Pub Date : 2024-07-25DOI: 10.1109/TSMC.2024.3427345
Yan-Hui Lin;Gang-Hui Li
Fault diagnosis plays an important role in guiding maintenance actions and prevent safety hazards. With the development of sensor and computer technology, deep learning (DL)-based fault diagnosis methods have been substantially developed. However, the inability to reliably represent and quantify uncertainties associated with the diagnostic results greatly hinders their industrial applicability. In this article, an uncertainty-aware fault diagnosis framework based on the Bayesian DL is proposed considering uncertainty quantification and calibration. To achieve explainable representations of different types of uncertainties, aleatoric uncertainty, epistemic uncertainty, and distributional uncertainty, which stem from the noise inherent in the observations, lack of knowledge, and domain shift, respectively, are jointly characterized for uncertainty quantification. Besides, to improve the quantification accuracy and obtain trustworthy diagnostic results to support subsequent maintenance, a novel calibration loss is proposed for the uncertainty calibration. The proposed method is applied to the two different bearing datasets to demonstrate its effectiveness in providing both the accurate diagnostic results and calibrated uncertainty quantification.
{"title":"Uncertainty-Aware Fault Diagnosis Under Calibration","authors":"Yan-Hui Lin;Gang-Hui Li","doi":"10.1109/TSMC.2024.3427345","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3427345","url":null,"abstract":"Fault diagnosis plays an important role in guiding maintenance actions and prevent safety hazards. With the development of sensor and computer technology, deep learning (DL)-based fault diagnosis methods have been substantially developed. However, the inability to reliably represent and quantify uncertainties associated with the diagnostic results greatly hinders their industrial applicability. In this article, an uncertainty-aware fault diagnosis framework based on the Bayesian DL is proposed considering uncertainty quantification and calibration. To achieve explainable representations of different types of uncertainties, aleatoric uncertainty, epistemic uncertainty, and distributional uncertainty, which stem from the noise inherent in the observations, lack of knowledge, and domain shift, respectively, are jointly characterized for uncertainty quantification. Besides, to improve the quantification accuracy and obtain trustworthy diagnostic results to support subsequent maintenance, a novel calibration loss is proposed for the uncertainty calibration. The proposed method is applied to the two different bearing datasets to demonstrate its effectiveness in providing both the accurate diagnostic results and calibrated uncertainty quantification.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1109/TSMC.2024.3427646
Wenhai Qi;Mingxuan Sha;Ju H. Park;Zheng-Guang Wu;Huaicheng Yan
This article is concerned with the observer-based asynchronous control for discrete hidden semi-Markov switching power systems under random denial-of-service (DoS) attacks. Considering the mismatched behavior between the controller and the system, the designed controller based on the observer model runs asynchronously with the system. The hidden stochastic switching model is introduced to characterize this mismatched behavior. Due to the difficulty in obtaining complete information about the semi-Markov kernel (SMK) in practice, the elements in the SMK of the underlying system associated with the hidden mode are considered to be incompletely known. Next, regarding the random DoS attacks and incomplete SMK, the conditions on the existence of the asynchronous controller based on the observer model are proposed by employing the stochastic Lyapunov function, and the closed-loop system is guaranteed to be mean-square stable. Finally, the effectiveness of the proposed scheme is validated through an example.
