load

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ELECTRIC POWER systems RESEARCH 2024年 233卷

作者： Liu, Chuwei Rao, Xuan Zhao, Bo Liu, Derong Wei, Qinglai Wang, Yonghua Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

This paper aims to address the bidding strategy optimization in the real-time multi-participant electricity market with short-term load dynamics. In order to avoid the sub-optimal solution and the dependence on the complete information in traditional mathematical programming methods, an electricity market bidding strategy optimization algorithm based on deep reinforcement learning (DRL) is developed. While conventional reinforcement learning algorithms (e.g., Q-learning and deep Q-learning) are only capable of handling simple problems in discrete state spaces, the proximal policy optimization (PPO) algorithm is implemented in the bidding strategy optimization since it can optimize the bidding strategy in the continuous action and state spaces. In order to substantiate the aforementioned perspective, this paper conducts a two-part experimental study. First, experiments which consider the fixed demand load of market participants show that the developed method can reach the Nash equilibrium just like the bi-level optimization, and higher profits can be achieved by adjusting hyperparameters. Then, complex experiments which consider the time-varying demand load verify the DRL-based electricity market bidding strategy performs better than bi-level optimization-based methods and increases the profits of generators.

关键词： Deep reinforcement learning Proximal policy optimization algorithm Bi-level optimization Electricity market bidding strategy Nash equilibrium

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TADNet: Temporal Attention Decomposition Networks for Probabilistic Energy Forecasting

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IEEE TRANSACTIONS ON POWER systems 2024年第6期39卷 7190-7202页

作者： Ye, Jiarui Zhao, Bo Liu, Derong Wei, Qinglai Wang, Yonghua Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Many decision-making processes in the energy industry depend on probabilistic forecasting to quantify the future uncertainties. However, existing methods are difficult to provide reliable probabilistic forecasting for the energy industry. Hence, we focus on solving the multi-horizon probabilistic forecasting problem of energy time series. In this paper, the time series is considered as the superposition of a regular changing component and a residual component. On this basis, we develop a novel deep learning-based temporal attention decomposition network (TADNet). TADNet predicts the regular change of the time series, and then quantifies the fluctuation intervals of the residual component, which enables TADNet to achieve reliable probabilistic forecasting. To take both short- and long-term patterns into account, TADNet combines convolution with attention mechanism to extract the temporal feature from the time series. To enable efficient information extraction, the gating components are utilized to suppress unnecessary features. Through extensive empirical evaluation of several real-world energy datasets, TADNet achieves better prediction accuracy than the compared models.

关键词： Forecasting Time series analysis Probabilistic logic Predictive models Reliability Long short term memory Load modeling Deep learning neural networks attention mechanism time series forecasting probabilistic energy forecasting

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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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UAV Path Planning With Terrain Constraints for Aerial Scanning

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 1189-1203页

作者： Yuan, Jinbiao Liu, Zhenbao Xiong, Xiaoyu Ai, Yunfeng Chen, Long Tian, Bin Polytech Univ Sch Northwestern Xian 710072 Peoples R China Northwestern Polytech Univ Res & Dev Inst Shenzhen Sch Civil Aviat Xian 710072 Peoples R China Univ Chinese Acad Sci Dept Artificial Intelligence Beijing 100049 Peoples R China Waytous Inc Qingdao 266109 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

A global path planning method constrained by dynamics, kinematics, and terrain is proposed for Vertical Takeoff and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). Firstly, new constant-altitude waypoints are interpolated above the terrain based on altitude information and terrain resolution. Account for the coverage of airborne sensors over the surface area, a horizontal waypoint interpolation optimization (HOPT) is performed at positions with excessive bending in the horizontal direction. Secondly, for vertical plane optimization (VOPT), i.e., height optimization, a Soft Actor-Critic-based Particle Swarm Optimization (SAC-PSO) is employed to optimize the convergence speed of the method and achieve terrain following (TF). Thirdly, to evaluate the generated paths, a deep residual network (DRSN) is designed to mitigate the impact of optimization failures during the iteration process and improve the stability of the algorithm. Simulation experiments demonstrate the efficiency and path quality of the proposed method, while real-world tasks validate its practicality.

关键词： Autonomous aerial vehicles Optimization Task analysis Path planning Turning Kinematics Trajectory UAV DRSN SAC-PSO path planning terrain following

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Pedestrian Detection for Autonomous Vehicles Using Virtual-to-Real Augmentation

Pedestrian Detection for Autonomous Vehicles Using Virtual-t...

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2022 Chinese Automation Congress, CAC 2022

作者： Lu, Bo Huang, Min Li, Xi Nie, Yunhao Miao, Qinghai Lv, Yisheng School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665465335

Annotated data are essential to the success of training deep neural models for autonomous driving. Practically, it is both expensive and time consuming to collect and annotate plenty of data driving cross the city. It is even harder to cover the corner cases involving rare pedestrian poses with occlusions. To tackle this problem, we propose a data augmentation method for pedestrian detection. First, we take a SMPL model to generate pedestrians with a variety of poses and textures. Second, we insert the rendered pedestrians into images with real traffic background according to vanishing point. Occlusions can also be set easily by taking use of segmentation annotations of original real dataset. We evaluated the effectiveness of the generated synthetic dataset using multiple detectors. Results of three experiments shown that detectors trained with mixture of synthetic and real data outperform those trained on only real datasets. © 2022 IEEE.

