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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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Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation

Fuzzy Positive Learning for Semi-Supervised Semantic Segment...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Pengchong Qiao Zhidan Wei Yu Wang Zhennan Wang Guoli Song Fan Xu Xiangyang Ji Chang Liu Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Department of Automation and BNRist Tsinghua University Beijing China

Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevitable error-prone pseudo-labeling, we think differently and resort to exhausting informative semantics from multiple probably correct candidate labels. In this paper, we introduce Fuzzy Positive Learning (FPL) for accurate SSL semantic segmentation in a plug-and-play fashion, targeting adaptively encouraging fuzzy positive predictions and suppressing highly-probable negatives. Being conceptually simple yet practically effective, FPL can remarkably alleviate interference from wrong pseudo labels and progressively achieve clear pixel-level semantic discrimination. Concretely, our FPL approach consists of two main components, including fuzzy positive assignment (FPA) to provide an adaptive number of labels for each pixel and fuzzy positive regularization (FPR) to restrict the predictions of fuzzy positive categories to be larger than the rest under different perturbations. Theoretical analysis and extensive experiments on Cityscapes and VOC 2012 with consistent performance gain justify the superiority of our approach. Codes are provided in https://***/qpc1611094/FPL.

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Two-timescale Distributed Approach to Nash Equilibrium Seeking of Euler-Lagrange Systems in Multi-cluster Nonconvex Games

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IEEE Transactions on Automatic Control 2025年

作者： Huang, Banghua Liu, Yang Lu, Jianquan Jiang, Yunliang Gui, Weihua Zhejiang Normal University School of Mathematical Sciences Jinhua321004 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application School of Mathematical Sciences Jinhua321004 China Zhejiang Normal University Hangzhou School of Automation Hangzhou311231 China Southeast University School of Mathematics Nanjing210096 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application School of Computer Science and Technology Jinhua321004 China Huzhou University School of Information Engineering Huzhou313000 China Central South University School of Automation Changsha410083 China

In this paper, we investigate a distributed generalized Nash equilibrium (GNE) seeking problem for multi-cluster games, where each cluster consists of multiple players. Different from most of the existing multi-cluster game studies, the multi-cluster games considered nonconvex cost functions, nonconvex (coupled) inequality constraints, and nonaffine (coupled) equality constraints. Each player in a cluster communicates and cooperates with other players based on a directed connected network to make optimal actions that minimize the cost function of their cluster. We propose a two-timescale multi-agent system (MAS) for multi-cluster nonconvex games. We further propose a two-timescale MAS in which players are formulated by Euler-Lagrange (EL) dynamics. We prove the convergence of the MAS with derived conditions to one of the local GNEs. We elaborate on a numerical example, a price-bidding problem in an electricity market, and a Nash-Cournot game to illustrate the characteristics of the proposed approach. © 1963-2012 IEEE.

关键词： Cost functions

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Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices

arXiv

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arXiv 2023年

作者： Li, Peichun Zhang, Hanwen Wu, Yuan Qian, Liping Yu, Rong Niyato, Dusit Shen, Xuemin The State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China School of Automation Guangdong University of Technology Guangzhou China Guangdong Key Laboratory of IoT Information Technology Guangzhou China The School of Computer Science and Engineering Nanyang Technological University Block N4-02a-32 Nanyang Avenue Singapore Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices’ resource utilization efficiency. In this paper, we propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a resource-aware data augmentation method that effectively mitigates the data heterogeneity while ensuring efficient FL training. We first quantify the relationship between the training data amount and the learning performance. We then study the FIMI optimization problem with the objective of minimizing the device-side overall energy consumption subject to required learning performance constraints. The decomposition-based analysis and the cross-entropy searching method are leveraged to derive the solution, where each device is assigned suitable AI-synthesized data and resource utilization policy. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy in comparison with the existing methods. Meanwhile, FIMI can significantly enhance the converged global accuracy under the non-independently-and-identically distribution (non-IID) data. © 2023, CC BY.

关键词： Energy utilization

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Adaptive Dynamic Event-Triggered Control for Switched Systems

Adaptive Dynamic Event-Triggered Control for Switched System...

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第40届中国控制会议

作者： Yuzhong Wang Jun Zhao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of computer science and engineering Northeastern University

This paper deals with event-triggered control design for switched systems. Firstly, an adaptive dynamic eventtriggering mechanism is proposed to save communication resources. Secondly, sufficient stability conditions to verdict exponentially stable and stabilization conditions to co-design the controller gain are obtained in terms of LMIs by using a common Lyapunov function. Thirdly, the positive lower-bound of the inter-execution time intervals is provided such that the Zeno behavior can be avoided. Finally, a simulation is provided to illustrate the effectiveness of the proposed approach.

关键词： Event-triggered switched systems common Lyapunov function

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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computer Systems science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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Efficient Graph Neural Network Driven Recurrent Reinforcement Learning for GNSS Position Correction 36

Efficient Graph Neural Network Driven Recurrent Reinforcemen...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Zhao, Haoli Tang, Jianhao Li, Zhenni Wu, Zhuoyu Xie, Shengli Wu, Zhaofeng Liu, Ming Kumara, Banage T.G.S. School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510000 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (纸本)9780936406350

