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Reinforcement Learning for Online Dispatching Policy in Real-Time Train Timetable Rescheduling

IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 478-490页

作者： Yue, Peng Jin, Yaochu Dai, Xuewu Feng, Zhenhua Cui, Dongliang Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Bielefeld Nature Inspired Computing and Engineering Group Bielefeld33619 Germany University of Surrey Department of Computer Science GuildfordGU2 7XH United Kingdom University of Surrey School of Computer Science and Electronic Engineering GuildfordGU2 7XH United Kingdom

Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed railways to maintain punctuality and efficiency in the presence of unexpected disturbances. However, it is challenging to promptly create a rescheduled timetable in real time. In this study, we propose a reinforcement-learning-based method for real-time rescheduling of high-speed trains. The key innovation of the proposed method is to learn a well-generalized dispatching policy from a large amount of samples, which can be applied to the TTR task directly. At first, the problem is transformed into a multi-stage decision process, and the decision agent is designed to predict dispatching rules. To enhance the training efficiency, we generate a small yet good-quality action set to reduce invalid explorations. Besides, we propose an action sampling strategy for action selection, which implements forward planning with consideration of evaluation uncertainty, thus improving search efficiency. Extensive experimental results demonstrate the effectiveness and competitiveness of the proposed method. It has been proven that the local policies trained by the proposed method can be applied to numerous problem instances directly, rendering it unnecessary to use human-designed rules. © 2000-2011 IEEE.

关键词： Reinforcement learning

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Distributed Adaptive Formation Control of Heterogeneous Multi-agent Systems with Unknown System Parameters 43

Distributed Adaptive Formation Control of Heterogeneous Mult...

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43rd Chinese Control Conference, CCC 2024

作者： Long, Xingjian Ze, Kunrui Wang, Wei Long, Jiang Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Zhongguancun Laboratory Beijing100194 China Northwestern Polytechnical University School of Computer Science Shaanxi Xi'an710072 China

ISBN: (纸本)9789887581581

In this paper, we consider the leaderless formation control problem for heterogeneous multi-agent systems (MASs) consisting of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with unknown system parameters. In order to solve the problem caused by nonidentical subsystem orders, a new distributed reference system is introduced in each agent, in which only the relative positions of the agents are involved. Then, a backstepping-based distributed adaptive control is proposed under directed graph condition. With the proposed control scheme, the boundedness of all the signals in the closed-loop system can be ensured and the positions of heterogeneous MASs can converge to a desired formation. Finally, experimental results are provided to demonstrate the effectiveness of the proposed control scheme. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Directed graphs

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Event-Triggered Adaptive Consensus Tracking Control of Uncertain Nonlinear Multi-agent Systems with Uniformly Positive Inter-Event Times 1

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Wu, Xiangkai Liu, Yuxiao Long, Jiang Wang, Wei School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710072 China Zhongguancun Laboratory Beijing100191 China

ISBN: (数字)9789819733408

ISBN: (纸本)9789819733392

In this paper, asymptotical consensus tracking control problem is considered for a class of uncertain nonlinear multi-agent systems under directed graph with event-triggered communication. In this work, the main challenge is to handle cross-coupling term associated with unknown parameters of all agents and asymmetric Laplacian matrix when the Lyapunov function candidate is selected in terms of local consensus errors. This leads to the difficulty in designing distributed asymptotical control laws which can guarantee a positive minimum inter-event time for each agent with event-triggered communication. To handle this issue, an event-triggered based control scheme is proposed by applying adaptive immersion & invariance control and concurrent learning adaptive control. We prove that all closed-loop signals are uniformly bounded, and all agents’ states can exponentially track the desired trajectory. Moreover, the inter-event times of all agents are uniformly lower bounded by a positive constant. Finally, an illustrative example is given to validate the effectiveness of the proposed control scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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Comparative Analysis of SVM and CNN Classifiers for EEG Signal Classification in Response to Different Auditory Stimuli

