基于直觉模糊神经网络的机动事件检测方法

战场事件检测在态势评估的各个推理层次上都起着重要作用,是态势评估的基础.已有文献对简单的战场事件提出了相应检测方法,但并不能满足对复杂多变的现代战场进行态势评估的需求.针对战场事件类型的多样性和发生的频繁性,设计了战场事件体系结构,定义了防空作战中空中目标的战术机动事件,并设计了直觉模糊神经网络对战术机动事件进行检测.仿真实验结果表明了该检测方法的可行性和有效性.     

基于Skyline的战场态势系统研究与实现

传统的战场态势系统主要是基于实物或二维GIS开发,一般停留在二维层面上的展示。本文提出的基于Skyline的战场态势显示,利用Skyline提供的3D控件技术,对战场元素、作战单元进行真实建模,实现了真正意义上的三维战场可视化。同时,采用二、三维联动的方法,更加全面准确地展示战场环境,直观地反映出作战单元的动态性和交互性,为作战指挥及作战决策提供强有力的参考支持。     

一种面向态势估计中分群问题的聚类方法

对目标分群技术问题进行了描述,分群或聚类问题是态势估计需要实现的一个重要功能,主要根据底层融合的结果应用聚类分析法实现战场目标分群。目标分群的结果有助于确定态势元素之间的相互关系,从而解释问题领域的各种行为,辅助指挥决策。提出使用CHAMELEON算法对战场目标或群进行划分,根据相对互连性RI和相对近似性RC所表征的相似度把它们形成更高层次的群。     

多Agent规划识别跟踪模型在态势估计中的应用

1 引言美国联合领导实验室数据融合委员会JDL/DTS定义态势评估为:综合固定和运动物体的配置、环境数据、理论数据和所谓性能数据(如敌方车辆运输能力、传感器观测性能等)以估计和推测敌方作战计划的一种方法,即确定敌人要做什么(行动)、企图达到什么目的(目标)等,从而确定己方的兵力部署。规划识别是根据Agent的行为序列来推断Agent所追求目标的过程,着重于对当前已发生行为的分析和抽象,因而对动态问题有很好的适应性,与态势估计中通过观察、分析战场中军事单元的动态行为来识别其计划的要求相一致,因此,本文在分析态势估计问题本质特征的基础上,采用多Agent规划识别理论来解决态势估计问题。     

态势估计模型的研究与实现

态势估计属于数据融合中的二级融合，它的目标就是在一级融合的基础上，通过对各个作战对象行为、状态、事件、企图及其关系的分析，给出参战各方力量部署、作战能力、效能尽可能准确、完整的军事态势估计和感知，并对战术画面进行解释，辨别敌方意图。但现代战争涉及到的作战对象多、协同关系复杂、战术机动频繁，建立一个完善的态势估计模型存在很多困难。该文提出了基于态势觉察、态势理解和态势预测的三级态势估计功能模型；给出了基于模板的计划识别推理框架，重点讨论了基于多级分层黑板模型在态势估计中的应用。     

基于作战能力估计的通信对抗作战目标决策

针对复杂战场下的通信对抗作战目标决策问题,提出一种基于作战能力估计的解决方法。根据群体决策方法的特点,构建战场态势的动态物元表示模型,设计多层次能力聚合的通信对抗体系作战能力评估方法,给出基于决策风险态度因子的通信对抗作战目标决策方法。分析结果表明,该方法能形式化、统一化、动态化地表示战场的态势信息,易于计算,且能实现对作战目标决策的量化分析。     

释案推理原理及其在态势估计中的应用

态势估计就是从当前相关对象间的关系中推导出有意义的结论,形成态势分析报告、情况判断结论和战场综合态势图,为决策和指挥提供支持.论文提出了基于释案推理的态势估计方法,该方法是“大胆猜测”敌作战意图和行动预案,然后根据实时检测到的事件“小心求证”敌作战意图和行动预案.当事件的发生对“大胆猜测”的结论支持比较小或相矛盾时,放弃原猜测,并根据以往事件作出新的猜测和动态调整战场态势图,达到为瞬息万变的战场决策提供充裕时间的目的,案例仿真证明基于释案推理的态势估计方法是有效的.     

