When an emergency happens, one of the most important tasks is to perform effective emergency disposal. To this end, emergency organizations need to collaborate to accomplish missions that exceed the capacity of any single organization. Typically, an emergency disposal is structured as a set of collaborative processes, referred to as cross-organization emergency response processes (CERPs). To deliver better emergency services, the initial step is to construct a high-quality CERP model. This paper introduces a top-down CERP model construction approach to tackle one of the most challenging issues in this area: How to construct a CERP model such that each organization can design, change, and modify their own processes without disturbing the overall collaboration and correctness of CERP. The proposed top-down CERP model construction approach involves the following stages: 1) Cross-organization public process model construction; 2) Intra-organization public process model generation; 3) Behavior-preserving intra-organization private process model construction; and 4) Organization-specific CERP model construction. A case study on cross-organization fire emergency response is conducted to demonstrate the applicability and effectiveness of the proposed approach.
{"title":"Behavior-Preserving Top-Down Construction of Cross-Organization Emergency Response Processes","authors":"Cong Liu;Huiling Li;Qingtian Zeng;Qi Mo;MengChu Zhou;Long Cheng;Shangce Gao","doi":"10.1109/JAS.2025.125537","DOIUrl":"https://doi.org/10.1109/JAS.2025.125537","url":null,"abstract":"When an emergency happens, one of the most important tasks is to perform effective emergency disposal. To this end, emergency organizations need to collaborate to accomplish missions that exceed the capacity of any single organization. Typically, an emergency disposal is structured as a set of collaborative processes, referred to as cross-organization emergency response processes (CERPs). To deliver better emergency services, the initial step is to construct a high-quality CERP model. This paper introduces a top-down CERP model construction approach to tackle one of the most challenging issues in this area: How to construct a CERP model such that each organization can design, change, and modify their own processes without disturbing the overall collaboration and correctness of CERP. The proposed top-down CERP model construction approach involves the following stages: 1) Cross-organization public process model construction; 2) Intra-organization public process model generation; 3) Behavior-preserving intra-organization private process model construction; and 4) Organization-specific CERP model construction. A case study on cross-organization fire emergency response is conducted to demonstrate the applicability and effectiveness of the proposed approach.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2513-2524"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861207","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}
Weixiong Huang;Rui Wang;Tao Zhang;Sheng Qi;Ling Wang
Solving constrained multiobjective optimization problems (CMOPs) is a highly challenging work. Numerous complex nonlinear constraints significantly add to the complexity of CMOPs, resulting in an exceptionally intricate feasible region. Makes it difficult for the algorithm to search for the complete constraint PF. In addition, under the influence of multiple complex nonlinear constraints, the conventional calculation method of overall constraint violation is inefficient for assessing the quality of infeasible solutions, potentially misguiding the evolutionary direction of the population. In response to these challenges, this paper proposes the fuzzy constraint dominance strategy (FCDS). This novel approach facilitates nuanced comparisons of solutions to strike a better balance between objectives and constraints. The fuzzy constraint violation introduced in FCDS mitigates the misleading impact of complex nonlinear constraints. Moreover, FCDS divides the solution process of complex CMOP into multiple stages from easy to difficult, and uses adaptive methods to increase the difficulty level of the problem. Systematic experiments on four test suites and three real-world applications have conclusively demonstrated the superior competitiveness of FCDS against leading algorithms.
