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Research Progress of Parallel control and Management

IEEE/CAA Journal of automatica Sinica 2020年第2期7卷 355-367页

作者： Gang Xiong Xisong Dong Hao Lu Dayong Shen State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China College of Systems Engineering National University of Defense TechnologyChangsha 410073China

Based on ACP(artificial systems,computational experiments,and parallel execution)methodology,parallel control and management has become a popularly systematic and complete solution for the control and management of complex *** paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework,core technologies,and the application *** future research,application directions,and suggestions are also discussed.

关键词： ACP methodology artificial systems computational experiments parallel control parallel management parallel systems

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

arXiv

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arXiv 2025年

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C.L. Philip The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of New Network Pengcheng Laboratory Shenzhen518000 China The China Tobacco Guangdong Industrial Corporation Guangzhou510385 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contextual information among image patches. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. Copyright © 2025, The Authors. All rights reserved.

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Design and Implementation of a Two-Limbed 3T1R Haptic Device

Design and Implementation of a Two-Limbed 3T1R Haptic Device

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Long Kang Yang Yang Byung-Ju Yi PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Xi'an Modern Control Technology Research Institute Xi'an China School of Automation Nanjing University of Information Science and Technology Nanjing China Department of the Electronic Systems Engineering Hanyang University Ansan South Korea

This paper presents a haptic device with a simple architecture of only two limbs that can provide translational motion in three degrees of freedom (DOF) and one-DOF rotational motion. Actuation redundancy eliminates all forward-kinematic singularities and improves the motion-force transmission property. Thanks to the special structure of the kinematic chains, all actuators are close to the base and full gravity compensation is achieved passively by using springs. Force producibility analysis shows that this haptic device is able to produce long-term continuous force feedback of 15–30 N in each direction. By developing a prototype of the haptic device and a virtual three-dimensional simulator, a preliminary performance evaluation of the haptic device was conducted. In addition, a torque distribution algorithm considering a relaxed form of actuator-torque saturation was experimentally evaluated, and a comparison with other algorithms reveals that this algorithm offers several advantages.

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Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

arXiv

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arXiv 2023年

作者： Xiao, Jiaren Dai, Quanyu Shen, Xiao Xie, Xiaochen Dai, Jing Lam, James Kwok, Ka-Wai Department of Mechanical Engineering The University of Hong Kong Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Hainan University Haikou China Department of Automation Harbin Institute of Technology Shenzhen China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be formally considered by the existing approaches designed for cross-graph node classification. This paper proposes a novel method called SemiGCL to tackle the graph Semi-supervised domain adaptation with Graph Contrastive Learning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph’s local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks. © 2023, CC BY-NC-ND.

关键词： Entropy

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Joining Edge-Enabled Metaverse Services with Network Externality: A Stackelberg Game Approach

Joining Edge-Enabled Metaverse Services with Network Externa...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Yuna Jiang Jiawen Kang Dusit Niyato Xiaohu Ge Zehui Xiong Minrui Xu Ruilong Deng School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

The metaverse is defined as a three-dimensional virtual-real fusion network focused on social connection. Edge computing can empower the metaverse by providing computing resources to realize real-time motion tracking, visual or auditory rendering and virtual scene dynamic change at the edge of networks. Users enjoy the immersive experience provided by the Metaverse Service Provider (MSP), and interact through digital avatars in the virtual world by requesting metaverse services. In this paper, we formulate the edge computing resources pricing, metaverse service rendering bitrate pricing and request problem in the metaverse. Especially, we model the interactions among edge servers, the MSP and Metaverse Service Users (MSUs) as a hierarchical three-stage Stackelberg game. The problem of joining edge-enabled metaverse services is also studied with the consideration of network externality, including the network effect and congestion effect. Based on the backward induction method, we derive the optimal computing resources pricing of edge servers, rendering bitrate pricing of the MSP, and rendering bitrate requests of MSUs. We also prove the existence and uniqueness of the Stackelberg game equilibrium. Simulation results show that the network effect can bring more positive utilities to entities when the social tie coefficient among MSUs is higher. Besides, all entities in the edge-enabled metaverse are affected negatively by the congestion effect.

