基于灰色关联模糊聚类算法优化飞机排故方法

采用灰色关联与模糊聚类原理，建立一种灰色关联的模糊聚类算法，应用该算法可直接对飞机维修经验中的多种排故方法．依排除故障的可能性进行排序，并根据排序结果时排故方法进行相似聚类，从而将排故经验与多因素决策算法进行有效综合．较好地解决了民航飞机故障诊断专家系统中多种故障原因的优化选择问题，并可直接给出排故方法的选择指导，提高专家系统的指导水平和飞机排故效率。文中对算法原理进行了详细介绍，并给出算法实例。     

灰色系统研究进展（2004--2014）

从灰数及其运算、灰色序列算子与灰色预测模型、灰色关联分析与灰色聚类评估模型、灰色决策模型、灰色组合模型和灰色控制几个方面介绍了灰色系统理论2004—2014年的研究进展及部分应用成果。梳理了基于"核"和灰度的灰数运算法则、一般灰数的概念、灰度合成公理及缓冲算子的一般形式,总结了4种GM(1,1)基本模型,即均值GM(1,1)模型(EGM)、离散GM(1,1)模型(DGM)、均值差分GM(1,1)模型(EDGM)、原始差分GM(1,1)模型(ODGM),及其适用的序列类型。介绍了灰色相似关联度、灰色接近关联度、三维灰色绝对关联度,以及基于中心点和端点混合三角白化权函数的灰色聚类评估模型、多目标加权智能灰靶决策模型、两阶段综合测度决策模型、灰色博弈模型、灰色投入产出模型,并且阐述了分布型、中立型和中立-分布型灰色随机时滞系统的指数鲁棒稳定性等新概念、新模型、新方法。同时提出了有待进一步研究解决的问题。     

灰色系统模型研究进展(英文)

针对灰色系统模型的研究进展进行了综述,介绍了一般灰数的概念、灰色序列算子和一些常用的灰色系统模型,包括灰色绝对关联度模型、灰色相对关联度模型、基于端点三角白化权函数的灰色聚类评估模型、基于中心点三角白化权函数的灰色聚类评估模型、GM(1,1)预测模型以及多目标加权灰靶决策模型。     

灰色模型GM (1,2)在机场旅客吞吐量预测中的应用

通过对影响机场旅客吞吐量的主要因素进行灰色关联分析,建立GM(1,2)预测模型,并且根据首都机场旅客吞吐量的实例进行预测,实现了灰色系统理论在机场旅客吞吐量预测中的应用。通过精度的检验以及与实际情况的比较,说明GM(1,2)模型应用于机场旅客吞吐量预测的精度较高,结果可信。     

用于复杂装备费用驱动因子筛选的灰色凸关联模型

针对复杂装备费用预测中样本少和费用影响因素繁多的问题,分析以往灰色关联度的缺陷,运用改进的灰色凸关联度建立费用影响因素与费用之间的灰色关联模型。依据灰色凸关联度的大小和接近程度筛选费用驱动因子,利用所选择的驱动因子建立多元回归模型。与已有文献中的方法进行对比,结果表明本文方法具有较高的预测精度,说明改进的灰色凸关联度模型能够诊断复杂装备费用的关键影响因素。     

科技综合实力评估指标与数学模型

运用灰色系统原理和德尔菲法，建立了省级区域科技综合实力评估指标体系。在灰色定权聚类模型的基础上，提出了一种基于三角白化权函数的评估模型。     

基于灰色系统GM（1，1）模型的疲劳寿命预测方法

将疲劳现象视为存在于一个灰色系统中,提出了运用灰色系统理论,通过建立灰色系统GM(1,1)模型预测构件疲劳寿命的新方法。对某试件的实验数据进行分析和计算。结果表明,该方法使疲劳寿命预测误差由原来的61.4%减小到24.1%,预测结果偏于安全,说明该方法具有较好的预测精度和工程实用价值。     

基于灰色聚类的民航不安全事件风险评价

为提高民航运行安全水平,以2007-2016年民航不安全事件为数据源,选取事故和典型事故征候事件作为民航不安全事件风险评价指标,构建了基于灰色白化权聚类的民航不安全事件风险评价模型,采用最大离差化原理确定指标权重,以实际运行数据对这10年的民航不安全事件的风险进行了评价。结果表明:基于灰色聚类的风险评价结果与民航实际运行情况基本相符,验证了以事故和典型事故征候为指标的民航不安全事件风险评价体系的合理性,对提高民航安全生产能力有积极意义。     

基于灰色关联度和聚类分析的通用航空区域发展能力评价

以2014年中国民航7个地区管理局管辖区域的通用航空发展水平为研究对象,选取9个反映通用航空发展能力的原始变量,采用灰色关联度对各区域的发展综合能力进行了由高到低的排序,最后将研究样本聚为3类分析,画出了树状聚类图。结果表明中南地区通用航空发展居首位,新疆相对落后于其他地区,该评价为各区域通用航空可持续发展提供决策依据。     

一种新的灰色决策模型及其数值仿真(英文)

针对一类权重完全未知且属性值为区间灰数的多指标灰色决策问题,根据灰色理论思想,提出了一种灰色多阶段决策方法。首先给出了一种基于可能度的灰数大小比较方法,然后利用灰色关联分析法对多位专家关于权重的评分结果进行计算得到各指标权重,进而分阶段对指标值以区间灰数的形式进行评分,根据灰色系统新信息优先原理给出了各个阶段评价值的赋权公式。实例验证了该决策方法的可行性。研究结果为求解灰色决策问题提供了一种新思路。     

基于灰色时序的航空发动机磨损故障预测模型

把时序AR(n)预测模型和灰色GM(1,1) 预测模型两者有机结合起来,形成新的灰色时序模型的预测模型.以发动机光谱为例,经过实测数据检验,显示了其更好的预测效果.     

