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
目的:探讨Pentacam眼前节分析仪和Keratron Scout角膜地形图仪测量Kappa角的一致性,评价两种仪器测量结果的重复性。方法:前瞻性随机对照研究。选取2018-01-01/30在我院眼视光中心行近视术前检查的患者69例,所有受试者由同一检查者分别...目的:探讨Pentacam眼前节分析仪和Keratron Scout角膜地形图仪测量Kappa角的一致性,评价两种仪器测量结果的重复性。方法:前瞻性随机对照研究。选取2018-01-01/30在我院眼视光中心行近视术前检查的患者69例,所有受试者由同一检查者分别采用Pentacam和Keratron Scout重复测量3次,以(X ,Y)坐标形式记录Kappa角的大小,采用组内相关系数(ICC)、Cronbach s Alpha系数评价两种仪器测量Kappa角的重复性;t 检验比较两种仪器测量结果的差异,Pearson相关分析其相关性;Bland-Altman图评估两种仪器测量结果的一致性。结果:3次重复测量时,两种仪器均表现出很好的重复性,两种仪器测得Kappa角差异无统计学意义( X 值: P =0.17;Y 值: P =0.61),Pearson相关分析表明Kappa角大小具有相关性(X 值: r =0.90, P <0.01;Y 值: r =0.91, P <0.01)。Bland-Altman图显示 X 值和 Y 值95%一致性区间分别为-0.11~0.14mm和-0.10~0.11mm。结论:Pentacam眼前节分析仪和Keratron Scout角膜地形图仪测量角膜屈光手术患者Kappa角重复性好,两种仪器测量的Kappa角结果一致性好,可以相互验证。展开更多
Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is amo...Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.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.
文摘目的:探讨Pentacam眼前节分析仪和Keratron Scout角膜地形图仪测量Kappa角的一致性,评价两种仪器测量结果的重复性。方法:前瞻性随机对照研究。选取2018-01-01/30在我院眼视光中心行近视术前检查的患者69例,所有受试者由同一检查者分别采用Pentacam和Keratron Scout重复测量3次,以(X ,Y)坐标形式记录Kappa角的大小,采用组内相关系数(ICC)、Cronbach s Alpha系数评价两种仪器测量Kappa角的重复性;t 检验比较两种仪器测量结果的差异,Pearson相关分析其相关性;Bland-Altman图评估两种仪器测量结果的一致性。结果:3次重复测量时,两种仪器均表现出很好的重复性,两种仪器测得Kappa角差异无统计学意义( X 值: P =0.17;Y 值: P =0.61),Pearson相关分析表明Kappa角大小具有相关性(X 值: r =0.90, P <0.01;Y 值: r =0.91, P <0.01)。Bland-Altman图显示 X 值和 Y 值95%一致性区间分别为-0.11~0.14mm和-0.10~0.11mm。结论:Pentacam眼前节分析仪和Keratron Scout角膜地形图仪测量角膜屈光手术患者Kappa角重复性好,两种仪器测量的Kappa角结果一致性好,可以相互验证。
基金supported by the National Natural Science Foundation of China(60573159)
文摘Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.
