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6th IEEE Conference on Energy Internet and Energy system Integration, EI2 2022

作者： Li, Longyan Ning, Chao Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350347159

This paper proposes a novel two-level electricity and hydrogen market framework for multi-microgrids (MMGs) coupled with offsite hydrogen refueling stations (HRSs), aiming to strengthen the synergy between electricity and hydrogen. The local hydrogen market among the MMG and HRSs is modeled by multi-leader multi-follower Stackelberg game theory, in which microgrids play the role of leaders to adjust hydrogen prices while HRSs participate as followers to determine their hydrogen purchase scheme. Moreover, to deal with economic and technical issues of the MMG in a holistic manner, Nash bargaining theory is used to model the electricity transaction among microgrids. A distributed algorithm is developed to solve such games, thus reducing the cost of information communication and the risk of privacy exposure. The case study demonstrates that this market mechanism guarantees benefits for all parties and boosts the independence of MMGs by reducing transactions with the main grid. © 2022 IEEE.

关键词： Game theory

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Model Predictive control Based on Amplitude Decaying Aggregation Strategy for Hydrogen Gas Turbine

Model Predictive Control Based on Amplitude Decaying Aggrega...

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2022 Chinese automation Congress, CAC 2022

作者： Ma, Aoyun Li, Dewei Xi, Yugeng Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665465335

The hydrogen gas turbine can be the key technology for carbon-neutral in the future. The flexibility of gas turbine under various operation conditions is an important issue. A state-space model of hydrogen gas turbine with different hydrogen volume ratio mixed fuel is constructed based on Rowen model, and a full-dimensional state observer is designed to obtain the states of state-space model. The model predictive control method based on amplitude decaying aggregation strategy is presented to stabilize the rotational speed of hydrogen gas turbine under various constraints, which are to ensure the safety of operation. The number of variables contained in the optimization problem to be solved online is reduced by aggregation strategy. The simulation results confirmed that the presented method works well, and the effects of the hydrogen volume ratio for the performance of hydrogen gas turbine are discussed. © 2022 IEEE.

关键词： Model predictive control

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An Expansion-based Document Ranking Framework Incorporated with Core Concern Capturing 5

An Expansion-based Document Ranking Framework Incorporated w...

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5th International Conference on Intelligent Autonomous systems, ICoIAS 2022

作者： Qi, Yinqiao Xin, Jinghao Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

Entity-based ranking is a popular research area in document ranking as knowledge graphs can introduce additional evidence for ranking systems. In most entity-based ranking framework, the original query is transformed into a set of entities and candidate documents are ranked according to their relevance to the entity set. However, mistakes can easily occur during the transformation process, which introduce noise to the ranking system. This paper represents an expansion-based ranking framework, which expand the original query with high-quality candidate entities to mitigate the impact of transformation mistakes and enrich the semantics of the query in the meanwhile. Compared with existing query expansion methods, our expansion method is lightweight as it reduces much computational burden and requests little extra information. However, the core concerns of the users might be covered up after query expansion. To alleviate this dilemma, a core concern capturing method is pro-posed, wherein the attention weight of each inter-entity relation is measured to capture the core entity pairs in the expanded query. Experiments on the S2-CS dataset demonstrate the effectiveness of our lightweight query expansion method and the superiority of our core concern capturing method. Compared with a classical retrieval model, our method provides 11% improvements in terms of NDCG@15. © 2022 IEEE.

关键词： Semantics

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Certificated Actor-Critic: Hierarchical Reinforcement Learning with control Barrier Functions for Safe Navigation

arXiv

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arXiv 2025年

作者： Xie, Junjun Zhao, Shuhao Hu, Liang Gao, Huijun Department of Automation School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

control Barrier Functions (CBFs) have emerged as a prominent approach to designing safe navigation systems of robots. Despite their popularity, current CBF-based methods exhibit some limitations: optimization-based safe control techniques tend to be either myopic or computationally intensive, and they rely on simplified system models;conversely, the learning-based methods suffer from the lack of quantitative indication in terms of navigation performance and safety. In this paper, we present a new model-free reinforcement learning algorithm called Certificated Actor-Critic (CAC), which introduces a hierarchical reinforcement learning framework and well-defined reward functions derived from CBFs. We carry out theoretical analysis and proof of our algorithm, and propose several improvements in algorithm implementation. Our analysis is validated by two simulation experiments, showing the effectiveness of our proposed CAC algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Event-triggered learning of Euler-Lagrange systems: A Koopman operator framework

Event-triggered learning of Euler-Lagrange systems: A Koopma...

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IEEE Conference on Decision and control

作者： Kaikai Zheng Dawei Shi Shilei Li Yang Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Mechanical Engineering Faculty of Engineering University of Victoria Victoria BC Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Euler-Lagrange (EL) systems represent a crucial and large class of dynamical systems, and a precise model of the true system would be beneficial in planning and tracking problems. This work aims to learn an unknown EL system using noisy measurement data to achieve improved data utilization efficiency. Specifically, for the considered EL system, a linear representation of the system is constructed using the Koopman operator, which is further characterized by sample data using Willems’ fundamental lemma. Moreover, an event-triggered learning mechanism is proposed to improve data utilization efficiency, and it is designed based on the analysis of the learning error bounds. The effectiveness of the proposed event-triggered learning approach is validated through a manipulator example.

