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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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Integrated Power and Hydrogen Trading in Multimicrogrid Coupled with Offsite Hydrogen Refueling Stations 6

Integrated Power and Hydrogen Trading in Multimicrogrid Coup...

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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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Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

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China Communications 2024年第5期21卷 297-313页

作者： Ma Shuai Yang Lei Ding Wanying Li Hang Zhang Zhongdan Xu Jing Li Zongyan Xu Gang Li Shiyin Department of Strategic and Advanced Interdisciplinary Research Peng Cheng LaboratoryShenzhen 518055China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Data-driven Intelligent Information System Laboratory Shenzhen Research Institute of Big DataShenzhen 518172China Ocean College Zhejiang UniversityZhoushan 316021China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication *** other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental *** on such a platform,we first construct a real signal dataset with ten modulation ***,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature *** also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated ***,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other *** also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

关键词： AdaBoost DBN machine learning signal demodulation

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Distributed Real-time State Estimation for Combined Heat and Power systems

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Journal of Modern Power systems and Clean Energy 2021年第2期9卷 316-327页

作者： Tingting Zhang Wen Zhang Qi Zhao Yaxin Du Jian Chen Junbo Zhao the Key Laboratory of Power System Intelligent Dispatch and Control Ministry of EducationShandong UniversityJinan 250061China the Department of Electrical and Computer Engineering Mississippi State UniversityStarkvilleMS 39762USA

This paper proposes a distributed real-time state estimation(RTSE)method for the combined heat and power systems(CHPSs).First,a difference-based model for the heat system is established considering the dynamics of heat *** heat system model is further used along with the power system steady-state model for holistic CHPS state estimation.A cubature Kalman filter(CKF)-based RTSE is developed to deal with the system nonlinearity while integrating both the historical and present measurement ***,a multi-timescale asynchronous distributed computation scheme is designed to enhance the scalability of the proposed method for largescale *** distributed implementation requires only a small amount of information exchange and thus protects the privacy of different energy *** carried out on two CHPSs show that the proposed method can significantly improve the estimation efficiency of CHPS without loss of accuracy compared with other existing models and methods.

关键词： Combined heat and power system(CHPS) cubature Kalman filter(CKF) heat dynamics multi-time-scale asynchronous distributed scheme real-time state estimation(RTSE)

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Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid 4

Event-Triggered Online Learning Distributionally Robust Ener...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Li, Longyan Ning, Chao 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 Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study. © 2022 IEEE.

关键词： Ammonia

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Data-Driven Robust Optimization for Energy Chemical Processes under Uncertainties: A Review and Tutorial 4

Data-Driven Robust Optimization for Energy Chemical Processe...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Ning, Chao Li, Longyan Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In recent years, data-driven robust optimization (DDRO) is becoming a popular and effective paradigm to address the challenging issue of uncertainty in energy chemical processes. This paper provides an overview of recent advances in the field of DDRO, with a primary focus on its methods and applications in process industries. Firstly, a brief introduction to various robust optimization model formulations and solution algorithms is presented. Secondly, research achievements of machine-learning enabled uncertainty sets, the corresponding DDRO, and variant techniques are summarized and analyzed in a systematic manner. Additionally, tutorial-like numerical examples are used to illustrate merits of DDRO compared with conventional robust optimization. Finally, fruitful applications of DDRO in energy chemical processes are encapsulated and categorized from domain perspectives. © 2022 IEEE.

关键词： Optimization

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Resource Allocation for Coupled Slices in Edge-Assisted Industrial IoT systems

Resource Allocation for Coupled Slices in Edge-Assisted Indu...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wen, Xiaojing Chen, Cailian Ren, Cheng Lyu, Ling Guan, Xinping 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 Shanghai200240 China Dalian Maritime University School of Information Science and Technology Dalian116026 China

ISBN: (纸本)9798350345384

Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for collecting data from heterogeneous sensors and meeting diverse Quality of Service (QoS) requirements. However, existing research on network slicing optimization mainly focuses on inter-slice resource constraints based on slice heterogeneity, neglecting slice coupling feature. In this regard, this paper proposes a novel network slicing framework for edge-assisted industrial IoT systems. The framework includes a slice coupling model and network resource allocation (transmission and computing) to address the problem of improving estimation performance while ensuring system convergence. The slice coupling model is primarily composed of the estimation error covariance of two slices, calculated using the Age of Task (AoT) as a bridge, which reflects the accuracy and freshness of the estimation. To reduce the complexity of the direct solution, the optimization problem is scaled down and decoupled. The iterative Particle Swarm Optimization (PSO) algorithm is proposed to determine the allocation rates of inter-slice transmission and computing resources. Simulation results demonstrate that the proposed algorithm achieves a flexible balance under heterogeneous estimation weights and exhibits near-optimal performance. © 2023 IEEE.

