This paper investigates impulsive orbital attack-defense(AD)games under multiple constraints and victory conditions,involving three spacecraft:attacker,target,and defender.In the AD scenario,the attacker aims to breac...This paper investigates impulsive orbital attack-defense(AD)games under multiple constraints and victory conditions,involving three spacecraft:attacker,target,and defender.In the AD scenario,the attacker aims to breach the defender's interception to rendezvous with the target,while the defender seeks to protect the target by blocking or actively pursuing the attacker.Four different maneuvering constraints and five potential game outcomes are incorporated to more accurately model AD game problems and increase complexity,thereby reducing the effectiveness of traditional methods such as differential games and game-tree searches.To address these challenges,this study proposes a multiagent deep reinforcement learning solution with variable reward functions.Two attack strategies,Direct attack(DA)and Bypass attack(BA),are developed for the attacker,each focusing on different mission priorities.Similarly,two defense strategies,Direct interdiction(DI)and Collinear interdiction(CI),are designed for the defender,each optimizing specific defensive actions through tailored reward functions.Each reward function incorporates both process rewards(e.g.,distance and angle)and outcome rewards,derived from physical principles and validated via geometric analysis.Extensive simulations of four strategy confrontations demonstrate average defensive success rates of 75%for DI vs.DA,40%for DI vs.BA,80%for CI vs.DA,and 70%for CI vs.BA.Results indicate that CI outperforms DI for defenders,while BA outperforms DA for attackers.Moreover,defenders achieve their objectives more effectively under identical maneuvering capabilities.Trajectory evolution analyses further illustrate the effectiveness of the proposed variable reward function-driven strategies.These strategies and analyses offer valuable guidance for practical orbital defense scenarios and lay a foundation for future multi-agent game research.展开更多
In the implementation of quality function deployment （QFD）, the determination of the target values of engineering characteristics is a complex decision process with multiple variables and multiple objectives that sh...In the implementation of quality function deployment （QFD）, the determination of the target values of engineering characteristics is a complex decision process with multiple variables and multiple objectives that should trade off, and optimize all kinds of conflicts and constraints. A fuzzy linear programming model （FLP） is proposed. On the basis of the inherent fuzziness of QFD system, triangular fuzzy numbers are used to represent all the relationships and correlations, and then, the functional relationships between the customer needs and engineering characteristics and the functional correlations among the engineering characteristics are determined with the information in the house of quality （HoQ） fully used. The fuzzy linear programming （FLP） model aims to find the optimal target values of the engineering characteristics to maximize the customer satisfaction. Finally, the proposed method is illustrated by a numerical example.展开更多
以基因、转录、蛋白质等生命组学为主体的生物大数据快速积累和以深度学习为代表的人工智能技术迅猛发展,催生出各种类别的生物大模型(biological large models)。复杂的深度学习架构、巨大的参数量和算力需求、以及海量的预训练数据等...以基因、转录、蛋白质等生命组学为主体的生物大数据快速积累和以深度学习为代表的人工智能技术迅猛发展,催生出各种类别的生物大模型(biological large models)。复杂的深度学习架构、巨大的参数量和算力需求、以及海量的预训练数据等是大模型技术的主要特征。预训练数据类别及参数量一定程度上决定了大模型所具备的能力强弱,而不同的模型架构则可支撑不同类别的下游任务。近两年,围绕DNA/RNA/蛋白质等生物序列与单细胞表达图谱等组学数据分析挖掘、大分子结构预测、新型药物设计和功能机制解析等多种应用场景,涌现了多种通用或专用大模型,展示出其在生物医学研究及转化应用等领域的巨大潜力。本文旨在结合不同类别的生物数据特点和研究应用需求,概述生物数据特征及其用于生物大模型训练的技术方法,并进一步综述现有大模型在生物医学研究及疾病诊疗中的应用进展,为提升生物大模型能力、拓展应用范围提供新的思路。展开更多
基金supported by National Key R&D Program of China:Gravitational Wave Detection Project(Grant Nos.2021YFC22026,2021YFC2202601,2021YFC2202603)National Natural Science Foundation of China(Grant Nos.12172288 and 12472046)。
文摘This paper investigates impulsive orbital attack-defense(AD)games under multiple constraints and victory conditions,involving three spacecraft:attacker,target,and defender.In the AD scenario,the attacker aims to breach the defender's interception to rendezvous with the target,while the defender seeks to protect the target by blocking or actively pursuing the attacker.Four different maneuvering constraints and five potential game outcomes are incorporated to more accurately model AD game problems and increase complexity,thereby reducing the effectiveness of traditional methods such as differential games and game-tree searches.To address these challenges,this study proposes a multiagent deep reinforcement learning solution with variable reward functions.Two attack strategies,Direct attack(DA)and Bypass attack(BA),are developed for the attacker,each focusing on different mission priorities.Similarly,two defense strategies,Direct interdiction(DI)and Collinear interdiction(CI),are designed for the defender,each optimizing specific defensive actions through tailored reward functions.Each reward function incorporates both process rewards(e.g.,distance and angle)and outcome rewards,derived from physical principles and validated via geometric analysis.Extensive simulations of four strategy confrontations demonstrate average defensive success rates of 75%for DI vs.DA,40%for DI vs.BA,80%for CI vs.DA,and 70%for CI vs.BA.Results indicate that CI outperforms DI for defenders,while BA outperforms DA for attackers.Moreover,defenders achieve their objectives more effectively under identical maneuvering capabilities.Trajectory evolution analyses further illustrate the effectiveness of the proposed variable reward function-driven strategies.These strategies and analyses offer valuable guidance for practical orbital defense scenarios and lay a foundation for future multi-agent game research.
基金supported by the National Natural Science Foundation of China (70571041).
文摘In the implementation of quality function deployment （QFD）, the determination of the target values of engineering characteristics is a complex decision process with multiple variables and multiple objectives that should trade off, and optimize all kinds of conflicts and constraints. A fuzzy linear programming model （FLP） is proposed. On the basis of the inherent fuzziness of QFD system, triangular fuzzy numbers are used to represent all the relationships and correlations, and then, the functional relationships between the customer needs and engineering characteristics and the functional correlations among the engineering characteristics are determined with the information in the house of quality （HoQ） fully used. The fuzzy linear programming （FLP） model aims to find the optimal target values of the engineering characteristics to maximize the customer satisfaction. Finally, the proposed method is illustrated by a numerical example.
文摘以基因、转录、蛋白质等生命组学为主体的生物大数据快速积累和以深度学习为代表的人工智能技术迅猛发展,催生出各种类别的生物大模型(biological large models)。复杂的深度学习架构、巨大的参数量和算力需求、以及海量的预训练数据等是大模型技术的主要特征。预训练数据类别及参数量一定程度上决定了大模型所具备的能力强弱,而不同的模型架构则可支撑不同类别的下游任务。近两年,围绕DNA/RNA/蛋白质等生物序列与单细胞表达图谱等组学数据分析挖掘、大分子结构预测、新型药物设计和功能机制解析等多种应用场景,涌现了多种通用或专用大模型,展示出其在生物医学研究及转化应用等领域的巨大潜力。本文旨在结合不同类别的生物数据特点和研究应用需求,概述生物数据特征及其用于生物大模型训练的技术方法,并进一步综述现有大模型在生物医学研究及疾病诊疗中的应用进展,为提升生物大模型能力、拓展应用范围提供新的思路。