{"title":"Fuzzy Constraint Dominance Strategy for Constrainted Multiobjective Optimization Problems with Multiple Constraints","authors":"Weixiong Huang;Rui Wang;Tao Zhang;Sheng Qi;Ling Wang","doi":"10.1109/JAS.2025.125255","DOIUrl":"https://doi.org/10.1109/JAS.2025.125255","url":null,"abstract":"Solving constrained multiobjective optimization problems (CMOPs) is a highly challenging work. Numerous complex nonlinear constraints significantly add to the complexity of CMOPs, resulting in an exceptionally intricate feasible region. Makes it difficult for the algorithm to search for the complete constraint PF. In addition, under the influence of multiple complex nonlinear constraints, the conventional calculation method of overall constraint violation is inefficient for assessing the quality of infeasible solutions, potentially misguiding the evolutionary direction of the population. In response to these challenges, this paper proposes the fuzzy constraint dominance strategy (FCDS). This novel approach facilitates nuanced comparisons of solutions to strike a better balance between objectives and constraints. The fuzzy constraint violation introduced in FCDS mitigates the misleading impact of complex nonlinear constraints. Moreover, FCDS divides the solution process of complex CMOP into multiple stages from easy to difficult, and uses adaptive methods to increase the difficulty level of the problem. Systematic experiments on four test suites and three real-world applications have conclusively demonstrated the superior competitiveness of FCDS against leading algorithms.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2455-2472"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861209","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}
Lu Ren;Zelong Fang;Wenzhang Liu;Chaoxu Mu;Changyin Sun
Dear Editor, This letter addresses the challenges of sparse and delayed rewards in complex indoor navigation tasks. To this end, we propose a task decomposition-based reinforcement learning framework that integrates a reinforcement learning (RL) algorithm with a path planner. Specifically, the rapidly-exploring random tree star (RRT*) algorithm is employed to generate a sequence of sub-goals, which are incorporated into the state space. This decomposition transforms the original long-horizon task into a series of easier sub-tasks with reward monotonicity, providing valuable spatial priors for the unmanned aerial vehicles (UAVs) and guiding it toward more effective exploration. As a result, the proposed method enhances learning stability and mitigates the negative effects of sparse and delayed rewards, facilitating the learning of an optimal navigation policy. Our source code is available at https://github.com/AHU-QXY/indoor_drone_navigation.
{"title":"Deep Reinforcement Learning for UAV Indoor Navigation Through Task Decomposition","authors":"Lu Ren;Zelong Fang;Wenzhang Liu;Chaoxu Mu;Changyin Sun","doi":"10.1109/JAS.2025.125642","DOIUrl":"https://doi.org/10.1109/JAS.2025.125642","url":null,"abstract":"Dear Editor, This letter addresses the challenges of sparse and delayed rewards in complex indoor navigation tasks. To this end, we propose a task decomposition-based reinforcement learning framework that integrates a reinforcement learning (RL) algorithm with a path planner. Specifically, the rapidly-exploring random tree star (RRT*) algorithm is employed to generate a sequence of sub-goals, which are incorporated into the state space. This decomposition transforms the original long-horizon task into a series of easier sub-tasks with reward monotonicity, providing valuable spatial priors for the unmanned aerial vehicles (UAVs) and guiding it toward more effective exploration. As a result, the proposed method enhances learning stability and mitigates the negative effects of sparse and delayed rewards, facilitating the learning of an optimal navigation policy. Our source code is available at https://github.com/AHU-QXY/indoor_drone_navigation.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2627-2629"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11321067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861195","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}
High-dimensional and incomplete (HDI) matrices are commonly encountered in various big data-related applications for illustrating the complex interactions among numerous entities, like the user-item interactions in a commercial recommender system or the user-user interactions in a social network services system. The factorization of such an HDI matrix can embed the involved entities into the low-dimensional feature space for acquiring their principal representation, which is a vital task in various application scenes and is often established through the Latent Factor Analysis (LFA). Nevertheless, an HDI matrix can be huge when the corresponding application explodes to involve millions of users, items, or other interactive nodes. In this case, a parallel optimization algorithm is desired for raising the scalability and time efficiency of an LFA model. This paper provides a comprehensive review of the existing parallel optimization algorithms for the LFA model. Specifically, it performs: 1) discussion and summary of these algorithms based on computing architecture and mode, 2) empirical studies of representative models, and 3) summary of the current challenges and future directions in this domain. This survey aims to offer an exhaustive review of Parallel Optimization Algorithms for High-Dimensional and Incomplete Matrix Factorization, thereby fostering further research in this field.