关键词： Resource allocation

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A Low-complexity Decision Variables Classification Brainstorm Optimization Algorithm

A Low-complexity Decision Variables Classification Brainstor...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Li, Qiyue Qiu, Lipeng Zhou, Aoran Wu, Yali Wang, Ziren Department of Information and Control Engineering Xi'An University of Technolog Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd. Xi'an710016 China

In the complex optimization scenarios of real life, we often need to not only weigh the conflicts between multi-objectives but also face the challenges brought by large-scale decision variables. When the scale of decision variables increases sharply, the search space will expand exponentially, which makes it difficult for the algorithm to traverse the whole search space under limited resources, so that the number of local optimal solutions increases sharply. The so-called 'dimension disaster' appears. To effectively deal with this problem, this paper proposes a large-scale multi-objective brainstorm optimization algorithm (LMaOBSO) based on low complexity decision variable classification method and improved penalty boundary intersection strategy. The algorithm uses a low-complexity tree sorting variable classification method to divide the decision variables into converges and diversity variables quickly. At the same time, the convergent variables are optimized by the improved penalty boundary crossover strategy to solve the problems existing in large-scale optimization problems. © Published under licence by IOP Publishing Ltd.

关键词： Disasters

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Flow and thermal modeling of liquid metal in expanded microchannel heat sink

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Frontiers in Energy 2023年第6期17卷 796-810页

作者： Mingkuan ZHANG Xudong ZHANG Luna GUO Xuan LI Wei RAO Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin 300384China Key Laboratory for Thermal Science and Power Engineering of the Ministry of Education Department of Engineering MechanicsTsinghua UniversityBeijing 100084China School of Mechanical Engineering Tianjin University of CommerceTianjin 300134China Institute of Refrigeration and Cryogenics Shanghai Jiao Tong UniversityShanghai 200240China Beijing Key Laboratory of CryoBiomedical Engineering and Key Laboratory of Cryogenics Beijing 100190China

Liquid metal-based microchannel heat sinks (MCHSs) suffer from the low heat capacity of coolant, resulting in an excessive temperature rise of coolant and heat sink when dealing with high-power heat dissipation. In this paper, it was found that expanded space at the top of fins could distribute the heat inside microchannels, reducing the temperature rise of coolant and heat sink. The orthogonal experiments revealed that expanding the top space of channels yielded similar temperature reductions to changing the channel width. The flow and thermal modeling of expanded microchannel heat sink (E-MCHS) were analyzed by both using the 3-dimensional (3D) numerical simulation and the 1-dimensional (1D) thermal resistance model. The fin efficiency of E-MCHS was derived to improve the accuracy of the 1D thermal resistance model. The heat conduction of liquid metal in Z direction and the heat convection between the top surface of fins and the liquid metal could reduce the total thermal resistance (Rt). The above process was effective for microchannels with low channel aspect ratio, low mean velocity (Um) or long heat sink length. The maximum thermal resistance reduction in the example of this paper reached 36.0%. The expanded space endowed the heat sink with lower pressure, which might further reduce the pumping power (P). This rule was feasible both when fins were truncated (h_(2) < 0, h_(2) is the height of expanded channel for E-MCHS) and when over plate was raised (h_(2) > 0).

关键词： liquid metal cooling heat sink expanded microchannel flow and thermal modeling

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CPL-SLAM: Centralized Collaborative Multi-Robot Visual-Inertial SLAM Using Point-and-Line Features

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IEEE Internet of Things Journal 2025年第12期12卷 21866-21875页

作者： Liu, Xin Wen, Shuhuan Liu, Huaping Richard Yu, F. Yanshan University Engineering Research Center The Ministry of Education for Intelligent Control System and Intelligent Equipment Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Shenzhen University College of Computer Science and Software Engineering China Carleton University Canada

Traditional visual-inertial SLAM (Simultaneous Localization and Mapping) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intra-robot and inter-robot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance. © 2014 IEEE.

关键词： SLAM robotics

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