随机环境下编队协同对地攻防对抗决策方法研究

随着现代军事技术的发展,军事作战指挥中的不确定性决策问题成为军事决策支持系统中迫切需要解决的问题。文章以对策论为基础,编队协同对地攻防对抗作战为背景,建立了动态对抗决策模型;针对不确定性军事指挥决策中的随机问题,提出了随机影响因子概念,反映战场随机环境对各参战单元产生的影响,并建立了随机对抗决策模型。仿真结果表明,该算法能够合理地处理战场随机状况,客观分析作战结果,为作战指挥决策提供有力的决策支持,方法实用、有效,具有良好的应用前景。     

基于聚类的海战场目标分群方法

目标分群是态势估计需要解决的一个重要问题.目标分群的结果有助于确定态势元素间的关系,从而为作战决策提供依据.提出了一种海战场环境下目标分群的方法.该方法在考虑目标各运动要素的基础上,对其进行最优权值分配,然后利用Chameleon算法对综合指标值进行聚类.     

潜艇战场目标概略位置态势快速判断及仿真

研究目标战场的辨识优化问题,关于潜艇战场环境中目标的位置态势是潜艇作战的关键要素,能否快速对目标的位置态势做出判断对取得战场主动权起着决定性作用.提出应用灰色系统理论,建立了潜艇战场目标概略位置态势快速判断的总体框架,以改进初值和背景值的GM(1,1)模型分段对目标序列进行预处理,应用灰色趋势关联度,综合考虑目标方位序列之间的距离差异和变化趋势差异,从而得到目标的战场态势.选取适当的样本、并设定样本的误差和收敛条件,进行仿真试验.由仿真结果可以看出,应用灰色系统理论可以在短时间内对目标的位置态势做出较准确的识别,并满足了精度的要求.     

Supervised clustering of label ranking data using label preference information

This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers’ feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.     

Local fuzzy PCA based GMM with dimension reduction on speaker identification

To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with fuzzy clustering. The proposed method firstly partitions the data space into several disjoint clusters by fuzzy clustering, and then performs PCA using the fuzzy covariance matrix on each cluster. Finally, the GMM for speaker is obtained from the transformed feature vectors with reduced dimension in each cluster. Compared to the conventional GMM with diagonal covariance matrix, the proposed method shows faster result with less storage maintaining same performance.     

低层特征获取高层语义的图像检索

首先采用基于颜色聚类的方法将图像分割成区域,提取每个区域的Gabor小波纹理特征和灰度共生矩阵纹理特征,接着采用信息熵对特征进行选择,使用选择后的特征对图像区域进行聚类,得到每幅图像的语义特征向量;然后提出遗传模糊C均值算法对图像进行聚类。在图像检索时,查询图像和聚类中心比较,在距离最小的类中进行检索。实验表明,提出的方法可以明显提高检索效率,提高了检索的精度。     

基于目标区域的语义图像检索

提出了一种基于高层语义的图像检索方法,该方法首先将图像分割成区域,提取每个区域的颜色、形状、位置特征,然后使用这些特征对图像对象进行聚类,得到每幅图像的语义特征向量;采用模糊C均值算法对图像进行聚类,在图像检索时,查询图像和聚类中心比较,然后在距离最小的类中进行检索。实验表明,提出的方法可以明显提高检索效率,缩小低层特征和高层语义之间的“语义鸿沟”。     

Foreground Focus: Unsupervised Learning from Partially Matching Images

We present a method to automatically discover meaningful features in unlabeled image collections. Each image is decomposed into semi-local features that describe neighborhood appearance and geometry. The goal is to determine for each image which of these parts are most relevant, given the image content in the remainder of the collection. Our method first computes an initial image-level grouping based on feature correspondences, and then iteratively refines cluster assignments based on the evolving intra-cluster pattern of local matches. As a result, the significance attributed to each feature influences an image’s cluster membership, while related images in a cluster affect the estimated significance of their features. We show that this mutual reinforcement of object-level and feature-level similarity improves unsupervised image clustering, and apply the technique to automatically discover categories and foreground regions in images from benchmark datasets.     