在各种与大数据相关的应用中，经常会遇到高维不完全矩阵（High-dimensional and incomplete， HDI），用于描述众多实体之间的复杂交互，比如商业推荐系统中的用户-物品交互，或者社交网络服务系统中的用户-用户交互。这种HDI矩阵的分解可以将相关实体嵌入到低维特征空间中以获取其主表示，这是各种应用场景中的重要任务，通常通过潜在因素分析（Latent Factor Analysis， LFA）建立。然而，当相应的应用程序激增到涉及数百万用户、项目或其他交互节点时，HDI矩阵可能会非常庞大。在这种情况下，需要一种并行优化算法来提高LFA模型的可扩展性和时间效率。本文对现有的LFA模型并行优化算法进行了综述。具体而言：1)基于计算架构和模式对这些算法进行了讨论和总结；2)代表性模型的实证研究；3)总结了该领域当前面临的挑战和未来的方向。本调查的目的是提供一个详尽的回顾并行优化算法的高维和不完全矩阵分解，从而促进该领域的进一步研究。
{"title":"A Comprehensive Review of Parallel Optimization Algorithms for High-Dimensional and Incomplete Matrix Factorization","authors":"Qicong Hu;Hao Wu;Xin Luo","doi":"10.1109/JAS.2025.125774","DOIUrl":"https://doi.org/10.1109/JAS.2025.125774","url":null,"abstract":"High-dimensional and incomplete (HDI) matrices are commonly encountered in various big data-related applications for illustrating the complex interactions among numerous entities, like the user-item interactions in a commercial recommender system or the user-user interactions in a social network services system. The factorization of such an HDI matrix can embed the involved entities into the low-dimensional feature space for acquiring their principal representation, which is a vital task in various application scenes and is often established through the Latent Factor Analysis (LFA). Nevertheless, an HDI matrix can be huge when the corresponding application explodes to involve millions of users, items, or other interactive nodes. In this case, a parallel optimization algorithm is desired for raising the scalability and time efficiency of an LFA model. This paper provides a comprehensive review of the existing parallel optimization algorithms for the LFA model. Specifically, it performs: 1) discussion and summary of these algorithms based on computing architecture and mode, 2) empirical studies of representative models, and 3) summary of the current challenges and future directions in this domain. This survey aims to offer an exhaustive review of Parallel Optimization Algorithms for High-Dimensional and Incomplete Matrix Factorization, thereby fostering further research in this field.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2399-2426"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861215","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}
Jin Ma;Min Tan;Yu Wang;Shaowei Cui;Yaozhong Cao;Shuo Wang
Underwater tactile sensing technology holds considerable promise in close-range perception for underwater vehicle manipulator systems (UVMSs), providing an alternative when other methods fail. Traditional array-based underwater tactile sensors face challenges in calibration and performance, such as cross-sensitivity to water pressure and low resolution. In this study, a novel gel-based underwater visuotactile sensor, GelUW, is introduced to address these issues. This sensor achieves high three-dimensional spatial resolution (1 mm × 1 mm in the plane, 0.7 mm in depth) in shallow water (50 m). Specifically, waterproofing and pressure-balancing mechanisms are designed to handle water pressure, with comparative experiments demonstrating the robustness of the sensor to pressure variations. A multi-color pattern-based 3D geometry perception pipeline (MCP-3D) is proposed for underwater dynamic contact scenarios to tackle marker mismatches caused by impacts, with tapping experiments revealing its self-repair capabilities and 400% improvement in stability. Furthermore, the GelUW is integrated into a UVMS for object surface perception, and pool experiments confirm its high-precision geometry perception capabilities. Finally, the UVMS equipped with GelUW successfully performs crack detection tasks at the Gezhouba Dam in Yichang, China.
{"title":"GelUW: A Novel Underwater Vision-Based Tactile Sensor for Geometry Perception","authors":"Jin Ma;Min Tan;Yu Wang;Shaowei Cui;Yaozhong Cao;Shuo Wang","doi":"10.1109/JAS.2025.125450","DOIUrl":"https://doi.org/10.1109/JAS.2025.125450","url":null,"abstract":"Underwater tactile sensing technology holds considerable promise in close-range perception for underwater vehicle manipulator systems (UVMSs), providing an alternative when other methods fail. Traditional array-based underwater tactile sensors face challenges in calibration and performance, such as cross-sensitivity to water pressure and low resolution. In this study, a novel gel-based underwater visuotactile sensor, GelUW, is introduced to address these issues. This sensor achieves high three-dimensional spatial resolution (1 mm × 1 mm in the plane, 0.7 mm in depth) in shallow water (50 m). Specifically, waterproofing and pressure-balancing mechanisms are designed to handle water pressure, with comparative experiments demonstrating the robustness of the sensor to pressure variations. A multi-color pattern-based 3D geometry perception pipeline (MCP-3D) is proposed for underwater dynamic contact scenarios to tackle marker mismatches caused by impacts, with tapping experiments revealing its self-repair capabilities and 400% improvement in stability. Furthermore, the GelUW is integrated into a UVMS for object surface perception, and pool experiments confirm its high-precision geometry perception capabilities. Finally, the UVMS equipped with GelUW successfully performs crack detection tasks at the Gezhouba Dam in Yichang, China.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2499-2512"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145859874","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}
Dear Editor, This letter investigates a data-driven $H_{infty}$ feedback fault-tolerant tracking control problem using off-policy Q-learning, focusing on challenges such as unobservable system states, external disturbances, and actuator faults in industrial processes. The effectiveness of the proposed method is demonstrated through simulations on an injection molding process.