本文关注随机拒绝服务（DoS）攻击下基于观测器的离散隐式半马尔可夫开关电源系统异步控制。考虑到控制器与系统之间的不匹配行为，基于观测器模型设计的控制器与系统异步运行。为描述这种不匹配行为，引入了隐藏随机切换模型。由于在实践中很难获得有关半马尔可夫核（SMK）的完整信息，与隐藏模式相关的底层系统 SMK 中的元素被认为是不完全已知的。接下来，针对随机 DoS 攻击和不完整 SMK，利用随机 Lyapunov 函数提出了基于观测器模型的异步控制器的存在条件，并保证了闭环系统的均方稳定。最后，通过实例验证了所提方案的有效性。
{"title":"Observer-Based Asynchronous Control of Discrete-Time Semi-Markov Switching Power Systems Under DoS Attacks","authors":"Wenhai Qi;Mingxuan Sha;Ju H. Park;Zheng-Guang Wu;Huaicheng Yan","doi":"10.1109/TSMC.2024.3427646","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3427646","url":null,"abstract":"This article is concerned with the observer-based asynchronous control for discrete hidden semi-Markov switching power systems under random denial-of-service (DoS) attacks. Considering the mismatched behavior between the controller and the system, the designed controller based on the observer model runs asynchronously with the system. The hidden stochastic switching model is introduced to characterize this mismatched behavior. Due to the difficulty in obtaining complete information about the semi-Markov kernel (SMK) in practice, the elements in the SMK of the underlying system associated with the hidden mode are considered to be incompletely known. Next, regarding the random DoS attacks and incomplete SMK, the conditions on the existence of the asynchronous controller based on the observer model are proposed by employing the stochastic Lyapunov function, and the closed-loop system is guaranteed to be mean-square stable. Finally, the effectiveness of the proposed scheme is validated through an example.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1109/TSMC.2024.3420882
Zhengtai Xie;Yu Zheng;Long Jin
For the image-based visual servoing (IBVS) of a manipulator with an unknown structure, the unavailability of the robot Jacobian matrix impedes the accurate control of the manipulator. To solve this issue, this article proposes a data-driven IBVS (DDIBVS) scheme combining model-free learning, matrix inversion estimation, feature tracking, and joint limits. On the one hand, a data-driven learning algorithm is designed, which enables an estimated end-effector velocity to approach the real one and outputs an estimated robot Jacobian matrix. On the other hand, we consider the desired velocity information of the visual feature to improve the tracking accuracy and design an auxiliary parameter to estimate the inversion operation and address the singularity problem. On this basis, a neural dynamic controller (NDC) is developed, which possesses learning, estimation, and control capabilities. Subsequently, the effectiveness, practicability, and superiority of the proposed method are evaluated through simulations and experiments conducted on a 7-degree-of-freedom (DOF) manipulator for visual servoing tasks.
{"title":"A Data-Driven Image-Based Visual Servoing Scheme for Redundant Manipulators With Unknown Structure and Singularity Solution","authors":"Zhengtai Xie;Yu Zheng;Long Jin","doi":"10.1109/TSMC.2024.3420882","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3420882","url":null,"abstract":"For the image-based visual servoing (IBVS) of a manipulator with an unknown structure, the unavailability of the robot Jacobian matrix impedes the accurate control of the manipulator. To solve this issue, this article proposes a data-driven IBVS (DDIBVS) scheme combining model-free learning, matrix inversion estimation, feature tracking, and joint limits. On the one hand, a data-driven learning algorithm is designed, which enables an estimated end-effector velocity to approach the real one and outputs an estimated robot Jacobian matrix. On the other hand, we consider the desired velocity information of the visual feature to improve the tracking accuracy and design an auxiliary parameter to estimate the inversion operation and address the singularity problem. On this basis, a neural dynamic controller (NDC) is developed, which possesses learning, estimation, and control capabilities. Subsequently, the effectiveness, practicability, and superiority of the proposed method are evaluated through simulations and experiments conducted on a 7-degree-of-freedom (DOF) manipulator for visual servoing tasks.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1109/TSMC.2024.3421658
Huaguang Zhang;Lulu Zhang;Jiayue Sun;Tianbiao Wang
This article investigates the optimal control problem for the discrete-time (DT) nonlinear semi-Markovian jump systems (s-MJSs) that possess unknown dynamics. The study uses the semi-Markovian kernel approach to address the problem of mode-switching in these systems. This approach employs the transition probability and the sojourn-time distribution function to jointly determine the transitions between different modes. Then, with a neural network (NN) identifier, the demand for accurate information on the system dynamics is eliminated, and an optimal control method for the nonlinear s-MJSs is utilized to solve the Hamilton-Jacobi–Bellman equation (HJBE) built upon adaptive dynamic programming methodology. Additionally, a detailed analysis of the convergence of a value iteration-based algorithm, which solves the optimal control issue for the DT s-MJSs, is thoroughly discussed. Furthermore, an actor-critic NN is trained to attain an estimated solution to the relevant HJBE. Finally, to validate the designed approach, two simulations are performed to prove its effectiveness.