Comparative Analysis of SVM and CNN Classifiers for EEG Sign...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Tibermacine, Ahmed Akrour, Djouher Khamar, Rofeida Tibermacine, Imad Eddine Rabehi, Abdelaziz University of Biskra Department of Computer Science Biskra Algeria Sapienza University of Rome Department of Computer Automation and Management Engineering Rome Italy Zian Achour University of Djelfa Laboratory of Telecommunications and Smart Systems Faculty of Sciences and Technology Djelfa17000 Algeria

ISBN: (纸本)9798331527396

This study aims to classify brainwave patterns using electroencephalogram (EEG) signals in response to various auditory stimuli, specifically Quran recitation, participants' favorite music, and Interstellar's main theme. By analyzing the unique EEG responses elicited by these stimuli, the study explores cognitive and emotional processing differences. Two prominent classifiers, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), were utilized. SVM, combined with Power Spectral Density (PSD) features, achieved the highest accuracy of 95% when distinguishing Quran recitation from rest. CNN, particularly with Kernel Density Estimation (KDE) features, exhibited strong performance in distinguishing between different auditory stimuli, with an average accuracy exceeding 90%. These results highlight the critical role of feature extraction techniques, such as PSD and KDE, in improving classification performance and deepening our understanding of EEG-based emotional and cognitive responses to auditory stimuli. © 2024 IEEE.

关键词： Convolutional neural networks

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Cross-Modal Recipe Retrieval With Fine-Grained Prompting Alignment and Evidential Semantic Consistency

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IEEE Transactions on Multimedia 2025年 27卷 2783-2794页

作者： Huang, Xu Liu, Jin Zhang, Zhizhong Xie, Yuan Tang, Yongqiang Zhang, Wensheng Cui, Xiaohui Wuhan University School of Computer Science Wuhan430072 China Ministry of Education Wuhan430062 China East China Normal University School of Computer Science and Technology Shanghai200062 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Alignment between the food images and the corresponding recipes is an emerging cross-modal representation learning task. In this task, the recipes are composed of three components, i.e., food title, ingredient lists, and cooking instructions, which require a fine-grained alignment between the features of the two modalities. Existing methods usually aggregate the recipes into global embeddings and then align them with the global image embeddings. Meanwhile, semantic classification is frequently used in these methods to regularize the embeddings of the two modalities. While these methods are efficient, there remain two problems. (1) Forcing the alignment between the global images and recipes embeddings may result in losing the component-specific information. (2) The high diversity of food appearance leads to high uncertainty in the semantic classification of food images and recipes. To solve these problems, we propose a Fine-grained Prompting and Alignment (FPA) model to enhance the feature extraction and bring more component-specific information for fine-grained alignment. Furthermore, to regularize the semantic information contained in the cross-modal features, we design an Evidential Semantic Consistency (ESC) loss to keep the cross-modal semantic consistency. We have conducted comprehensive experiments on the benchmark dataset Recipe1M and the state-of-the-art results on the cross-modal recipe retrieval task demonstrate the effectiveness of our method. © 1999-2012 IEEE.

关键词： Alignment

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Exact Algorithm for Batch Scheduling with Many-to-Many Job-Tool Matching and Job Release Time Constraints

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IEEE Transactions on automation Science and engineering 2025年 22卷 16499-16511页

作者： Li, Shengchao Liu, Shixin Zhao, Ziyan Zhou, Mengchu Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang 110819 China Northeastern University at Qinhuangdao Hebei Key Laboratory of Industrial Software for Advanced Planning and Scheduling of Complex Industrial Processes School of Control Engineering Qinhuangdao 066004 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark 07102 NJ United States