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Application of Machine Learning to Performance Assessment for a class of PID-based control Systems

arXiv

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arXiv 2021年

作者： Grelewicz, Patryk Khuat, Thanh Tung Czeczot, Jacek Nowak, Pawel Klopot, Tomasz Gabrys, Bogdan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Gliwice44100 Poland University of Technology Sydney Faculty of Engineering and IT Advanced Analytics Institute NSW2007 Australia

In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control industrial loops with processes exhibiting dynamical properties close to second order plus delay time (SOPDT). The proposed concept is very general and easy to configure to distinguish between acceptable and poor closed loop performance. This approach allows for determining the best (but also robust and practically achievable) closed loop performance based on very popular and intuitive closed loop quality factors. Training set can be automatically derived off-line using a number of different, diverse control performance indices (CPIs) used as discriminative features of the assessed control system. The proposed extended set of CPIs is discussed with comprehensive performance assessment of different machine learning based classification methods and practical application of the suggested solution. As a result, a general-purpose CPA system is derived that can be immediately applied in practice without any preliminary or additional learning stage during normal closed loop operation. It is verified by practical application to assess the control system for a laboratory heat exchange and distribution *** Codes 93 © 2021, CC BY-NC-SA.

关键词： Three term control systems

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Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

Machine Learning-Based Cascade Filtering System for fNIRS Da...

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Progress in Applied Electrical engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Tuukka Ruotsalo Luis A. Leiva Adam Sudoł Edward Jacek Gorzelańczyk Adam Łysiak Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Helsinki Finland University of Copenhagen Denmark Department of Computer Science University of Luxembourg Luxembourg Luxembourg Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence.

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Intelligent survey method of rice diseases and pests using AR glasses and image-text multimodal fusion model

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computers and Electronics in Agriculture 2025年 237卷

作者： Chen, Xiangfu Yang, Baojun Liu, Yongjian Feng, Zelin Lyu, Jun Luo, Ju Wu, Jian Yao, Qing Liu, Shuhua School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China State Key Laboratory of Rice Biology and Breeding China National Rice Research Institute Hangzhou311401 China School of Information and Control Keyi College of Zhejiang Sci-Tech University Hangzhou310018 China School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China

The timely and accurate detection of the occurrence types and dynamics of rice diseases and insect pests in the field is a fundamental requirement for effective scientific prevention and control. Currently, survey methods rely heavily on the expertise and experience of surveyors, leading to challenges such as limited data traceability, high labor demands, and low efficiency. The complex environmental conditions in rice fields, coupled with the diversity of pests and diseases—many of which coexist and exhibit significant intraspecies variation and interspecies similarities—further complicate detection efforts. When identification models are trained using only a limited set of image samples, they often suffer from poor generalization, undermining the accuracy of pest and disease forecasts. To overcome these challenges, a rapid, efficient, and precise intelligent survey method using AR glasses and image-text multimodal fusion model to detect rice pests and diseases was proposed. The AR glasses have advantages of wearable, hands-free, voice-control functions, which is very convenient to collect rice images in paddy fields. An image-text multimodal fusion model with two stages, RDP-Detector, was developed to improve the detection accuracy rates of rice pest and disease lesions in the images. In the first stage, the improved YOLOv5X model with AF-FPN, Decoupled Head and Soft-NMS post-processing achieved improvements in detection ability. In the second stage, text modalities are introduced, and Prompt tuning is used to perform transfer learning for downstream tasks on the basis of the CLIP model. To improve the accuracy of pest detection, the detection boxes with low confidence in the first stage are subjected to reidentification in the second stage. Compared with the state of the art models, the RDP-Detector achieved an precision, recall, and mAP of 82.3 %, 86.5 %, and 87.4 %, respectively, on the detection of seven rice pests and diseases. Compared with the object detect

关键词： Image enhancement

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Communication-Efficient Personalized Federated Learning for Green Communications in IoMT

Communication-Efficient Personalized Federated Learning for ...

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IEEE International Conference on Communications (ICC)

作者： Ziqi Chen Jun Du Chunxiao Jiang Yunlong Lu Zhu Han Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The rapid development of the Internet of Medical Things (IoMT) has brought about an enormous amount of healthcare data. Effectively and securely processing this sensitive data has become a significant challenge for green communication and privacy protection of the IoMT. As a decentralized learning framework, Federate learning (FL) enables model training without directly aggregating users' raw data, thus ensuring user privacy protection. Moreover, numerous studies have put forth various approaches to enhance the efficiency of FL by minimizing communication costs, yet they may not fully account for the unique characteristics of IoMT. Specifically, the efficiency and performance of model training are closely related to patient life and health. Meanwhile, existing research has indicated that reducing communication costs can result in a decline in training accuracy, which may be critical to patient health. Therefore, aimed at green communication and ensuring the model accuracy, we design a communication-efficient personalized federated learning framework, namely pFedCAS. Specifically, we introduce a control unit, which enables adaptive sparsity of local models, to reduce training costs. Furthermore, a selection unit based on communication quality is added into the global aggregation, which can select suitable clients for model updating. Simulation results validate that the proposed method can significantly reduce communication costs while ensuring the model accuracy. Additionally, The simulation results also validate the excellent robustness of our method to non-iid healthcare data.

关键词： Training Adaptation models Data privacy Costs Accuracy Federated learning Computational modeling

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