{"title":"Optimal Control for Unknown Nonlinear System With Semi-Markovian Jump Parameters via Adaptive Dynamic Programming","authors":"Huaguang Zhang;Lulu Zhang;Jiayue Sun;Tianbiao Wang","doi":"10.1109/TSMC.2024.3421658","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3421658","url":null,"abstract":"This article investigates the optimal control problem for the discrete-time (DT) nonlinear semi-Markovian jump systems (s-MJSs) that possess unknown dynamics. The study uses the semi-Markovian kernel approach to address the problem of mode-switching in these systems. This approach employs the transition probability and the sojourn-time distribution function to jointly determine the transitions between different modes. Then, with a neural network (NN) identifier, the demand for accurate information on the system dynamics is eliminated, and an optimal control method for the nonlinear s-MJSs is utilized to solve the Hamilton-Jacobi–Bellman equation (HJBE) built upon adaptive dynamic programming methodology. Additionally, a detailed analysis of the convergence of a value iteration-based algorithm, which solves the optimal control issue for the DT s-MJSs, is thoroughly discussed. Furthermore, an actor-critic NN is trained to attain an estimated solution to the relevant HJBE. Finally, to validate the designed approach, two simulations are performed to prove its effectiveness.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1109/TSMC.2024.3420446
Qingsong Liu;Guangjie Wang;Li Chai;Wenjun Mei
The vaccination has played a significant role in government departments to control the spread of infectious diseases. Therefore, it is interesting to theoretically analyse the impact of vaccination on the disease spreading. In this article, we propose a discrete-time epidemic-willingness dynamics model to analyse the influence of vaccine willingness on epidemic spreading. Sufficient conditions are provided to guarantee that the proportion of the infected population exponentially converges to zero. The explicit relationship between the trend of epidemic spreading and the willingness-based reproduction number is presented. Based on the real data from a survey conducted on a sample of Italian population, we employ the proposed epidemic-willingness dynamics model to reproduce the social phenomenon that increasing the willingness to vaccinate can reduce and delay the maximum proportion of infected communities. Additionally, simulation experiments validate the effectiveness of the proposed epidemic-willingness dynamics model by utilizing the real data of COVID-19 infections from 28 February to 31 May 2022 in Shanghai. It is shown that the higher the level of infection, the greater the willingness to vaccinate. Moreover, we find that the willingness-based reproduction number is not monotonically decreasing and differs from the classical reproduction number.
{"title":"The Influence of Vaccine Willingness on Epidemic Spreading in Social Networks","authors":"Qingsong Liu;Guangjie Wang;Li Chai;Wenjun Mei","doi":"10.1109/TSMC.2024.3420446","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3420446","url":null,"abstract":"The vaccination has played a significant role in government departments to control the spread of infectious diseases. Therefore, it is interesting to theoretically analyse the impact of vaccination on the disease spreading. In this article, we propose a discrete-time epidemic-willingness dynamics model to analyse the influence of vaccine willingness on epidemic spreading. Sufficient conditions are provided to guarantee that the proportion of the infected population exponentially converges to zero. The explicit relationship between the trend of epidemic spreading and the willingness-based reproduction number is presented. Based on the real data from a survey conducted on a sample of Italian population, we employ the proposed epidemic-willingness dynamics model to reproduce the social phenomenon that increasing the willingness to vaccinate can reduce and delay the maximum proportion of infected communities. Additionally, simulation experiments validate the effectiveness of the proposed epidemic-willingness dynamics model by utilizing the real data of COVID-19 infections from 28 February to 31 May 2022 in Shanghai. It is shown that the higher the level of infection, the greater the willingness to vaccinate. Moreover, we find that the willingness-based reproduction number is not monotonically decreasing and differs from the classical reproduction number.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1109/TSMC.2024.3418411
Pritpal Singh;Yo-Ping Huang
Conventional soft clustering algorithms perform well on linearly distributed features, but their performance degrades on nonlinearly distributed features in high-dimensional space. In this study, a novel soft clustering algorithm, the ambiguous kernel distance clustering (AKDC) algorithm, is presented. This algorithm is developed by applying ambiguous set theory and the Gaussian kernel function. The ambiguous set theory defines the ambiguities inherent in each feature with four membership values: 1) true; 2) false; 3) true-ambiguous; and 4) false-ambiguous. The degree of membership values here forms a low-dimensional feature space that is not linearly distributed. Therefore, these nonlinearly distributed membership values are mapped into a high-dimensional feature space using the Gaussian kernel function. This study focuses on performing cluster analysis of computerized tomography scans of COVID-19 (CTSC-19) cases using AKDC. COVID-19, recognized as one of the most life-threatening diseases of this century, is highly contagious, and early diagnosis may prevent one-to-one transmission. Extensive empirical studies have been conducted with different types of CTSC-19 to demonstrate its effectiveness against existing kernel-based clustering and nonkernel-based clustering algorithms, namely mercer kernel fuzzy c-mean (MKFCM), kernel generalized FCM (KGFCM), kernel intuitionistic fuzzy entropy c-means (KIFECMs), morphological reconstruction and membership filtering clustering (FRFCM), and intuitionistic FCM based on membership information transferring and similarity measurements (IFCM-MS). The effectiveness of the proposed algorithm compared to the existing algorithms is evaluated using standard statistical metrics, such as dice index (DI), Jaccard index (JI), structural similarity index (SI), and correlation coefficient (CC). The empirical results show that AKDC is more effective than existing algorithms based on DI, JI, SI, and CC.
