Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

Text event mining, as an indispensable method of text mining processing, has attracted the extensive attention of researchers. A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper, i.e. UKGE-MS. Specifically, UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information, and solves the problems of traditional unsupervised feature selection methods, which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples. Firstly, considering the influence of local information of samples in feature correlation evaluation, a feature clustering algorithm based on average neighborhood mutual information is proposed, and the feature clusters with certain event correlation are obtained; Secondly, an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation, so as to enhance the generalization ability of the selected feature items. Finally, the events knowledge graph is constructed by means of sparse representation and l₁ norm. Extensive experiments are carried out on five real datasets and synthetic datasets, and the UKGE-MS are compared with five corresponding algorithms. The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection, and has some advantages over other methods in text event recognition and discovery.     

基于数据挖掘的海战场态势可视化平台构建

可视化和数据挖掘是进行海战场态势评估、实现战场可视化的两项关键技术。海战场态势可视化能提高指挥员的战场态势感知能力,有效辅助指挥员进行当前态势评估和未来态势预测。而数据挖掘技术是对海量战场态势数据进行知识发现的有效工具,基于数据挖掘技术从顶层构建了海战场态势数据可视化挖掘平台,并对其中的各子模块进行了具体研究。     

基于入侵性野草优化算法的平面天线阵列的方向图综合

针对入侵性野草优化(invasive weed optimization,IWO)算法存在早熟收敛和陷入局部最优的问题,提出了一种混沌自适应IWO(chaotic adaptive IWO,CAIWO)算法,并将改进的算法应用于平面阵列天线的方向图综合问题。计算结果表明:该算法的优化结果均优于对比文献中的结果,更适合于阵列天线方向图综合问题。     

基于网格的聚类挖掘算法

为了提高海量数据挖掘效率,研究了一种基于网格环境下的分布式聚类（Prejudge-Based Distributed Clus-tering,PBDC）算法,并引入距离、模和内积的概念,在聚类之前进行预判断,减少了不必要的计算开销。在此基础上提出了一种分布式并行化聚类（Distributed Parallel Clustering,DPC）算法,将其嵌入到Weka4ws中,以开源数据挖掘类库Weka为底层支持环境,构建网格环境下的分布式数据挖掘体系,同时进行仿真实验。实验结果表明：该算法对于网格环境下海量数据的分布式聚类具有良好的效果。     

在数学教学中实施情境教学模式推进数学与军事学科的交叉融合

在现代教学模式中,单科独进、难于聚合的现象非常严重,各学科的知识相互脱节。为此,学员对所学的各科知识不能整合运用,从而束缚了学员的综合创新能力的培养,也限制了学员综合素质的提高。在论述单科独进教学模式弊端同时,提出了情境教学模式,并举例说明了教员在概率教学过程中,如何实施情境模式教学,来推进文化课与军事课的交叉融合,为以后的专业教学打下良好的基础,从而使学员的综合素质及解决实际问题的能力得以提高,促使他们在以后的军事问题研究中多出成果．     

Brill流型的脉冲防暴水炮发射管内气液两相流流型

搭建了脉冲防暴水炮气液两相流动力研究的模化实验平台,对水炮发射管进行可视化研究的改装,通过高速摄影机来拍摄发射管内气液两相流气液界面运动的行为。简化了水炮的三维物理模型,应用OpenFOAM软件自带的大涡数值模拟模型(LES)对发射管内气液两相流进行流场仿真,得到了流型的演变过程。数值模拟结果显示脉冲防暴水炮发射管内气液两相动力过程的流型演变规律为:开始阶段为冲击流,但持续时间极为短暂;然后演变为环状流,持续时间较长;最后为气液掺混阶段的雾状流。数值模拟结果与实验结果吻合良好。     

联合编队作战行动方案建模

研究联合编队的作战行动方案问题,首先需要建立合理的任务模型。在基本作战样式基础上,针对联合编队任务特点,设计了一种联合编队作战行动方案的建模方法。该模型包含对海攻击和对空防御等5种基本作战样式,结合规划和调度两方面的特点,提供了资源和系统状态的描述机制。实例证明建模方案不仅具有可行性,而且丰富了作战行动方案的建模理论,对联合编队作战系统的研究、决策和建设具有一定的参考价值。     

一阶超对策模型中结局偏好认知信息集结的模式识别法

针对一类一阶n人超对策模型,提出了一种结局偏好认知信息的集结方法.首先通过对单一局中人中不同专家给出的对其他局中人结局偏好信息的不同认知进行关联分析,确定出专家的权重评价值,进而运用模糊模式识别原理,通过建立一个非线性规划模型,给出了一种新的超对策结局偏好认知信息的集结方法.最后,用一个数值例子对文中所提方法进行了说明.     

一种新型的OFDM智能天线

根据自适应天线阵列理论,结合给定的参考波束的误差,引入虚拟干扰的概念,对目标波束图形状进行调整,提出一种新的可以应用于任意类型天线阵列的波束综合算法.应用提出的新算法,在主瓣和旁瓣位置都可以对波束进行有效的调节.最终获得阵列的最优权矢量,能够最小化目标波束图与参考波束图间的差异.理论分析与仿真结果表明,与现有的同类算法相比,该算法能更有效地获得与参考波束基本相符的波束.应用于OFDM智能天线系统时,对不同子载波频率上信号进行单独处理,利用该算法进行波束综合,能够在整个有效频段,使所有子载波上获得基本一致的阵列输出.     

A misclassification cost‐minimizing evolutionary–neural classification approach

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)‐based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real‐world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back‐propagation artificial neural network, integrated cost preference‐based linear mathematical programming‐based minimize squared deviations, linear integrated cost preference‐based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost‐minimizing approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.