Job-dependent tool switching is necessary in many batch processing systems (BPSs). Heterogeneous tool demand and extra time consumption for tool switch bring great challenge for high-performance production scheduling in BPSs. In this study, we present a novel batch scheduling problem derived from a satellite vibration test system. Different from basic scheduling problems in BPSs with tool switching, it has complications caused by many-to-many task-tool matching and job release time constraints. Specifically, each job has predetermined candidate tools. One of them should be selected and equipped to process a given job. Unlike sequence-dependent setup time that is only related to two adjacent jobs, tool switching time is additionally dependent on the tools selected by both jobs from their respective candidate tools. Both job sequence and tool selections need to be determined to minimize makespan. Mixed-integer linear programming (MILP) and constraint programming (CP) models are developed for formulating the problem. We prove that the problem is NP hard. To obtain exact solutions, we propose a novel branch-and-bound algorithm (B&B) based on the analysis and extraction of problem properties. It integrates a domain reduction mechanism, branching strategies, and dominance rules. Among them, the domain reduction mechanism reduces the number of jobs to be scheduled;the branching strategies accelerate solving speed;and the dominance rules save the memory consumption. Ablation studies and key influencing factor experiments are performed. The results show that the proposed B&B can optimally solve much larger instances than state-of-the-art methods (including some heuristics). It can optimally solve industrial-size problems in a short time, implying that it can well satisfy the performance demand of a satellite vibration test system and thus advancing the filed of optimal scheduling for BPSs. © 2004-2012 IEEE.

关键词： Batch scheduling branch-and-bound algorithm heterogenerous tool demand mixed-integer linear programming satellite vibration test system

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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A power allocation strategy to maximize CR-NOMA throughput 7

A power allocation strategy to maximize CR-NOMA throughput

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7th IEEE Information Technology and Mechatronics engineering Conference, ITOEC 2023

作者： Ma, Xiaolin Peng, Yi Yang, Qingqing Kunming University of Science and Technology College of Information Engineering and Automation Kunming China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Science Kunming China

ISBN: (纸本)9798350334197

Aiming at the problem that the total throughput of secondary users is not maximized, considering the fairness between secondary users and the interference of primary users to secondary users, a power allocation strategy to maximize the throughput of CR-NOMA is proposed. Firstly, the elementary matrix is established according to the equivalent channel gain, and the secondary user is selected and multiplexed to the primary user sub-channel. Then, for the selected secondary users, the minimum transmission rate is dynamically adjusted based on fairness, and the optimal power scaling factor under the maximization of the total throughput of the secondary users is calculated. Finally, power distribution is performed. The simulation results show that the proposed algorithm can effectively reduce the complexity of the algorithm and maximize the total throughput of the secondary users compared with the HPPA and CR-OMA algorithms. © 2023 IEEE.

关键词： Cognitive radio

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method 32

Texture Analysis of Breast US Images Using Morphological Tra...

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32nd Mediterranean Conference on Control and automation, MED 2024

作者： Tabacaru, Gigi Moldovanu, Simona Barbu, Marian 'Dunarea de Jos' University of Galati Faculty of Automation Computers Electrical Engineering and Electronics Department of Automatic Control and Electrical Engineering Romania 'Dunarea de Jos' University of Galati Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Romania Dunarea de Jos University of Galati The Modelling & Simulation Laboratory Galati800008 Romania

ISBN: (纸本)9798350395440

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer. © 2024 IEEE.

关键词： Fractal dimension

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Fusion enhancement : UAV target detection based on multi-modal GAN 7

Fusion enhancement : UAV target detection based on multi-mod...

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7th IEEE Information Technology and Mechatronics engineering Conference, ITOEC 2023

作者： Li, Rui Peng, Yi Yang, Qingqing Kunming University of Science and Technology College of Information Engineering and Automation Kunming China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Science Kunming China

ISBN: (纸本)9798350334197

UAV target detection has important application value in the fields of modern military, security and emergency rescue. In order to improve the accuracy and robustness of UAV target detection, this paper uses image fusion technology to enhance the information of input images, and combines YOLOv5 target detection model to introduce attention mechanism for improvement. In the experiment, the generative adversarial network (GAN) is used as the image fusion technology, and the fusion image is generated by changing the structure of the generator and the discriminator. At the same time, the attention mechanism is introduced into the YOLOv5 model to enhance the focus of target detection. The experimental results show that the proposed improved algorithm has achieved significant performance improvement in the UAV target detection task. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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