{"title":"Input-Output Data Driven Intelligent $H_{infty}$ Fault-Tolerant Tracking Control for Industrial Process in Industry 5.0","authors":"Limin Wang;Linzhu Jia;Ridong Zhang","doi":"10.1109/JAS.2025.125465","DOIUrl":"https://doi.org/10.1109/JAS.2025.125465","url":null,"abstract":"Dear Editor, This letter investigates a data-driven <tex>$H_{infty}$</tex> feedback fault-tolerant tracking control problem using off-policy Q-learning, focusing on challenges such as unobservable system states, external disturbances, and actuator faults in industrial processes. The effectiveness of the proposed method is demonstrated through simulations on an injection molding process.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2624-2626"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11321135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861202","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}
With the increasing concerns about energy consumption and environmental protection, minimizing energy consumption while ensuring desired productivity becomes more and more important in flexible assembly systems (FASs) design and operation. However, because of the complexity of deadlock-prone FASs, only a few researchers have addressed their scheduling problems. Besides, no existing literature in the field of scheduling of deadlock-prone FASs takes energy consumption minimization as the optimization criterion to our best knowledge. This paper presents an $A^{star}$-based hybrid heuristic search algorithm to minimize the total energy consumption of FASs with tool change processes. Based on a developed Petri net (PN) model, two energy functions are proposed to calculate the energy consumption of FASs. To achieve better performance, six new heuristic functions are designed to guide the search process by considering the features of FASs. Besides, two selection functions are proposed to evaluate the prospects of vertexes and choose the promising ones. Moreover, a dynamic window is applied in the algorithm to limit the search space, and a deadlock prevention policy is used to ensure feasible schedules. Experimental results show that the proposed algorithm can effectively find feasible schedules for FASs, and a well-designed heuristic function is likely to obtain schedules to meet industrial application requirements.
{"title":"Petri Net and Hybrid Heuristic Search-Based Method for Energy-Minimized Scheduling of Flexible Assembly Systems with Tool Change Processes","authors":"Jianchao Luo;Xinjian Jiang;MengChu Zhou;Keyi Xing;Abdullah Abusorrah","doi":"10.1109/JAS.2025.125756","DOIUrl":"https://doi.org/10.1109/JAS.2025.125756","url":null,"abstract":"With the increasing concerns about energy consumption and environmental protection, minimizing energy consumption while ensuring desired productivity becomes more and more important in flexible assembly systems (FASs) design and operation. However, because of the complexity of deadlock-prone FASs, only a few researchers have addressed their scheduling problems. Besides, no existing literature in the field of scheduling of deadlock-prone FASs takes energy consumption minimization as the optimization criterion to our best knowledge. This paper presents an <tex>$A^{star}$</tex>-based hybrid heuristic search algorithm to minimize the total energy consumption of FASs with tool change processes. Based on a developed Petri net (PN) model, two energy functions are proposed to calculate the energy consumption of FASs. To achieve better performance, six new heuristic functions are designed to guide the search process by considering the features of FASs. Besides, two selection functions are proposed to evaluate the prospects of vertexes and choose the promising ones. Moreover, a dynamic window is applied in the algorithm to limit the search space, and a deadlock prevention policy is used to ensure feasible schedules. Experimental results show that the proposed algorithm can effectively find feasible schedules for FASs, and a well-designed heuristic function is likely to obtain schedules to meet industrial application requirements.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2473-2485"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861239","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}
Weijie Mai;Zhifan Tang;Weili Liu;Jinghui Zhong;Hu Jin
Evolutionary multitasking optimization (EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and engineering problems. Nevertheless, faced with the widespread large-scale multi-objective optimization problems (LSMOPs), the existing EMTO literature barely involves the research of LSMOPs. More importantly, these EMTO algorithms often get trapped in local optima when dealing with LSMOPs, resulting in a slow convergence speed, which is worthy of our attention. To this end, this paper proposes an EMTO algorithm dedicated to solving LSMOPs. On the one hand, given the intricate nature of LSMOPs, we propose a knowledge domination-based knowledge transfer mechanism that can flexibly transfer knowledge from multiple knowledge representations, i.e., the information distribution and distribution distance of the task population. On the other hand, we design an elite vector-guided search strategy. Specifically, the generative adversarial network (GAN) model should first be trained within the divided populations. Then, the well-trained model is used to generate a high-quality individual for the target individual. After that, the high-quality individual is combined with the top-performing individual in the current population to find the elite vector corresponding to the target individual. Finally, the elite vector is applied to guide the target individual to accelerate convergence towards the global optimum in the high-dimensional decision space. We conduct comprehensive experimental investigations on two artificial LSMOPs suites and six real-world LSMOPs to validate the efficiency and robustness of the proposed algorithm, through comparative analysis with state-of-the-art peer algorithms.
