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A new Fermatean fuzzy Spearman-like correlation coefficient and its application in evaluating insecurity problem via multi-criteria decision-making approach

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Heliyon 2024年第22期10卷 e40403页

作者： Ejegwa, Paul Augustine Kausar, Nasreen Aydin, Nezir Feng, Yuming Olanrewaju, Oludolapo Akanni Department of Mathematics Joseph Sarwuan Tarka University Makurdi Nigeria Department of Mathematics Faculty of Arts and Science Yildiz Technical University Esenler Istanbul 34220 Turkey College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Industrial Engineering Yildiz Technical University Besiktas Istanbul 34349 Turkey Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing 404100 China Department of Industrial Engineering Durban University of Technology Durban 4001 South Africa

The problem of insecurity is a global crisis with adverse effects on lives and properties. Fermatean fuzzy correlation coefficient is a dependable method for handling imprecision, which is the main bottleneck of insecurity assessment. A number of Fermatean fuzzy correlation coefficient methods have been developed. Based on Spearman's correlation coefficient, an innovative Fermatean fuzzy correlation coefficient method is built to enhance trustworthy insecurity assessment. The existing Fermatean fuzzy correlation coefficient methods are evaluated, and their shortcomings are identified in order to validate the construction of a new Fermatean fuzzy correlation coefficient method. The drawbacks of the existing methods lead us into building a new Fermatean fuzzy correlation coefficient method by using the Spearman's correlation coefficient approach, which has the potential of overcoming the drawbacks of the existing Fermatean fuzzy correlation coefficient methods. In addition, some theoretical findings are provided to support the strength of the novel Fermatean fuzzy correlation coefficient method and it is shown that the new method satisfies the Fermatean fuzzy correlation coefficient requirements. Furthermore, the novel Fermatean fuzzy correlation coefficient method is applied to assess the insecurity situation in the North-Central Region of Nigeria to furnish intended tourists with relevant travel advice. To demonstrate the inherent significance of the novel Fermatean fuzzy correlation coefficient method, we compare its effectiveness to that of the extant Fermatean fuzzy correlation coefficient methods. The results of the comparison show the superiority of the novel Fermatean fuzzy correlation coefficient method over the existing ones in terms of reliability, consistency, precision and compliance with the Fermatean fuzzy correlation coefficient axioms. Ultimately, it is discovered that the new method can effectively address hesitations related to insecurity assessment

关键词： Correlation coefficient Fermatean fuzzy sets Insecurity assessment Multi-criteria decision-making North central region of Nigeria Security crises

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Optimal Fault Detection Observer Design using Excluding Degree

Optimal Fault Detection Observer Design using Excluding Degr...

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IEEE Conference on Decision and control

作者： Feng Xu Yiming Wan Ye Wang Division of Information Science and Technology Shenzhen International Graduate School Tsinghua University Shenzhen P.R. China School of Artificial Intelligence and Automation Huazhong University of Science and Technology and Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan P.R. China Department of Electrical and Electronic Engineering The University of Melbourne Parkville VIC Australia

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a novel optimal gain design method for robust fault detection using set-valued observers. First, a new fault detection performance specification is proposed for set-valued observers based on a new notion named the excluding degree of the origin from a zonotope. Second, a fractional programming problem is formulated to describe fault detection optimality of set-valued observers. Third, optimal fault detection gains are obtained by solving the fractional programming problem. Finally, numerical examples are used to illustrate the effectiveness of the proposed method.

关键词： Fault detection Design methodology Observers Programming Computational complexity Optimization Numerical stability

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Communication and control Co-Design for Heterogeneous industrial IoT: A Logic-Based Stochastic Switched System Approach

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IEEE Journal on Selected Areas in Communications 2025年

作者： Wang, Shuling Zhu, Shanying Chen, Cailian Shen, Fei Zhang, Weihai Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Chinese Academy of Sciences Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Shanghai200050 China Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao266590 China

With the development of Industry 4.0, mobile agents are deployed to coordinate with multi-loop control systems to perform manufacturing tasks, leading to heterogeneous Internet of Things (IoT). Due to shadow fading induced by the movement of mobile agents, the wireless channel closing the control loops is inherently unreliable, which can compromise the control system performance. This paper addresses the co-design problem of transmission scheduling and agents' movement to ensure both control performance and energy efficiency. A generalized cyber-physical-agent framework is proposed to capture the coupling between IoT systems and a mobile agent through a state-dependent fading channel. Moreover, the movement of the mobile agent among areas exhibiting different levels of shadow effects is modeled by Markov decision process. To address such heterogeneous dynamics, the co-design problem is formulated into the optimization of a logic-based stochastic switched system by utilizing the semi-tensor product technique. Based on state mergence and H-representation methods, a tractable and effective algorithm is then proposed to design co-design policies, minimizing the average joint cost of communication and control. A parallel scheme is further developed to alleviate the computation burden of the algorithm. Theoretical guarantees are provided to ensure control system performance. Finally, simulation results are given to demonstrate effectiveness of the proposed method. © 1983-2012 IEEE.

