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您的检索式：作者名="Karl HJohansson"
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| 1 | Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence显示文摘Based on the analysis of the characteristics and operation status of the process industry,as well as the development of the global intelligent manufacturing industry,a new mode of intelligent manufacturing for the process industry,namely,deep integration of industrial artificial intelligence and the Industrial Internet with the process industry,is proposed.This paper analyzes the development status of the existing three-tier structure of the process industry,which consists of the enterprise resource planning,the manufacturing execution system,and the process control system,and examines the decision-making,control,and operation management adopted by process enterprises.Based on this analysis,it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system.Finally,this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry. | Tao Yang Xinlei Yi Shaowen Lu Karl HJohansson Tianyou Chai | 2021 | Engineering2021，7，9： | 8 |
| 2 | Predefined-time distributed multiobjective optimization for network resource allocation显示文摘We consider the multiobjective optimization problem for the resource allocation of the multiagent network,where each agent contains multiple conflicting local objective functions.The goal is to find compromise solutions minimizing all local objective functions subject to resource constraints as much as possible,i.e.,the Pareto optimums.To this end,we first reformulate the multiobjective optimization problem into one single-objective distributed optimization problem by using the weighted L_(p)preference index,where the weighting factors of all local objective functions are obtained from the optimization procedure so that the optimizer of the latter is the desired Pareto optimum of the former.Next,we propose novel predefined-time algorithms to solve the reformulated problem by time-based generators.We show that the reformulated problem is solved within a predefined time if the local objective functions are strongly convex and smooth.Moreover,the settling time can be arbitrarily preset since it does not depend on the initial values and designed parameters.Finally,numerical simulations are presented to illustrate the effectiveness of the proposed algorithms. | Kunpeng ZHANG Lei XU Xinlei YI Zhengtao DING Karl HJOHANSSON Tianyou CHAI Tao YANG | 2023 | Science China(Information Sciences)2023，66，7： | 0 |
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