{"title":"Evolutionary Multitasking with Multiple Knowledge Representations and Elite Vector Guidance for Solving Large-Scale Multi-Objective Optimization Problems","authors":"Weijie Mai;Zhifan Tang;Weili Liu;Jinghui Zhong;Hu Jin","doi":"10.1109/JAS.2025.125483","DOIUrl":"https://doi.org/10.1109/JAS.2025.125483","url":null,"abstract":"Evolutionary multitasking optimization (EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and engineering problems. Nevertheless, faced with the widespread large-scale multi-objective optimization problems (LSMOPs), the existing EMTO literature barely involves the research of LSMOPs. More importantly, these EMTO algorithms often get trapped in local optima when dealing with LSMOPs, resulting in a slow convergence speed, which is worthy of our attention. To this end, this paper proposes an EMTO algorithm dedicated to solving LSMOPs. On the one hand, given the intricate nature of LSMOPs, we propose a knowledge domination-based knowledge transfer mechanism that can flexibly transfer knowledge from multiple knowledge representations, i.e., the information distribution and distribution distance of the task population. On the other hand, we design an elite vector-guided search strategy. Specifically, the generative adversarial network (GAN) model should first be trained within the divided populations. Then, the well-trained model is used to generate a high-quality individual for the target individual. After that, the high-quality individual is combined with the top-performing individual in the current population to find the elite vector corresponding to the target individual. Finally, the elite vector is applied to guide the target individual to accelerate convergence towards the global optimum in the high-dimensional decision space. We conduct comprehensive experimental investigations on two artificial LSMOPs suites and six real-world LSMOPs to validate the efficiency and robustness of the proposed algorithm, through comparative analysis with state-of-the-art peer algorithms.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2553-2571"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861193","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}
The main motivation of this paper arises from the fact that some complex systems have high demands for time precision, which need to reach the desired state in a pre-specified time interval. This paper addresses the predetermined-time output projective synchronization of coupled fuzzy neural networks. To mimic the uncertainty and relatedness among complex systems, coupled fuzzy neural networks are introduced to characterize complex systems in this paper. First, a novel controller is developed by means of a generalized exponential function and output states information, which can effectively avoid the chattering situations arising from the sign function. Under the controller, the output states of coupled fuzzy neural networks eventually converge to the projective state in the predefined time, which can reduce the requirements for sensor devices and improve the flexibility and efficiency of the control scheme. Second, in light of Lyapunov function and inequality techniques, sufficient criteria for ensuring to achieve the predetermined-time output projective synchronization of coupled fuzzy neural networks are deduced based on the assumption of the digraph containing a spanning tree. Furthermore, the results obtained in this paper not only represent an extension of master-slave systems but also demonstrate that the output synchronization of coupled fuzzy neural networks is a specific case of projective synchronization exemplified by a corollary. Finally, numerical examples are offered to reveal the correctness of theoretical results.
