为预测机场场面运行安全态势,提出一种本体和贝叶斯网络(BN)集成建模方法来评估跑道侵入严重度值,并进行最优风险把控措施决策。首先,基于威胁与差错管理(threat and error management,TEM)模型构建跑道侵入态势评估领域本体并完成BN转...为预测机场场面运行安全态势,提出一种本体和贝叶斯网络(BN)集成建模方法来评估跑道侵入严重度值,并进行最优风险把控措施决策。首先,基于威胁与差错管理(threat and error management,TEM)模型构建跑道侵入态势评估领域本体并完成BN转换;然后,利用融合本体语义信息的BN模型学习历史数据,提出跑道侵入事故征候严重度值评价指标;最后,分析实际案例并进行最优风险把控决策。结果表明,跑道侵入态势评估系统能够有效地描述事故征候的形成进而演化为跑道侵入事故的动态过程,并为最优风险控制决策提供客观依据。展开更多
This paper describes a Bayesian approach to robot group control applied in industrial applications. The proposed model is based on well-known concepts of Ubiquitous Computing and can enable some degree of contextual p...This paper describes a Bayesian approach to robot group control applied in industrial applications. The proposed model is based on well-known concepts of Ubiquitous Computing and can enable some degree of contextual perception of the environment. Compared with classical industrial robots, usually preprogrammed for a limited number of operations/actions, the system based on this model can react in uncertain situations and scenarios. The model combines ontology to describe the specific domain of interest and decision-making mechanisms based on Bayesian Networks to enable the work of a single robot without human intervention by learning Behavioral Patterns of other robots in the group. The described model is designed to be expressive enough to provide adequate level of abstractions needed for making timely appropriate actions and respecting the current application.展开更多
文摘为预测机场场面运行安全态势,提出一种本体和贝叶斯网络(BN)集成建模方法来评估跑道侵入严重度值,并进行最优风险把控措施决策。首先,基于威胁与差错管理(threat and error management,TEM)模型构建跑道侵入态势评估领域本体并完成BN转换;然后,利用融合本体语义信息的BN模型学习历史数据,提出跑道侵入事故征候严重度值评价指标;最后,分析实际案例并进行最优风险把控决策。结果表明,跑道侵入态势评估系统能够有效地描述事故征候的形成进而演化为跑道侵入事故的动态过程,并为最优风险控制决策提供客观依据。
文摘This paper describes a Bayesian approach to robot group control applied in industrial applications. The proposed model is based on well-known concepts of Ubiquitous Computing and can enable some degree of contextual perception of the environment. Compared with classical industrial robots, usually preprogrammed for a limited number of operations/actions, the system based on this model can react in uncertain situations and scenarios. The model combines ontology to describe the specific domain of interest and decision-making mechanisms based on Bayesian Networks to enable the work of a single robot without human intervention by learning Behavioral Patterns of other robots in the group. The described model is designed to be expressive enough to provide adequate level of abstractions needed for making timely appropriate actions and respecting the current application.
