The battlefield situation changes rapidly because underwater targets'are concealment and the sea environment is uncertain.So,a great number of situation information greatly increase,which need to be dealt with in ...The battlefield situation changes rapidly because underwater targets'are concealment and the sea environment is uncertain.So,a great number of situation information greatly increase,which need to be dealt with in the course of scouting underwater targets.Situation assessment in sea battlefield with a lot of uncertain information is studied,and a new situation assessment method of scouting underwater targets with fixed-wing patrol aircraft is proposed based on the cloud Bayesian network,which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation.Moreover,in the method,the cloud model knowledge deal with the input data of Bayesian network reasoning,and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied;also,the fuzziness and stochasticity of cloud theory in knowledge expression,the reasoning ability of Bayesian network,are combined.Then,the situation assessment model of scouting underwater targets with fixed-wing patrol aircraft is established.Hence,the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined.Next,the cloud model is used to deal with Bayesian network,and the discrete Bayesian network is obtained.Moreover,after CPT of each node and the transformation between certainty degree and probability are accomplished;the final situation level is obtained through a probability synthesis formula.Therefore,the target type and the operational intention of the other side are deduced to form the battlefield situation.Finally,simulations are carried out,and the rationality and validity of the proposed method are testified by simulation results.By this method,the battlefield situation can be gained.And this method has a wider application range,especially for large sample data processing,and it has better practicability.展开更多
根据电力系统中广域测量系统(wide area measurement system,WAMS)通信主干网的自愈环形结构特点,以一个具代表性的4节点2纤双向通道环网为例,通过定性分析和定量计算相结合,提出了WAMS通信主干网的风险评估模型和计算方法。该模型将风...根据电力系统中广域测量系统(wide area measurement system,WAMS)通信主干网的自愈环形结构特点,以一个具代表性的4节点2纤双向通道环网为例,通过定性分析和定量计算相结合,提出了WAMS通信主干网的风险评估模型和计算方法。该模型将风险评估涉及的资产、威胁、脆弱点和风险影响4大要素有机融合。采用可靠性分析方法计算出风险事件发生的概率,借鉴层次分析法中构建比较判断矩阵实现风险影响值的量化计算,从而得出通信主干网的风险综合值。计算结果表明所提出的方法具有良好的实用性和可操作性,可用于后续的评估工作,并为电力系统制定安全保护策略提供了依据。展开更多
We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavi...We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.展开更多
基金Natural Science Foundation of Shangdong,Grant/Award Number:ZR2019MF065.
文摘The battlefield situation changes rapidly because underwater targets'are concealment and the sea environment is uncertain.So,a great number of situation information greatly increase,which need to be dealt with in the course of scouting underwater targets.Situation assessment in sea battlefield with a lot of uncertain information is studied,and a new situation assessment method of scouting underwater targets with fixed-wing patrol aircraft is proposed based on the cloud Bayesian network,which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation.Moreover,in the method,the cloud model knowledge deal with the input data of Bayesian network reasoning,and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied;also,the fuzziness and stochasticity of cloud theory in knowledge expression,the reasoning ability of Bayesian network,are combined.Then,the situation assessment model of scouting underwater targets with fixed-wing patrol aircraft is established.Hence,the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined.Next,the cloud model is used to deal with Bayesian network,and the discrete Bayesian network is obtained.Moreover,after CPT of each node and the transformation between certainty degree and probability are accomplished;the final situation level is obtained through a probability synthesis formula.Therefore,the target type and the operational intention of the other side are deduced to form the battlefield situation.Finally,simulations are carried out,and the rationality and validity of the proposed method are testified by simulation results.By this method,the battlefield situation can be gained.And this method has a wider application range,especially for large sample data processing,and it has better practicability.
文摘根据电力系统中广域测量系统(wide area measurement system,WAMS)通信主干网的自愈环形结构特点,以一个具代表性的4节点2纤双向通道环网为例,通过定性分析和定量计算相结合,提出了WAMS通信主干网的风险评估模型和计算方法。该模型将风险评估涉及的资产、威胁、脆弱点和风险影响4大要素有机融合。采用可靠性分析方法计算出风险事件发生的概率,借鉴层次分析法中构建比较判断矩阵实现风险影响值的量化计算,从而得出通信主干网的风险综合值。计算结果表明所提出的方法具有良好的实用性和可操作性,可用于后续的评估工作,并为电力系统制定安全保护策略提供了依据。
基金This work is partially supported by National Basic Research Priorities Programme （No. 2013CB329502）, Na-tional Natural Science Foundation of China （No. 61472468, 61502115）, General Research Fund of Hong Kong （No. 417112）, and Fundamental Research Funds for the Central Universities （No. 3262014T75, 3262015T20, 3262015T70, 3262016T31）.
文摘We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.
