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作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Institute of Systems Engineering Macau University of Science and Technology SARTaipa 999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey School of Electrical Engineering Dalian University of Technology Dalian China

The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network-independent step sizes and an event-triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network-independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency. © 2024 John Wiley & Sons Ltd.

关键词： Cost functions

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Gradient Projection Differential Neural Solution for Quadratic Optimization with Quadratic Constraints: An ACP Perspective

Gradient Projection Differential Neural Solution for Quadrat...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Liufu, Ying Liu, Mei Jin, Long Wang, Feiyue School of Information Science and Engineering Lanzhou University Lanzhou China College of Computer Qinghai Normal University Xining China Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350337020

In recent years, quadratic optimizations have become increasingly popular in engineering. However, conventional methods that investigate this problem from the perspective of a canonical form with linear constraints are not effective in dealing with the significant challenges posed by quadratic constraints in practice. This paper proposes a solution framework for the quadratic optimization with quadratic constraints (QOQC) based on innovative artificial societies, computational experiments, and parallel execution (ACP) framework. Then, a gradient projection differential neural solution (GPDNS) is proposed to address this. To illustrate the effectiveness of the GPDNS model in solving the QOQC system, numerical simulations are provided. Overall, this paper presents the potential of innovative approaches like the ACP framework to enhance our capabilities in addressing challenging optimization systems. © 2023 IEEE.

关键词： ACP constraints differential neural solution numerical simulation Quadratic optimization with quadratic

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RoboFold: A platform for Protein-folding Inspired Robot Swarms ∗ 5

RoboFold: A platform for Protein-folding Inspired Robot Swar...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhang, Feng Gu, Yuping Yuan, Shuai Yang, Yongliang Shenyang Jianzhu University School of Electrical and Control Engineering Liaoning Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110016 China Shenyang Jianzhu University School of Computer Science and Engineering School of Electrical and Control Engineering Liaoning Shenyang110168 China

ISBN: (纸本)9798350307320

Natural swarms have inspired various controlling algorithms for swarm robotics, while few of them were programmed in a similar way as the natural swarms. Defining a reliable programming method is still a daunting challenge in swarm robotics. In nature, the information of 3D protein structure is programmed in the sequence of amino acids of the protein. To mimic the programming method of protein folding, we present a platform for investigating protein-folding inspired programming method for robot swarms. This platform includes three parts: a vibration machine, protein-like robot sequences, and recording devices. The preliminary results presented here demonstrated the effectiveness of this platform and the feasibility of protein-folding inspired programming method for robot swarms. © 2023 IEEE.

关键词： Protein folding

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Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

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engineering 2023年第1期20卷 77-95页

作者： Jun Liu Kangli Dong Yi Sun Ioannis Kakkos Fan Huang Guozheng Wang Peng Qi Xing Chen Delin Zhang Anastasios Bezerianos Yu Sun Key Laboratory for Biomedical Engineering of Ministry of Education of China Zhejiang UniversityHangzhou 310007China Department of Neurology Sir Run Run Shaw HospitalSchool of MedicineZhejiang UniversityHangzhou 310020China School of Electrical and Computer Engineering National Technical University of AthensAthens 15780Greece Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai 200092China Department of Anesthesiology The First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003China The N1 Institute for Health Center for Life SciencesNational University of SingaporeSingapore 117456Singapore

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

关键词： Anesthesia Brain network Connectivity Graph theoretical analysis Clinical monitoring system

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Toward parallel intelligence: An interdisciplinary solution for complex systems

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The Innovation 2023年第6期4卷 152-164页

作者： Yong Zhao Zhengqiu Zhu Bin Chen Sihang Qiu Jincai Huang Xin Lu Weiyi Yang Chuan Ai Kuihua Huang Cheng He Yucheng Jin Zhong Liu Fei-Yue Wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Hunan Institute of Advanced Technology Changsha 410073China Shanghai Institute of Infectious Disease and Biosecurity Shanghai 200032China Institute of Epidemiology Helmholtz Zentrum Minchen-German Research Center for Environmental Health(GmbH)NeuherbergGermany Department of Computer Science Hong Kong Baptist UniversityHong KongChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling,analysis,management,and *** meet these demands,the parallel systems method rooted in the artificial systems,computational experiments,and parallel execution(ACP)approach has been *** method cultivates a cycle termed parallel intelligence,which iteratively creates data,acquires knowledge,and refines the actual *** the past two decades,the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines,offering vers atile interdisciplinary solutions for complex systems across diverse *** review explores the origins and fundamental concepts of the parallel systems method,showcasing its accomplishments as a diverse array of parallel technologies and applica-tions while also prognosticating potential *** posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.

关键词： execution artificial offering

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Character Recognition of Vedic Sanskrit using Deep Learning Algorithms 3

Character Recognition of Vedic Sanskrit using Deep Learning ...

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Pandey, Mrinal Pandey, Rashmikiran Nazarov, Alexey Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781665462006

One of the applications of deep learning is deciphering the unscripted text over the walls and pillars of historical monuments is the major source of information extraction. This information gives us an idea about the art and the culture of that respective era. In this paper we proposed a model for digital recognition of textual characters written in Sanskrit language using the 4-fold Convolutional Neural Network (CNN) architecture. At the first stage we remove the noise and subsequently segmentation of characters from the input image is performed, later we convert the image into binary image format by the help of image processing techniques. The research is performed over the Devanagari script which was developed during 1st to the 4th century CE and is common to many languages from the Indian subcontinent. © 2022 IEEE.

