基于粗集的神经网络在目标类型识别中的研究

为克服传统的目标类型识别方法的不足,提出将粗集和神经网络紧耦合建立新的识别模型,即经过识别信息预处理、样本数据粗集方法简化、神经网络学习训练及待识信息网络识别等步骤,充分融合了粗集强大的规则提取能力和神经网络优良的分类能力。实验表明,该模型减少了识别的主观因素,简化了神经网络结构,提高了运算速度。     

Machine scheduling with pickup and delivery

The coordination of production, supply, and distribution is an important issue in logistics and operations management. This paper develops and analyzes a single‐machine scheduling model that incorporates the scheduling of jobs and the pickup and delivery arrangements of the materials and finished jobs. In this model, there is a capacitated pickup and delivery vehicle that travels between the machine and the storage area, and the objective is to minimize the makespan of the schedule. The problem is strongly NP‐hard in general but is solvable in polynomial time when the job processing sequence is predetermined. An efficient heuristic is developed for the general problem. The effectiveness of the heuristic is studied both analytically and computationally. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

A model to predict the residual life of aircraft engines based upon oil analysis data

This paper reports on a study using the available oil monitoring information, such as the data obtained using the Spectrometric Oil Analysis Programme (SOAP), to predict the residual life of a set of aircraft engines. The relationship between oil monitoring information and the residual life is established using the concept of the proportional residual, which states that the predicted residual life may be proportional to the wear increment measured by the oil analysis programmes. Assuming such a relationship between wear and the residual life exists, we formulated a recursive prediction model for the item's residual life given measured oil monitoring information to date. A set of censored life data of 30 aircraft engines (right censored due to preventive overhaul) along with the history of their monitored metal concentration information are available to us. The metal concentration information includes many variables, such as Fe, Cu, Al, etc.; not all of them are useful, and some of them may be correlated. The principal component analysis (PCA) has been adopted to reduce the dimension of the original data set and to produce a new set of uncorrelated variables, which we shall use in the prediction model. The procedure associated with estimating model parameters is discussed. The model is fitted to the actual SOAP data from the aircraft engines, and the goodness‐of‐fit test has been carried out. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

二值化联合变换相关器识别复杂背景目标时的阈值选取

讨论了利用二值化联合变换相关器对复杂背景目标进行相关识别的阈值选取问题。由于不同阈值对二值化联合变换相关器识别结果有很大的影响,阈值过高,会损失过多的联合变换功率谱中的有效成份,使相关峰减弱;阈值过低,会使联合功率谱中的噪声放大,相关峰可能被淹没在噪声之中。因而存在一个最佳阈值。实验表明,最佳阈值位于联合功率谱最大灰度值的40%～50%之间,在此阈值范围内,相关峰值较高,噪声也得到有效抑制。     

利用FFT算法模拟联合变换相关器

由于联合变换相关器结构灵活,易于实现相关运算,目前在诸多领域得到了广泛的应用。实际应用中,联合变换相关器的光学系统带来的噪声会对识别效果产生较大的影响。采用快速傅里叶变换算法,由计算机模拟光学联合变换相关器可消除光学系统带来的噪声,达到理想的识别效果。通过实验验证了该方法的可行性。     

基于多目标模糊决策模型的联合作战方案优选

为选择作战方案中的最优方案,对所有的方案进行了排序,依据定性准则和定量的数学模型,利用多目标模糊决策分析方法,对联合作战方案的相关因素进行了分析,综合评价出各方案的优劣程度,最后得到了较为合理的作战方案先后顺序.     

高层建筑消防登高面设计现状与对策研究

就高层建筑消防审核中消防登高面、登高场地设计现状和存在问题进行归纳分析,提出了加强审核、验收及监督工作;分段设置消防登高面;科学计算裙房高度和深度;形成有机整体;确保原消防设计和功能等对策。     

基于粗集理论证据加权的电子目标识别法

在对电子目标进行识别时,往往采取多传感器融合的D2S证据理论进行处理,但是由于传统的D2S证据理论中各传感器对识别结果的重要性没有区分,基于此将粗集理论属性重要度概念应用到各传感器的重要性上,从而实现加权融合的证据理论。仿真实验及其结果表明该方法对电子目标识别是有效的,尤其在传感器受到干扰时,具有较强的现实意义。     

一种基于改进的ROUSTIDA算法的数据补齐方法

针对ROUSTIDA数据补齐方法存在的问题,提出了一种改进的ROUSTIDA数据补齐方法.该方法首先将条件属性与决策属性区分对待,优先填补决策属性值,避免了不一致决策表的产生;然后,在决策属性相同的前提下填补条件属性缺失值,扩展了原算法的使用范围;最后,通过一个实例说明改进算法补齐后的决策表的确能够获得更集中的决策规则...     

基于物元模型-AHP的联合电子防空作战效能评估

针对现代防空作战训练中联合电子防空作战效能评估的问题,引入物元模型,并结合层次分析法(AHP)提出了基于物元模型-AHP的联合电子防空作战效能评估方法.以某次复杂电磁环境下联合电子防空演习任务为背景,建立了作战效能评估指标体系和评估模型,给出了评估模型的仿真算例,结果表明,该方法在联合电子防空作战的效能评估中是可行和有...