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Quantification of Valve Stiction in Control Loops Using Adaptive Differential Evolution Algorithm

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Quantification of Valve Stiction in Control Loops Using Adap...

Chinese automation Congress (CAC)

作者： Dong Yang Xun Lang Jiande Wu Wanke Yu Songhua Liu Lei Xie Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Automation China University of Geosciences Wuhan China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurately and promptly quantifying stiction is crucial for valve maintenance in industrial settings and ensuring the stability of control loop performance. However, current Hammerstein-based stiction quantification methods overlook valve dynamics, potentially leading to inaccurate quantification. Therefore, this paper proposes a novel stiction quantification method based on an adaptive differential evolution algorithm. In addition to considering the impact of valve dynamics on stiction, the proposed method demonstrates a certain level of robustness against the interference of unknown noise. Finally, the effectiveness of the proposed method is demonstrated through a series of simulations and industrial case studies.

关键词： Adaptation models Heuristic algorithms Noise Interference Valves Stability analysis Robustness Data models Maintenance Classification algorithms

An Energy Sharing Mechanism Considering Network Constraints and Market Power Limitation

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arXiv 2022年

作者： Chen, Yue Zhao, Changhong Low, Steven H. Wierman, Adam The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong The Department of Information Engineering The Chinese University of Hong Kong Hong Kong The Computing + Mathematical Sciences Department California Institute of Technology PasadenaCA91125 United States

As the number of prosumers with distributed energy resources (DERs) grows, the conventional centralized operation scheme may suffer from conflicting interests, privacy concerns, and incentive inadequacy. In this paper, we propose an energy sharing mechanism to address the above challenges. It takes into account network constraints and fairness among prosumers. In the proposed energy sharing market, all prosumers play a generalized Nash game. The market equilibrium is proved to have nice features in a large market or when it is a variational equilibrium. To deal with the possible market failure, inefficiency, or instability in general cases, we introduce a price regulation policy to avoid market power exploitation. The improved energy sharing mechanism with price regulation can guarantee existence and uniqueness of a socially near-optimal market equilibrium. Some advantageous properties are proved, such as prosumer’s individual rationality, a sharing price structure similar to the locational marginal price, and the tendency towards social optimum with an increasing number of prosumers. For implementation, a practical bidding algorithm is developed with convergence condition. Experimental results validate the theoretical outcomes and show the practicability of our model and method. © 2022, CC BY-NC-ND.

关键词： Energy resources

Research on experimental method of high-speed railway perimeter intrusion monitoring technology based on multi-sensor fusion 24

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Research on experimental method of high-speed railway perime...

Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Xue Yang Bo Zhang Tao Yu Ye Liang Wanpeng Zhang Wentao Li Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing China School of Automation and Software Engineering Shanxi University Taiyuan Shanxi China China Railway Nanchang Group Co. Ltd. Nanchang Jiangxi China Graduate Department China Academy of Railway Sciences Beijing China

ISBN: (纸本)9798400709753

In recent years, China's high-speed railway has achieved rapid development, and has become the largest and fastest high-speed railway country in the world. By the end of 2023, the operating mileage has reached 159,000 km, including 45,000 km of high-speed railway. With the continuous expansion of high-speed railway development scale, the country also attaches more and more importance to railway operation safety. The report of the 20th National Congress of the Communist Party of China pointed out that we should accelerate the construction of a modern infrastructure system, transportation equipment system, transportation service system, and transportation governance system, promote the safe, intelligent, green and open development of transportation, and find a new road of transportation modernization with Chinese characteristics. Many scholars have successively proposed high-speed railway perimeter monitoring technology schemes based on various sensor devices. However, due to the diversity of monitoring equipment, uneven data interfaces and unified data standards, centralized testing of high-speed railway perimeter intrusion monitoring technology based on different types and models of sensing equipment has been unable to be carried out, and synchronous testing cannot be conducted in a unified test environment. In order to solve the above problems, this paper carried out research on device interface, data standardization, sample library construction methods and test methods, and compared various high-speed railway perimeter intrusion monitoring technology schemes based on multi-sensor fusion to verify the functional performance and environmental adaptability of various monitoring devices in actual operation scenarios.

关键词： Data standard

Addressing Intermediate Verification Latency in Online Learning Through Immediate Pseudo-labeling and Oriented Synthetic Correction

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Addressing Intermediate Verification Latency in Online Learn...