关键词： Computer vision

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Stress Detection Using Wearable Devices based on Transfer Learning

Stress Detection Using Wearable Devices based on Transfer Le...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wu, Jinting Zhang, Yujia Zhao, Xiaoguang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665401265

Excessive stress will have a negative impact on people's physical and mental health, especially for some special occupations. Because stressful stimuli can trigger a variety of physiological responses, analyzing physiological signals collected by wearable devices has become an important way to evaluate the stress state in recent years. However, the number of available subjects of a target group may be small, and collecting a large amount of data when the target group changes is costly and time-consuming. To solve this problem, we propose a stress detection framework for a small target group which uses adversarial transfer learning method to learn shared knowledge about stress between different groups. In order to verify the performance of the framework, we establish a dataset consisting of 264 ordinary college students and 32 police school students, aiming to evaluate the acute stress state of police school students under video stimuli for psychological training in the future. Comprehensive experiments show that our algorithm has achieved a significant improvement in the target group compared with the baseline methods. © 2021 IEEE.

关键词： Physiology

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Cross stage partial connections based weighted Bi-directional feature pyramid and enhanced spatial transformation network for robust object detection

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NEUROCOMPUTING 2022年 513卷 70-82页

作者： Lu, Yan-Feng Yu, Qian Gao, Jing-Wen Li, Yi Zou, Jun-Cheng Qiao, Hong Chinese Acad Sci Inst Automation State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Huizhou Univ Sch Elect Informat & Elect Engn Huizhou 516007 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Structural information is an essential component for efficient object detection. In many visual detection tasks, the objects with large structural deformation usually make up a large proportion. The shape, con-tour, and internal structure of the objects tend toward dramatic change, which easily causes troubles for efficient object detection. Therefore, how to detect these objects robustly and accurately is one of the sig-nificant challenges. To address this issue, we introduce a Cross Stage Partial connections-based weighted Bi-directional Feature Pyramid Network (CSP-BiFPN), which allows easy and efficient multi-scale feature fusion by cross-stage partial connections. Second, to enhance the model's spatial transformation capacity, the multi-scale feature maps extracted from the YOLO backbone network are processed by an enhanced spatial transformation network (ESTN) for spatial deformations. Based on these architectural modifica-tions and optimizations, we further develop a novel real-time robust object detection model called Bi-STN-YOLO. We evaluate the performance of the proposed method on four image datasets. The experi-mental results demonstrate that the proposed approach achieves significant improvements compared with the typical YOLO families and competitive performance compared to the state-of-the-arts in detec-tion tasks. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Robust object detection Structural deformation Image detection Spatial transformation

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First-in-human orbital tumor surgery guided by near-infrared II window fluorescence imaging: A feasibility study

INTERDISCIPLINARY MEDICINE

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INTERDISCIPLINARY MEDICINE 2025年第1期3卷

作者： Zhang, Zeyu Guo, Lishuang Yao, Lan Li, Yueyue Hei, Yan Wang, Qi Wang, Xiaoyi Ma, Rui Yang, Xinji Hu, Zhenhua Wu, Wei Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Engn Med Beijing Peoples R China Beihang Univ Key Lab Big Data Based Precis Med Minist Ind & Informat Technol Beijing Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Ctr 3 Sr Dept Ophthalmol Beijing 100143 Peoples R China Chinese Acad Sci CAS Key Lab Mol Imaging Inst Automat Beijing Key Lab Mol Imaging Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

Precise resection of orbital tumors is a critically important but elusive issue. Fluorescence imaging in the near-infrared II window (NIR-II) holds the potential to provide the surgeons with real-time identification for orbital tumors. Here, for the first time, we evaluated the feasibility and clinical value of NIR-II fluorescence imaging in orbital tumor surgery. To establish the method of NIR-II fluorescence imaging for orbital tumors, we developed a NIR-II fluorescence imaging system and indocyanine green (ICG) served as the fluorescent contrast agent. Twenty-two patients diagnosed with orbital tumors and scheduled for standard-of-care surgery were enrolled in this study. Time-course NIR-II fluorescence imaging of two patients with superficial orbital tumors showed the optimum imaging time was 2 h post injection of ICG. Fifteen patients were allocated for diagnostic test, which showed that both the in situ and ex vivo NIR-II fluorescence imaging showed better sensitivity and specificity than the surgeon judgment. In the feasibility trial of the remaining five patients, the surgeon encountered 34 suspicious regions and surgical decisions were changed nine times due to NIR-II fluorescence imaging. The resultant seven additional resections were justified by histopathology and the two conservative treatments did not result in recurrence. Based on these findings, we suggested that ICG-based NIR-II fluorescence imaging was feasible to guide precise resection of orbital tumors. A future randomized controlled trial with a larger cohort is encouraged to further verify the clinical value.

关键词： fluorescence imaging ICG NIR-II imaging orbital tumor

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Omnidirectional Drift control of an Underwater Biomimetic Vehicle-Manipulator System via Reinforcement Learning 10

Omnidirectional Drift Control of an Underwater Biomimetic Ve...

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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Ma, Ruichen Wang, Yu Wang, Rui Wang, Shuo University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665424233

This paper proposes an omnidirectional drift control on an underwater biomimetic vehicle-manipulator system (UB-VMS). The UBVMS has two biomimetic propellers, they obtain propulsive force by actuating their undulating fins. A configuration of the system, the dynamics of the UBVMS and the kinematics of the fins are given respectively. Then the control problem is designed as a Markov decision process (MDP). A reinforcement learning method based on the twin delayed deep deterministic policy gradient (TD3) is proposed for this MDP. A control policy is well trained by the reinforcement learning method and tested in eight different simulations. An analysis of the simulation results is also given. © 2021 IEEE.

关键词： Reinforcement learning

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