{"title":"Predetermined-Time Output Projective Synchronization of Coupled Fuzzy Neural Networks via Generalized Exponential Function","authors":"Ting Liu;Shiwen Xie;Yongfang Xie;Peng Liu;Tingwen Huang","doi":"10.1109/JAS.2025.125519","DOIUrl":"https://doi.org/10.1109/JAS.2025.125519","url":null,"abstract":"The main motivation of this paper arises from the fact that some complex systems have high demands for time precision, which need to reach the desired state in a pre-specified time interval. This paper addresses the predetermined-time output projective synchronization of coupled fuzzy neural networks. To mimic the uncertainty and relatedness among complex systems, coupled fuzzy neural networks are introduced to characterize complex systems in this paper. First, a novel controller is developed by means of a generalized exponential function and output states information, which can effectively avoid the chattering situations arising from the sign function. Under the controller, the output states of coupled fuzzy neural networks eventually converge to the projective state in the predefined time, which can reduce the requirements for sensor devices and improve the flexibility and efficiency of the control scheme. Second, in light of Lyapunov function and inequality techniques, sufficient criteria for ensuring to achieve the predetermined-time output projective synchronization of coupled fuzzy neural networks are deduced based on the assumption of the digraph containing a spanning tree. Furthermore, the results obtained in this paper not only represent an extension of master-slave systems but also demonstrate that the output synchronization of coupled fuzzy neural networks is a specific case of projective synchronization exemplified by a corollary. Finally, numerical examples are offered to reveal the correctness of theoretical results.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2602-2611"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145859876","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}
Zhenlin Hu;Zhizhi Peng;Zhen Bi;Qing Shen;Zhenfang Liu;Jungang Lou;Xin Luo
As the costs of global healthcare systems continue to rise, large language models (LLMs) have emerged as a promising technology with vast potential and wide-ranging applications in the medical field. We provide a detailed overview of the lifecycle of medical LLMs, encompassing three key stages: get, refine, and use, aimed at assisting healthcare practitioners and patients in utilizing these models more effectively. We also summarize the currently widely used medical evaluation benchmarks, analyzing their advantages and limitations. Furthermore, we conduct a comparative analysis of specialized medical LLMs on benchmarks such as MedQA, PubMedQA, MMLU-MED, and MedM-CQA, revealing that methods like retrieval-augmented generation (RAG) enable smaller models to outperform larger ones by effectively integrating external medical knowledge. This review provides a reference for medical professionals to evaluate LLM capabilities and inspires the development of more effective benchmarking methods. Additionally, we showcase practical applications of medical LLMs in clinical, research, and educational settings, providing healthcare workers with valuable resources. Finally, we identify current challenges faced by medical LLMs and present outlooks for future technological advancements, aiming to inspire users to explore new ways to address existing issues. This review serves as an entry point for interested clinicians, helping them determine whether and how to integrate LLM technology into healthcare for the benefit of patients and practitioners.
{"title":"Advancing Healthcare with Large Language Models: Techniques and Application","authors":"Zhenlin Hu;Zhizhi Peng;Zhen Bi;Qing Shen;Zhenfang Liu;Jungang Lou;Xin Luo","doi":"10.1109/JAS.2025.125540","DOIUrl":"https://doi.org/10.1109/JAS.2025.125540","url":null,"abstract":"As the costs of global healthcare systems continue to rise, large language models (LLMs) have emerged as a promising technology with vast potential and wide-ranging applications in the medical field. We provide a detailed overview of the lifecycle of medical LLMs, encompassing three key stages: get, refine, and use, aimed at assisting healthcare practitioners and patients in utilizing these models more effectively. We also summarize the currently widely used medical evaluation benchmarks, analyzing their advantages and limitations. Furthermore, we conduct a comparative analysis of specialized medical LLMs on benchmarks such as MedQA, PubMedQA, MMLU-MED, and MedM-CQA, revealing that methods like retrieval-augmented generation (RAG) enable smaller models to outperform larger ones by effectively integrating external medical knowledge. This review provides a reference for medical professionals to evaluate LLM capabilities and inspires the development of more effective benchmarking methods. Additionally, we showcase practical applications of medical LLMs in clinical, research, and educational settings, providing healthcare workers with valuable resources. Finally, we identify current challenges faced by medical LLMs and present outlooks for future technological advancements, aiming to inspire users to explore new ways to address existing issues. This review serves as an entry point for interested clinicians, helping them determine whether and how to integrate LLM technology into healthcare for the benefit of patients and practitioners.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"12 12","pages":"2371-2398"},"PeriodicalIF":19.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861237","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：