关键词： Fading channels

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A Vector control-based Design of Multi-port Interline DC Power Flow controller for HVDC System

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Electric Power Systems Research 2025年 248卷

作者： Khan, Muhammad Qasim Khan, Muhammad Mansoor Nawaz, Arshad Wang, Haibing Chengmin, Wang School of Electrical Engineering and Key Laboratory of Control of Power Transmission and Transformation Ministry of Education of China Shanghai Jiao Tong University Shanghai China School of Automation and Key Laboratory of System Control Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China University of Shanghai for Science and Technology Shanghai China

The Interline DC Power Flow controller (IDCPFC) represents a significant advancement in power distribution and control within Multi-Terminal High Voltage DC (MT-HVDC) systems. Traditional IDCPFC control without external voltage support suffers from coupling issues due to coupled dynamics, which limits transient response in multi-legged converter-based IDCPFC systems. This paper introduces a novel approach that employs vector-based decomposition to address this issue by decoupling current regulation from DC-link voltage regulation, thereby efficiently managing DC power flow across multiple transmission lines in the grid. Furthermore, the concept of zero-sequence control for IDCPFC is introduced. The incorporation of the zero-sequence algorithm increases modulation depth by up to 25%, enhancing DC link voltage utilization and improving system efficiency by reducing switching losses by up to 12%. The proposed method undergoes a detailed analysis encompassing network topology, operational principles, and decoupled control strategies. Simulations and experimental results validate the method's effectiveness under various grid scenarios. © 2025

关键词： Decouple control Interline DC Power Flow controller Multi-port HVDC Zero-sequence Algorithm

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Topology Design for Edge Sensing and control: A Dynamic Observability Guaranteed Method

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IEEE transactions on cybernetics 2025年 PP卷 PP页

作者： Tiankai Jin Cailian Chen Zhiduo Ji Yehan Ma Xinping Guan Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory for System Control and Information Processing Shanghai200240 China Shanghai Key Laboratory for Perception and Control in Industrial Network Systems Shanghai200240 China Chinese Aeronautical Radio Electronics Research Institute State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Shanghai200241 China

It is one of the most essential processes in the industrial cyber-physical system (ICPS) that multiple edge computing nodes (ECNs) collect field sensor information and cooperate for sensing and control. The exchange of sensing information on the edge side is critical for these ECNs that serve as multiple edge estimators and one edge controller. However, the limited transmission resources and the diverse performance demands of ECNs for sensing and control make it challenging to design the edge network topology delicately. For this issue, a novel dynamic observability (DO) condition is proposed to balance sensing-control performance and transmission cost under various demand settings. Based on the quantitative analysis of the relationship between overall transmission cost and each ECN's effective observability, DO gives the criterion for desirable network topologies with spatio-temporal dynamics. Then for the given performance demands, a dynamic observability guaranteed method (DOGM) is proposed to determine the network topology by triggering proper sensing links. In this way, the set of triggered sensing links may vary in a dynamic manner to satisfy the DO condition, and the overall performance of sensing and control is theoretically guaranteed. Finally, the comprehensive advantages of DOGM are demonstrated by the simulation study in the hot rolling laminar cooling process.

关键词： Observability

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Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

arXiv

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Ge, Leijiao Lou, Suhua Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin 132012 China The Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin 300072 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Evolution of opinion dynamics with eccentric agents ⁎

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IFAC-PapersOnLine 2020年第2期53卷 11012-11017页

作者： Qi Zhang Lin Wang Xiaofan Wang Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai China

More recently, there has been a surge of studies that seek mechanisms of the opinion evolution. While many studies have been dedicated to this field, much less attention has been paid to the joint influence of diverse agents on the opinion evolution. In this paper, we proposed an opinion dynamic model based on the Deffuant Weisbuch(DW) model with the existence of eccentric agents. The eccentric agent will change its opinion if the eccentric agent is selected and the opinion difference between two selected agents is beyond the bounded confidence. Previous studies have demonstrated that consensus usually cannot be achieved in the DW model. However, our study suggests that the existence of a single eccentric agent is able to promote consensus in numerical simulations, regardless of any bounded confidence and initial opinion distribution. We further proved that the DW model with the single eccentric agent achieves quasi-consensus. The equilibrium of the system was also proposed. Lastly, we analyzed the final opinion distribution and convergence time with varying bounded confidence and convergence parameters.

关键词： Social network Opinion dynamics Bounded confidence the Deffuant Weisbuch model Quasi-Consensus

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Artificial intelligence-based methods for renewable power system operation

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年第3期1卷 163-179页

作者： Yuanzheng Li Fei Hu Juntao Duan Yong Zhao Zhigang Zeng Yizhou Ding Shangyang He Guanghui Wen Hua Geng Zhengguang Wu Hoay Beng Gooi Chenghui Zhang Shengwei Mei Key Laboratory of lmage Information Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China Department of Systems Science School of Mathematics Southeast University Nanjing China Department of Automation Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore School of Control Science and Engineering Shandong University Jinan China Department of Electrical Engineering Tsinghua University Beijing China

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity.

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Speed Planning Using Bézier Polynomials with Trapezoidal Corridors

arXiv

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arXiv 2021年

作者： Li, Jialun Xie, Xiaojia Ma, Hengbo Liu, Xiao He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Technology Inc. Beijing China University of California BerkeleyCA94720 United States

—To generate safe and real-time trajectories for an autonomous vehicle in dynamic environments, path and speed decoupled planning methods are often considered. This paper studies speed planning, which mainly deals with dynamic obstacle avoidance given the planning path. The main challenges lie in the decisions in non-convex space and the trade-off between safety, comfort and efficiency performances. This work uses dynamic programming to search heuristic waypoints on the S-T graph and to construct convex feasible spaces. Further, a piecewise Bezier polynomials optimization approach with trapezoidal corridors is presented, which theoretically guarantees the safety and optimality of the trajectory. The simulations verify the effectiveness of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Dynamic programming

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