{"title":"AKDC: Ambiguous Kernel Distance Clustering Algorithm for COVID-19 CT Scans Analysis","authors":"Pritpal Singh;Yo-Ping Huang","doi":"10.1109/TSMC.2024.3418411","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3418411","url":null,"abstract":"Conventional soft clustering algorithms perform well on linearly distributed features, but their performance degrades on nonlinearly distributed features in high-dimensional space. In this study, a novel soft clustering algorithm, the ambiguous kernel distance clustering (AKDC) algorithm, is presented. This algorithm is developed by applying ambiguous set theory and the Gaussian kernel function. The ambiguous set theory defines the ambiguities inherent in each feature with four membership values: 1) true; 2) false; 3) true-ambiguous; and 4) false-ambiguous. The degree of membership values here forms a low-dimensional feature space that is not linearly distributed. Therefore, these nonlinearly distributed membership values are mapped into a high-dimensional feature space using the Gaussian kernel function. This study focuses on performing cluster analysis of computerized tomography scans of COVID-19 (CTSC-19) cases using AKDC. COVID-19, recognized as one of the most life-threatening diseases of this century, is highly contagious, and early diagnosis may prevent one-to-one transmission. Extensive empirical studies have been conducted with different types of CTSC-19 to demonstrate its effectiveness against existing kernel-based clustering and nonkernel-based clustering algorithms, namely mercer kernel fuzzy c-mean (MKFCM), kernel generalized FCM (KGFCM), kernel intuitionistic fuzzy entropy c-means (KIFECMs), morphological reconstruction and membership filtering clustering (FRFCM), and intuitionistic FCM based on membership information transferring and similarity measurements (IFCM-MS). The effectiveness of the proposed algorithm compared to the existing algorithms is evaluated using standard statistical metrics, such as dice index (DI), Jaccard index (JI), structural similarity index (SI), and correlation coefficient (CC). The empirical results show that AKDC is more effective than existing algorithms based on DI, JI, SI, and CC.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.1109/TSMC.2024.3418346
Xiangyu Wang;Ran Cheng;Yaochu Jin
Sparse large-scale evolutionary multiobjective optimization has garnered substantial interest over the past years due to its significant practical implications. These optimization problems are characterized by a predominance of zero-valued decision variables in the Pareto optimal solutions. Most existing algorithms focus on exploiting the sparsity of solutions by starting with initializing all decision variables with a nonzero value. Opposite to the existing approaches, we propose to initialize all decision variables to zero, then progressively identify and optimize the nonzero ones. The proposed framework consists of two stages. In the first stage of evolutionary optimization, a clustering method is applied at a predefined period of generations to identify nonzero decision variables according to the statistics of each variable’s current and historical values. Once a new nonzero decision variable is identified, it is randomly initialized within one of the two intervals, one defined by its lower quartile and lower bound, and the other by its upper quartile and upper bound. In the second stage, the clustering method is also periodically employed to distinguish between zero and nonzero decision variables. Different to the first stage, the zero decision variables will be set to zero straight, and the nonzero decision variables will be mutated at a higher probability. The performance of the proposed framework is empirically examined against state-of-the-art evolutionary algorithms on both sparse and nonsparse benchmarks and real-world problems, demonstrating its superior performance on different classes of problems.
