Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby c...Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.展开更多
为了分析机场跑道侵入的影响因素,更有针对性地对预防机场跑道侵入提出合理建议,结合相关信息通告中统计的数据,首先从人员因素、环境因素、设备因素和管理因素4个方面分析跑道侵入的事故成因,并建立了故障树(Fault Tree Analysis,FTA)...为了分析机场跑道侵入的影响因素,更有针对性地对预防机场跑道侵入提出合理建议,结合相关信息通告中统计的数据,首先从人员因素、环境因素、设备因素和管理因素4个方面分析跑道侵入的事故成因,并建立了故障树(Fault Tree Analysis,FTA)模型和贝叶斯网络(Bayesian Network,BN)模型,然后利用软件Netica对贝叶斯网络模型进行了后验概率推理与敏感性分析,最后根据分析结果提出了相应的建议。结果表明,人员因素影响程度最大,其次是管理因素,而环境因素和设备因素的影响程度相对偏小。展开更多
基金sponsored by the National Defense Science and Technology Key Laboratory Fund(Grant No.61422062205)the Equipment Pre-Research Fund(Grant No.JCKYS2022LD9)。
文摘Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.
文摘为了分析机场跑道侵入的影响因素,更有针对性地对预防机场跑道侵入提出合理建议,结合相关信息通告中统计的数据,首先从人员因素、环境因素、设备因素和管理因素4个方面分析跑道侵入的事故成因,并建立了故障树(Fault Tree Analysis,FTA)模型和贝叶斯网络(Bayesian Network,BN)模型,然后利用软件Netica对贝叶斯网络模型进行了后验概率推理与敏感性分析,最后根据分析结果提出了相应的建议。结果表明,人员因素影响程度最大,其次是管理因素,而环境因素和设备因素的影响程度相对偏小。