OPTICS聚类与目标区域概率模型的多运动目标跟踪

目的 针对多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题,本文提出了一种基于OPTICS聚类与目标区域概率模型的方法。方法 首先引入了Harris-Sift特征点检测,完成相邻帧特征点匹配,提高了特征点跟踪精度和鲁棒性;再根据各运动目标与背景运动向量不同这一点,引入了改进后的OPTICS加注算法,在构建的光流图上聚类,从而准确的分离出背景,得到各运动目标的估计区域;对每个运动目标建立一个独立的目标区域概率模型(OPM),随着检测帧数的迭代更新,以得到运动目标的准确区域。结果 多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题通过本文方法得到了很好地解决,Harris-Sift特征点提取、匹配时间仅为Sift特征的17%。在室外复杂环境下,本文方法的平均准确率比传统背景补偿方法高出14%,本文方法能从移动背景中准确分离出运动目标。结论 实验结果表明,该算法能满足实时要求,能够准确分离出运动目标区域和背景区域,且对相机运动、旋转,场景亮度变化等影响因素具有较强的鲁棒性。     

视频数据库的聚类索引方法

理想的视频库组织方法应该把语义相关并且特征相似的视频的特征向量相邻存储.针对大规模视频库的特点,在语义监督下基于低层视觉特征对视频库进行层次聚类划分,当一个聚类中只包含一个语义类别的视频时,为这个聚类建立索引项,每个聚类所包含的原始特征数据在磁盘上连续存储.统计低层特征和高层特征的概率联系,构造Bayes分类器.查询时对用户的查询范例,首先确定最可能的候选聚类,然后在候选聚类范围内查询相似视频片段.实验结果表明,文中的方法不仅提高了检索速度而且提高了检索的语义敏感度.     

基于综合特征的图像分割

文章提出了一种有效的基于颜色和纹理综合特征的图像分割方法。将图像以块为单位进行划分,在YUV空间,提取块的颜色特征和纹理特征,在这种综合特征基础上,采用改进的K均值聚类法进行图像分割。该方法能自适应确定聚类中的参数,且兼顾点的位置连通关系,从而达到了较好的分割效果。     

MC环境下基于加工特征的零件工时定额研究

工时定额是大规模定制生产模式下企业确定产品交货期和提高顾客满意度的重要参考依据。为更好地支持大规模定制生产的企业工时定额的制定,提出一种大规模定制环境下基于加工特征的零件工时定额的制定方法。在此方法中,运用面向对象方法将零件组内的加工特征进行分类、编码,建立加工特征信息模型,以便于零件加工特征的检索;采用神经网络技术,结合MATLAB软件针对编码系统中每一个最底层的加工特征建立对应的工时模型;根据零件加工特征的编码检索各个加工特征的工时模型,估算每个加工特征的加工工时,从而得到整个零件的工时定额。通过与传统的工时定额方法对比,验证了该方法的准确性和快速性。     

A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction

Cross-project defect prediction (CPDP) uses the labeled data from external source software projects to compensate the shortage of useful data in the target project, in order to build a meaningful classification model. However, the distribution gap between software features extracted from the source and the target projects may be too large to make the mixed data useful for training. In this paper, we propose a cluster-based novel method FeSCH (Feature Selection Using Clusters of Hybrid-Data) to alleviate the distribution differences by feature selection. FeSCH includes two phases. The feature clustering phase clusters features using a density-based clustering method, and the feature selection phase selects features from each cluster using a ranking strategy. For CPDP, we design three different heuristic ranking strategies in the second phase. To investigate the prediction performance of FeSCH, we design experiments based on real-world software projects, and study the effects of design options in FeSCH (such as ranking strategy, feature selection ratio, and classifiers). The experimental results prove the effectiveness of FeSCH. Firstly, compared with the state-of-the-art baseline methods, FeSCH achieves better performance and its performance is less affected by the classifiers used. Secondly, FeSCH enhances the performance by effectively selecting features across feature categories, and provides guidelines for selecting useful features for defect prediction.