{"title":"Sparse Large-Scale Multiobjective Optimization by Identifying Nonzero Decision Variables","authors":"Xiangyu Wang;Ran Cheng;Yaochu Jin","doi":"10.1109/TSMC.2024.3418346","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3418346","url":null,"abstract":"Sparse large-scale evolutionary multiobjective optimization has garnered substantial interest over the past years due to its significant practical implications. These optimization problems are characterized by a predominance of zero-valued decision variables in the Pareto optimal solutions. Most existing algorithms focus on exploiting the sparsity of solutions by starting with initializing all decision variables with a nonzero value. Opposite to the existing approaches, we propose to initialize all decision variables to zero, then progressively identify and optimize the nonzero ones. The proposed framework consists of two stages. In the first stage of evolutionary optimization, a clustering method is applied at a predefined period of generations to identify nonzero decision variables according to the statistics of each variable’s current and historical values. Once a new nonzero decision variable is identified, it is randomly initialized within one of the two intervals, one defined by its lower quartile and lower bound, and the other by its upper quartile and upper bound. In the second stage, the clustering method is also periodically employed to distinguish between zero and nonzero decision variables. Different to the first stage, the zero decision variables will be set to zero straight, and the nonzero decision variables will be mutated at a higher probability. The performance of the proposed framework is empirically examined against state-of-the-art evolutionary algorithms on both sparse and nonsparse benchmarks and real-world problems, demonstrating its superior performance on different classes of problems.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-18DOI: 10.1109/TSMC.2024.3429673
{"title":"Information For Authors","authors":"","doi":"10.1109/TSMC.2024.3429673","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3429673","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10604699","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The graph model is devoted to game conflicts arising from incongruent pursued objectives among conflicting parties. Considering that each conflicting party is composed of multiple individuals, preference conflicts stemming from differing cognitive levels and knowledge backgrounds exist among internal individuals. This scenario simultaneously involving game conflicts and preference conflicts is termed dual conflict decision-making problem. Tailored to effectively address this problem, this study proposes an enhanced graph model that incorporates internal consensus and external stability. The best-worst method, incorporating comparative linguistic expressions, is devised to effectively elicit individual preferences over game states. To mitigate preference conflicts inherent to internal individuals within conflicting party concerning game states, a consensus reaching model minimizing preference information loss is introduced. By this way, collective preferences are obtained. Based on these, the concept of “game consensus” is proposed to manage the game conflicts and the diverse behaviors exhibited by conflicting party. Finally, a case study regarding price conflict within a dual-channel supply chain, accompanied by a comparative analysis, is presented to validate the effectiveness of the proposal. Compared to existing graph model, the proposal effectively grapples with consensus issues and heterogeneous behaviors within conflicting parties, making it more valuable in practice.
{"title":"Graph Model for Conflict Resolution With Internal Consensus Reaching and External Game","authors":"Hengjie Zhang;Fang Wang;Yucheng Dong;Francisco Chiclana;Enrique Herrera-Viedma","doi":"10.1109/TSMC.2024.3418469","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3418469","url":null,"abstract":"The graph model is devoted to game conflicts arising from incongruent pursued objectives among conflicting parties. Considering that each conflicting party is composed of multiple individuals, preference conflicts stemming from differing cognitive levels and knowledge backgrounds exist among internal individuals. This scenario simultaneously involving game conflicts and preference conflicts is termed dual conflict decision-making problem. Tailored to effectively address this problem, this study proposes an enhanced graph model that incorporates internal consensus and external stability. The best-worst method, incorporating comparative linguistic expressions, is devised to effectively elicit individual preferences over game states. To mitigate preference conflicts inherent to internal individuals within conflicting party concerning game states, a consensus reaching model minimizing preference information loss is introduced. By this way, collective preferences are obtained. Based on these, the concept of “game consensus” is proposed to manage the game conflicts and the diverse behaviors exhibited by conflicting party. Finally, a case study regarding price conflict within a dual-channel supply chain, accompanied by a comparative analysis, is presented to validate the effectiveness of the proposal. Compared to existing graph model, the proposal effectively grapples with consensus issues and heterogeneous behaviors within conflicting parties, making it more valuable in practice.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-18DOI: 10.1109/TSMC.2024.3429681
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2024.3429681","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3429681","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10604666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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