International Joint Conference on Neural Networks (IJCNN)

作者： Zixin Zhong Liyan Song Fengzhen Tang Bo Yuan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Faculty of Computing Harbin Institute of Technology Harbin China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In non-stationary data streams, the challenges of concept drift are further compounded by the issue of Intermediate Verification Latency (IVL), which can impede timely model adaptation. IVL refers to the finite delay between the arrival of data features and their corresponding labels. This delay could pose a significant challenge in adapting models to new concepts, ultimately hindering predictive performance. However, existing IVL approaches exhibit certain limitations. Some approaches passively wait for delayed labels, thereby overlooking temporarily unlabeled data. Other approaches employ pseudo-labeling for immediate model updates, but may risk losing valuable information when reverting model states to rectify previous pseudo-labeling mistakes. To overcome these limitations, we propose a novel approach called Micro-cluster based Immediate Pseudo-Labeling with Oriented Synthetic Correction (MIPLOSC). MIPLOSC leverages micro-cluster systems to effectively capture data distributions, thus facilitating its two core components: immediate pseudo-labeling and oriented synthetic correction. The immediate pseudo-labeling mechanism facilitates immediate utilization of temporarily unlabeled data, and the oriented synthetic correction mechanism enables finergrained rectification from previous erroneous pseudo-labels and concept drift, minimizing the loss of learned information. Experimental studies validated the effectiveness of MIPLOSC in addressing IVL, demonstrating its superiority over competing methods in both space consumption and predictive performance across varying degrees of label delay.

关键词： Adaptation models Costs Concept drift Noise Neural networks Predictive models Robustness

LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion

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arXiv 2023年

作者： Xiong, Weiyi Liu, Jianan Huang, Tao Han, Qing-Long Xia, Yuxuan Zhu, Bing The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Vitalent Consulting Gothenburg Sweden The College of Science and Engineering James Cook University CairnsQLD4878 Australia The School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia The Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden

As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, as a new image view transformation strategy, "sampling" has been applied in a few image-based detectors and shown to outperform the widely applied "depth-based splatting" proposed in Lift-Splat-Shoot (LSS), even without image depth prediction. However, the potential of "sampling" is not fully unleashed. This paper investigates the "sampling" view transformation strategy on the camera and 4D imaging radar fusion-based 3D object detection. LiDAR Excluded Lean (LXL) model, predicted image depth distribution maps and radar 3D occupancy grids are generated from image perspective view (PV) features and radar bird’s eye view (BEV) features, respectively. They are sent to the core of LXL, called "radar occupancy-assisted depth-based sampling", to aid image view transformation. We demonstrated that more accurate view transformation can be performed by introducing image depths and radar information to enhance the "sampling" strategy. Experiments on VoD and TJ4DRadSet datasets show that the proposed method outperforms the state-of-the-art 3D object detection methods by a significant margin without bells and whistles. Ablation studies demonstrate that our method performs the best among different enhancement settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

Data-Driven DLT3 Federated Deep Reinforcement Learning for Secure and Efficient Autonomous Driving

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Rong Li, Qianxi Wang, Kan Khan, Fazlullah Alturki, Ryan Lan, Dapeng Xi’an University of Technology School of Computer and Engineering Xi’an710000 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Ningbo315104 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah21955 Saudi Arabia Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110003 China

As autonomous vehicles (AVs) become increasingly widespread, the intelligent driving control and safety concerns have emerged. Recent advents in Internet of Things (IoTs) and 6G technologies have vastly boosted AVs’ sensing and communication capabilities, thereby enabling the high-speed and real-time data exchange. However, efficiently utilizing data while safeguarding the privacy remains challenges. Thus, we propose a federated learning (FL) framework with the privacy protection to facilitate the deep reinforcement learning (DRL) for longitudinal vehicle safety tracking control. First, the proposed framework enables the efficient information sharing and policy updates via encrypted policy gradient transmission. Then, the twin delayed deep deterministic policy gradient (TD3) method makes the velocity decision in the local model, while the dual long short-term memory (LSTM) network captures the temporal trajectory information. Finally, stacked denoising autoencoders (SDAE) is used to extract high-level environmental features, and the dual-LSTM-TD3 (DLT3)-SDAE model is introduced to enhance the policy decision efficiency and accuracy. Experimental results demonstrate that within the FL framework supported by homomorphic encryption, the DLT3-SDAE-based AV agent surpasses other DRL methods in terms of collision avoidance and average rewards, in the LAF environment. The safety, efficiency and comfort of the DLT3-SDAE AV agent are also evaluated and demonstrated. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning

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arXiv 2024年

作者： Chen, Yang He, Tian Fu, Junfeng Wang, Ling Guo, Jingcai Hu, Ting Cheng, Hong The School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China The School of Automation Engineering University of Electronic Science and Technology of China Chengdu China The Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen China The No.1 Orthopedic Hospital of Chengdu Chengdu China

Skeleton-based action representation learning aims to interpret and understand human behaviors by encoding the skeleton sequences, which can be categorized into two primary training paradigms: supervised learning and self-supervised learning. However, the former one-hot classification requires labor-intensive predefined action categories annotations, while the latter involves skeleton transformations (e.g., cropping) in the pretext tasks that may impair the skeleton structure. To address these challenges, we introduce a novel skeleton-based training framework (C2VL) based on Cross-modal Contrastive learning that uses the progressive distillation to learn task-agnostic human skeleton action representation from the VisionLanguage knowledge prompts. Specifically, we establish the vision-language action concept space through vision-language knowledge prompts generated by pre-trained large multimodal models (LMMs), which enrich the fine-grained details that the skeleton action space lacks. Moreover, we propose the intra-modal self-similarity and inter-modal cross-consistency softened targets in the cross-modal representation learning process to progressively control and guide the degree of pulling vision-language knowledge prompts and corresponding skeletons closer. These soft instance discrimination and self-knowledge distillation strategies contribute to the learning of better skeleton-based action representations from the noisy skeleton-vision-language pairs. During the inference phase, our method requires only the skeleton data as the input for action recognition and no longer for vision-language prompts. Extensive experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets demonstrate that our method outperforms the previous methods and achieves state-of-the-art results. Code is available at: https://***/cseeyangchen/C2VL. Copyright © 2024, The Authors. All rights reserved.

关键词： Musculoskeletal system

RoW-based Parallel Control for Mixed Traffic Scenario: A Case Study on Lane-Changing

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RoW-based Parallel Control for Mixed Traffic Scenario: A Cas...

International Conference on Intelligent Transportation

作者： Jingru Yu Yi Yu Shengyue Yao Ding Wang Pinlong Cai Honghai Li Li Li Fei-Yue Wang Yilun Lin Shanghai Artificial Intelligence Laboratory Urban Computing Shanghai China Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group Department of Automation Tsinghua University Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology Macau China Institute of Automation Chinese Academy of Sciences Beijing China

To address the challenge of developing effective control methods for governing the entire driving process of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) in mixed traffic scenario, this paper proposes a parallel control framework with calibrating module and pricing control module. Based on parallel learning theory, this framework enables intelligent agents to take over both HDVs and CAVs and engage in right-of-way (RoW) negotiations in the mixed traffic network. Game theory is employed to model RoW transactions and incentivize transactions through utility compensation. Empirical datasets are used to showcase the performance of the proposed framework in a lane-changing scenario. Our findings demonstrate that the introduction of incentives can increase the RoW trading probability of drivers significantly. To the best of our knowledge, this is the first parallel control framework based on pricing mechanism allowing both CAVs and HDVs to negotiate based on intelligent agents.

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Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study

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arXiv 2024年

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Wang, Jiacheng Sikdar, Biplab Zhang, Ping College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

Data augmentation is a powerful technique to mitigate data scarcity. However, owing to fundamental differences in wireless data structures, traditional data augmentation techniques may not be suitable for wireless data. Fortunately, Generative Artificial Intelligence (GenAI) can be an effective alternative to wireless data augmentation due to its excellent data generation capability. This article systemically explores the potential and effectiveness of GenAI-driven data augmentation in wireless networks. We first briefly review data augmentation techniques, discuss their limitations in wireless networks, and introduce generative data augmentation, including reviewing GenAI models and their applications in data augmentation. We then explore the application prospects of GenAI-driven data augmentation in wireless networks from the physical, network, and application layers, which provides a GenAI-driven data augmentation architecture for each application. Subsequently, we propose a general generative diffusion model-based data augmentation framework for Wi-Fi gesture recognition, which uses transformer-based diffusion models to generate high-quality channel state information data. Furthermore, we develop residual neural network models for Wi-Fi gesture recognition to evaluate the role of augmented data and conduct a case study based on a real dataset. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss research directions for generative data augmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks