Task strategy optimization for multi-state system based on virtual ship

Xiong, Zihao; Xie, Zongren; Liu, Jianwei; Xu, Yifan

doi:10.1016/j.simpat.2022.102578

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…TH S is the threshold for changing the plan speed, which is to prevent the ship reaching its destination early and causing task failure [17]. The real ship will not start the virtual ship for prediction after it sails to this point.…”

Section: ) Optimized Sailing Strategymentioning

confidence: 99%

Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems

Xiong

Xie²,

et al. 2022

IEEE Access

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The complex multi-state systems joint operation simulation and two task strategies are explored. For Monte Carlo sampling, based on the indirect sampling method, the principle of accelerated sampling methods, including forced transition FT and fault biasing FB, is studied. It is found that the direct adoption of the current FT and FB will result in systematic bias for the calculation of task success metrics. For the bias, state-transition chain truncation is studied, and its definition and truncation rules are proposed. In case study, after the simulation of various parameter combinations, it is found that when the sailing time is relatively smaller than the equipment reliability, which means a small probability event for the sampling of equipment faults, the ASM based on indirect sampling is applicable. Otherwise, the conventional sampling method should be used. The proposed statetransition chain truncation rule is also verified in case study.

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Section: ) Optimized Sailing Strategymentioning

confidence: 99%

Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems

Xiong

Xie²,

et al. 2022

IEEE Access

Self Cite

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“…Te main contributions of this paper are summarized as follows: [8] (1) It solves the diferent characteristics of diferent combat reconnaissance missions under complex naval battle environment, and the established particle swarm optimization model is compatible with the research and calculation of the multimission in diferent complex environments and the confguration of UAV combat reconnaissance missions with more complex constraints [9]. (2) Considering the diferences of diferent UAV models and diferent UAV equipment of the same model, through the consideration of the diferences of reconnaissance combat forces, the ability and efectiveness of diferent UAV combat forces to complete diferent combat reconnaissance tasks are further analyzed, and the mission confguration model is established on this basis [10].…”

Section: Introductionmentioning

confidence: 99%

Research on Combat Mission Configuration of Unmanned Aerial Vehicle Maritime Reconnaissance Based on Particle Swarm Optimization Algorithm

Dong,

Chen,

Wang

et al. 2024

Complexity

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In recent years, in the classic battles and armed conflicts around the world, battlefield environment reconnaissance and the collection and processing of operational information play an increasingly critical role in the victory and defeat of the battlefield. Unmanned equipment, especially UAV equipment, is used by more and more countries in the field of combat reconnaissance. Meanwhile, the types of UAV are gradually diversified with the change of operational requirements. UAVs adapted to different combat environments shine brightly on the battlefield. In terms of naval battle field, due to the limitations and deficiencies of reconnaissance methods such as surface radar, UAVs play a more prominent role in combat reconnaissance. There are more scenarios for UAVs to be used in combat reconnaissance in naval battle field and higher requirements for UAVs’ combat effectiveness. Therefore, this paper takes UAVs’ naval battle reconnaissance missions as the research object. By using PSO as the research method, this paper studies the combat reconnaissance task configuration of UAVs, hoping to contribute to the improvement of UAVs’ combat reconnaissance capability and combat effectiveness.

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MC-reduction simulation approach with heuristic rules for reliability estimation in a multi-state flow network

Chang,

Huang,

Huang

2024

Simulation Modelling Practice and Theory

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Task strategy optimization for multi-state system based on virtual ship

Cited by 5 publications

References 15 publications

Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems

Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems

Research on Combat Mission Configuration of Unmanned Aerial Vehicle Maritime Reconnaissance Based on Particle Swarm Optimization Algorithm

MC-reduction simulation approach with heuristic rules for reliability estimation in a multi-state flow network

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