关键词： Character recognition

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Modular Model Reduction of Interconnected Systems: A Top-Down Approach

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IFAC-PapersOnLine 2023年第2期56卷 4246-4251页

作者： Lars A.L. Janssen Bart Besselink Rob H.B. Fey Nathan van de Wouw Dynamics and Control group Department of Mechanical Engineering Eindhoven University of Technology Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen

Complex systems often involve interconnected subsystem models developed by multi-disciplinary teams. To simulate and control such systems, a reduced-order model of the interconnected system is required. In the scope of this paper, we pursue this goal by subsystem reduction to warrant modularity of the reduction approach. However, reducing subsystem models affects not only the subsystems accuracy, but also the interconnected model accuracy, making it difficult to predict a priori the accuracy impact of subsystem reduction. To address this challenge, we introduce a top-down approach using mathematical tools from robust performance analysis, enabling the translation of accuracy requirements from the interconnected model to the subsystem level. This enables independent subsystem reduction while ensuring the desired accuracy of the interconnected model. We demonstrate the effectiveness of our approach through a structural dynamics case study.

关键词： Complex systems Model reduction control of interconnected systems Robust performance Top-down approach Error bounds

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Research on Two-Stage Optimization Approach for Community Integrated Energy System Planning 2

Research on Two-Stage Optimization Approach for Community In...

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2nd International Conference on computer, control and Robotics, ICCCR 2022

作者： Wen, Gangcheng Shi, Xin Wu, Zhiyue Wang, Tingting Luo, Bing School of Control and Computer Engineering North China Electric Power University Beijing China Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9781665466745

The community integrated energy system (CIES) has become a new energy utilization approach for users to meet multi-energy supply and demand. Taking a community in China as the research object, this paper develops a two-stage optimization approach for the CIES planning. The first stage is based on the improved whale optimization approach (WOA) to optimize the equipment type and capacity of the community energy station. It is a multi-objective planning optimization problem, and its purpose is to achieve the coordinated optimization of economic costs and environmental costs. As for the multiple Pareto frontier solutions obtained in the first stage, the mixed integer linear programming (MILP) is used to separately optimize the operation cost of each planning scheme. Case studies validate that the developed approach is practical and feasible for CIES planning. © 2022 IEEE.

关键词： Energy utilization

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Weighted Null Space Fitting (WNSF): A Link between The Prediction Error Method and Subspace Identification

arXiv

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arXiv 2024年

作者： He, Jiabao Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

— Subspace identification method (SIM) has been proven to be very useful and numerically robust for estimating state-space models. However, it is in general not believed to be as accurate as prediction error method (PEM). Conversely, PEM, although more accurate, comes with non-convex optimization problems and requires local non-linear optimization algorithms and good initialization points. This contribution proposes a weighted null space fitting (WNSF) method to identify a state-space model, combining some advantages of the two mainstream approaches aforementioned. It starts with the estimate of a nonparametric model using least-squares, and then the reduction to a state-space model in the observer canonical form is a multi-step least-squares procedure where each step consists of the solution of a quadratic optimization problem. Unlike SIM, which focuses on the range space of the extended observability matrix, WNSF estimates its null space, avoiding the need for singular value decomposition. Moreover, the statistically optimal weighting for the null space fitting problem is derived. It is conjectured that WNSF is asymptotically efficient, which is supported by a simulation study. Copyright © 2024, The Authors. All rights reserved.

关键词： State space methods

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PSRMER: Proactive Services Recommendation Driven-by Multimodal Emotion Recognition

PSRMER: Proactive Services Recommendation Driven-by Multimod...

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IEEE International Conference on Web Services (ICWS)

作者： Zhizhong Liu Guangyu Huang Dianhui Chu Yuhang Sun School of Computer and Control Engineering Yantai University Yantai China School of Computer Science and Technology Harbin Institute of Technology Weihai China

In the past three years, global COVID-19 pandemic not only impacted people’s physical health but also significantly affected their mental health, which resulting in rapid increase of psychological problems. Emotions are a common manifestation of psychological changes, and some services (such as music, video, or psychological counseling services) can help users to adjust their emotions in a timely manner, thus to avoid bringing extreme events (e.g., running away from home or committing suicide). Therefore, how to perceive users’ real-time emotions and then recommend the most appropriate services to users has become a challenge. To address this issue, this work proposes an approach for proactive services recommendation driven-by multimodal emotion recognition (named as PSRMER). Specifically, PSRMER first actively identifies a user’s emotion with a multimodal emotion recognition model based on BiGRU and Transformer; Then, considering the user’s emotion and preferences, PSRMER selects the optimal services based on an index-graph linking different emotions and various services; Finally, PSRMER proactively recommends the selected optimal service to the user. Extensive experiments have been conducted and the effectiveness of our proposed method have been proved. Moreover, the proposed method can also be used in smart education, smart transportation, smart elderly care and other modern industry fields.

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