关键词： Learning systems Event detection Manipulators Safety Planning Noise measurement Dynamical systems

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Online Vehicle Forensics Method of Responsible Party for Accidents Based on LSTM-BiDBN External Intrusion Detection

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Journal of Shanghai Jiaotong university(Science) 2024年第6期29卷 1161-1168页

作者： LIU Wen XU Jianxin YANG Genke CHEN Yuanfang Ningbo Industrial Internet Institute Ningbo 315000ZhejiangChina Ningbo Artificial Intelligence Institute of Shanghai Jiao Tong University Ningbo 315000ZhejiangChina Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China School of Cyberspace Hangzhou Dianzi UniversityHangzhou 310018China

Vehicle data is one of the important sources of traffic accident digital *** propose a novel method using long short-term memory-deep belief network by binary encoding(LSTM-BiDBN)controller area network identifier(CAN ID)to extract the event sequence of CAN IDs and the semantic of CAN IDs *** of detecting attacks only aimed at a specific CAN ID,the proposed method fully considers the potential interaction between electronic control *** this means,we can detect whether the vehicle has been invaded by the outside,to online determine the responsible party of the *** use our LSTM-BiDBN to distinguish attack-free and abnormal situations on *** results show that our proposed method is more effective in identifying anomalies caused by denial of service attack,fuzzy attack and impersonation attack with an accuracy value of 97.02%,a false-positive rate of 6.09%,and a false-negative rate of 1.94%compared with traditional methods.

关键词： digital forensics deep belief network(DBN) long short-term memory(LSTM) binary encoding controller area network identifier(CAN ID) responsible party

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Improvement of the Electrical Properties of Silicone Rubber Doped with Nano-zirconia Grafted by Voltage Stabilizer 4

Improvement of the Electrical Properties of Silicone Rubber ...

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4th IEEE International Conference on Electrical Materials and Power Equipment, ICEMPE 2023

作者： Zhu, Xiaoqian Zhou, Yuanxiang Zhang, Yunxiao Chen, Ming Liu, Xuanhao Tsinghua University State Key Lab of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Beijing China Fuzhou University College of Electrical Engineering and Automation Fuzhou China Xiamen Power Supply Company State Grid Fujian Electric Power Company Xiamen China Xinjiang University The Wind Solar Storage Division State Key Laboratory of Control and Simulation of Power System and Generation Equipment School of Electrical Engineering Urumqi China

ISBN: (纸本)9798350334678

Enhancing the electrical performance of silicone rubber (SiR) for cable accessories is noteworthy as they are almost the weakest parts of the cable system. Two types of voltage stabilizers were grafted onto the surface of nano-zirconia (ZrO2) particles, and the grafted nanoparticles were added to the SiR. Measurements such as breakdown strength, and conductivity were carried out to evaluate the electrical performance of SiR nanocomposites. The electrical performance measurement results indicated that the breakdown strength of the nanocomposite SiR improved while the conductance current was reduced. The trap distribution showed that the improvement of the electrical performance of the nanocomposite SiR is mainly due to the decrease of the shallow trap density or the increase of the deep trap density. © 2023 IEEE.

关键词： Nanoparticles

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Mean policy-based proximal policy optimization for maneuvering decision in multi-UAV air combat

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Neural Computing and Applications 2024年第31期36卷 19667-19690页

作者： Zheng, Yifan Xin, Bin He, Bin Ding, Yulong Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China School of Automation Beijing Institute of Technology Beijing China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Autonomous maneuvering decision-making is a crucial technology for Unmanned Aerial Vehicles (UAVs) to take the air domination in modern unmanned warfare. With the advantage of balancing exploration and exploitation, as well as the immediacy of end-to-end output by combining with deep neural network, multi-agent reinforcement learning (MARL) has made remarkable achievements in multi-UAV autonomous air combat maneuvering decision-making (MUAAMD). However, the implementation of effective cooperative policy learning remains a challenging issue for MARL methods with centralized training decentralized execution (CTDE) paradigm. This paper proposes a MARL-based method to improve the performance of cooperation in MUAAMD. Firstly, considering the constraints of dynamic and limited perception for UAVs in the realistic air combat scenario, the MUAAMD problem is formulated based on partially observable Markov game (POMG) model. Secondly, a novel efficient MARL algorithm named the mean policy-based proximal policy optimization (MP3O) is introduced. Specifically, a joint policy optimization mechanism is constructed by estimating the policies of neighboring agents in group as a mean-field approximation while training, which enables both centralized evaluation and improvement of cooperative policy under the CTDE paradigm. Thirdly, by combining with three improvement techniques, a cooperative decision-making framework for MUAAMD based on MP3O is proposed. Empirically, results of simulations and comparative experiments validate the effectiveness of proposed method in promoting cooperative policy learning in resolving MUAAMD problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Decision making

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Modeling and Operating Point Analysis for Aquatic Translational Motion of a Cross-Medium Vehicle 42

Modeling and Operating Point Analysis for Aquatic Translatio...

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42nd Chinese control Conference, CCC 2023

作者： Dou, Minghao Liu, Xuchen Huang, Dongyue Wang, Biao Cui, Jinqiang Ren, Qinyuan Dou, Lihua Chen, Jie Chen, Ben M. Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin N.T. Hong Kong College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Peng Cheng Laboratory Guangdong Shenzhen China College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou China Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9789887581543

Modeling of cross-medium vehicles with complex shapes still requires a thorough investigation. This paper proposes a multi-method combination modeling approach to tackle such a problem. First-principle model is derived to determinate a model structure. Experiments are then set up to estimate parameters related to its rigid-body model and propulsion system. Computational fluid dynamics (CFD) is performed to calculate and identify coefficients related to surrounding fluid. Base on the model obtained, we systematically investigate the possible steady motion of the cross-medium vehicle and analyze their related performance. Results are instrumental for designing controllers for the vehicle to perform autonoumous missions. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Computational fluid dynamics

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