With the wide applications of the Global Navigation Satellite System (GNSS) in autonomous driving scenarios, the demand for high-precision positioning of navigation systems has increased dramatically in complex multipath environments. Conventional model-based methods are constrained by strict assumptions about noise models and can hardly model complex environment errors. In contrast, approaches based on artificial intelligent learning have become an important direction to solving the problem of high-precision positioning because learning-based approaches only require simple assumptions. However, current learning-based approaches are facing the following issues. The existing Graph Neural Network-based (GNN) method could hardly adapt to dynamically changing driving environment scenarios since it considers positioning discretely. On the other hand, existing Reinforcement Learning-based (RL) approaches ignore the relationship between multi-constellation satellites, resulting in an inadequate description of the driving correction environment observations. In this paper, we construct a GNN-driven recurrent reinforcement learning method to consider the GNSS measurement of multi-constellation satellites and to learn real-time correction strategy in the dynamic driving environment. To establish a comprehensive positioning correction environment, we construct a multi-constellation graph observation, based on the feature vector concerning GNSS measurement of multi-constellation satellites and edges for satellites in and between constellations. To make more effective use of GNSS measurements, we employ the graph embedding module to deal with the multi-constellation graph inputs, to extract hidden topological features to form the brief states about relationships between multi-constellation satellites for the RL environment. Finally, we construct a recurrent actor-critic structured RL model with cumulative reward and continuous action space to exploit historical information and a

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An Improved YOLOv5 Detection Method for Strip Surface Defect

An Improved YOLOv5 Detection Method for Strip Surface Defect

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International Conference on automation and Computing (ICAC)

作者： Yefeng Liu Longfei Yu Qichun Zhang dept. Liaoning Key Laboratory of Information Physical Fusion and Intelligent CNC Machine Tool Manufacturing Shenyang Institute of Technology dept. School of Automation and Electrical Fushun China dept. School of Automation and Electrica Engineering Shenyang Ligong University dept. Department of Computer Science University of Bradford Bradford U. K.

Traditional steel surface defect detection methods have issues such as low detection accuracy and efficiency. It is suggested a technique based on enhanced YOLOv5 steel surface fault identification. In order to improve the network's capacity to handle feature information, this study suggests adding SE attention mechanisms to the YOLOv5s network's Backbone, Neck, and SPPF and replacing the C3 structure at the end of the Backbone with C3TR. To achieve network model lightweight, it is proposed to replace the Neck part in the YOLOv5s model and the convolution in SPPF with Ghost convolution. Then, to improve network feature fusion, extend the improved SPPF to the Neck part. Introduce the SPD convolution in the Backbone section to reduce the information loss caused by the neural network's convolution. The tested mAP is 75.3%, which is 5.3% higher than the original model, 24.6% higher than the SSD method, and 4.6% higher than the YOLOv6 algorithm after the upgraded model has been trained on the NEU-DET dataset. The observed FPS is 81.3, which is 6.3 more than the relative literature algorithm and 18.0 more than the SSD algorithm.

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Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing

arXiv

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arXiv 2024年

作者： Zhao, Yong Zhu, Zhengqiu Gao, Chen Wang, En Huang, Jincai Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Hunan Province Changsha410073 China BNRist Tsinghua University Beijing100084 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Spatial Crowdsourcing (SC) is gaining traction in both academia and industry, with tasks on SC platforms becoming increasingly complex and requiring collaboration among workers with diverse skills. Recent research works address complex tasks by dividing them into subtasks with dependencies and assigning them to suitable workers. However, the dependencies among subtasks and their heterogeneous skill requirements, as well as the need for efficient utilization of workers’ limited work time in the multi-task allocation mode, pose challenges in achieving an optimal task allocation scheme. Therefore, this paper formally investigates the problem of Dependency-aware Multi-task Allocation (DMA) and presents a well-designed framework to solve it, known as Heterogeneous Graph Reinforcement Learning-based Task Allocation (HGRL-TA). To address the challenges associated with representing and embedding diverse problem instances to ensure robust generalization, we propose a multi-relation graph model and a Compound-path-based Heterogeneous Graph Attention Network (CHANet) for effectively representing and capturing intricate relations among tasks and workers, as well as providing embedding of problem state. The task allocation decision is determined sequentially by a policy network, which undergoes simultaneous training with CHANet using the proximal policy optimization algorithm. Extensive experiment results demonstrate the effectiveness and generality of the proposed HGRL-TA in solving the DMA problem, leading to average profits that is 21.78% higher than those achieved using the metaheuristic methods. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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LMI methods for extended H8Filters for Landmark-based mobile robot localization

LMI methods for extended H8Filters for Landmark-based mobile...

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2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021

作者： Fajardo, Julio Ferman, Victor Guerra, Jabes Neto, Antonio Ribas Rohmer, Eric FEEC UNICAMP Department of Computer Engineering and Industrial Automation SP Campinas13083-852 Brazil Turing Research Laboratory FISICC Galileo University Guatemala City Guatemala Federal Institute of Education Science and Technology Catarinense SC Luzerna89609-000 Brazil

ISBN: (纸本)9781665441391

Localization is still one of the most fundamental tasks for autonomous navigation of mobile robots. However, the existing methods lack robustness when dealing with uncertainties without assuming some characteristics about noise inputs and the nonlinearity of the measurement models. In this work, a theoretical basis for designing two separate extended robust filters based on linear matrix inequalities is proposed to solve the localization problem. The first approach is based on the design of an H8 observer-based filter through a two-step prediction correction structure. In this way, a convex optimization problem needs to be solved at each time step to determine the observer-gain that corrects the predicted pose of a differential wheeled robot. The second approach considers the advantages of a full-order filter which guarantees a better performance under the H8 robust requirements. Besides, satisfactory results that validate theoretical remarks were performed in real and virtual scenarios through simulation frameworks. © 2021 IEEE.

关键词： Robust control

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