基于灰色GM(1,1)模型和灰关联的数据挖掘方法

根据灰色系统模型的特点,将灰色GM（1,1）模型应用于数据的预处理,把灰关联作为一种直接的数据挖掘工具对数据进行关联分析,结合GM（1,1）模型和灰关联,给出一个完整的数据挖掘算法。     

基于信息熵的危险天气下终端区管制风险评估

终端区结构复杂、飞机事故多，其中危险天气对航空器的安全运行影响极大，因此有必要对危险天气下终端区管制风险进行评估。针对终端区管制系统运行风险性大、易发不安全事件的问题，提出基于风险信息熵的危险天气条件下终端区管制系统风险评估模型。首先，通过分析危险天气条件下管制系统的运行过程，建立管制系统运行中的熵流模型，然后建立危险天气下终端区管制风险评估指标体系，给出评估指标灰色关联度属性的计算方法，利用其反映出的信息熵确定指标权重，最终建立危险天气下终端区管制风险评估模型，并通过算例验证了模型的可用性和有效性。     

基于互信息的图像配准技术研究及应用

介绍了图像配准的基本框架和方法,提出了一种基于灰度信息的配准算法,利用互信息作为图像相似性度量的准则,通过Matlab实验证明了该算法的有效性。     

复杂网络的灰点及灰度分析(英文)

依据对复杂网络和灰色系统理论的相关分析,提出了复杂网络中的灰点问题,认为灰点分为存在而未知的灰点和演化灰点两类,并给出了相关定义。进一步给出复杂网络灰度问题的相关描述,将网络灰度分为相对灰度和绝对灰度,并分别进行了证明。     

Ground-Based Cloud Using Exponential Entropy/Exponential Gray Entropy and UPSO

Objective and accurate classification model or method of cloud image is a prerequisite for accurate weather monitoring and forecast. Thus safety of aircraft taking off and landing and air flight can be guaranteed. Thresholding is a kind of simple and effective method of cloud classification. It can realize automated ground-based cloud detection and cloudage observation. The existing segmentation methods based on fixed threshold and single threshold cannot achieve good segmentation effect. Thus it is difficult to obtain the accurate result of cloud detection and cloudage observation. In view of the above-mentioned problems, multi-thresholding methods of ground-based cloud based on exponential entropy/exponential gray entropy and uniform searching particle swarm optimization （UPSO） are proposed. Exponential entropy and exponential gray entropy make up for the defects of undefined value and zero value in Shannon entropy. In addition, exponential gray entropy reflects the relative uniformity of gray levels within the cloud cluster and background cluster. Cloud regions and background regions of different gray level ranges can be distinguished more precisely using the multi-thresholding strategy. In order to reduce computational complexity of original exhaustive algorithm for multi-threshold selection, the UPSO algorithm is adopted. It can find the optimal thresholds quickly and accurately. As a result, the real-time processing of segmentation of groundbased cloud image can be realized. The experimental results show that, in comparison with the existing groundbased cloud image segmentation methods and multi-thresholding method based on maximum Shannon entropy, the proposed methods can extract the boundary shape, textures and details feature of cloud more clearly. Therefore, the accuracies of cloudage detection and morphology classification for ground-based cloud are both improved.     

一种改进的模糊聚类算法在图像边缘检测中的应用

提出了一种改进的模糊聚类图像边缘快速检测算法,该算法在利用像素灰度值的同时还考虑了像素的空间信息,基于模糊集合理论将图像从灰度空间映射成一个模糊隶属度矩阵,然后将隶属度矩阵中的元素作为样本进行模糊聚类,从而提取出图像边缘。基于热力学原理选取隶属度函数,通过调节温度系数,实现图像边缘由粗到细的提取。实验证明,该方法在计算速度、滤除噪声、提取边缘等方面均优于C-均值聚类算法。     

Threshold Selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional（2-D）histogram region oblique division and artificial bee colony（ABC）optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional（1-D）method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization（NCPSO）,the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.     

Optimized Fuzzy Clustering Method for Health Monitoring of Shield Tunnels

Since health monitoring of shield tunnels generally employs multiple sensors belonging to different types,a fine analysis on massive monitoring data,as well as further quantitative health grading,is really challenging.An optimized fuzzy clustering analysis method based on the fuzzy equivalence relation is proposed for health monitoring of shield tunnels.Clustering results are auto-generated by using fuzzy similarity-valued map.The results follow the idea of unsupervised classification.Moreover,a convenient new health index HI is proposed for a fast tunnel-health grading.A case study on Nanjing Yangtze River Tunnel is presented to validate this method.Three types of indicators,namely soil pressure,pore water pressure and steel strain,are used to develop the clustering model.The clustering results are verified by analyzing the engineering geological conditions;the validity and the efficacy of the proposed method are also demonstrated.Further,the fuzzy clustering analysis also represents a potential for identifying abnormal monitoring data.This investigation indicates the fuzzy clustering analysis method is capable of characterizing the fuzziness of tunnel health,and beneficial to clarify the tunnel health evaluation uncertainties.     

A Rework Reduction Mechanism in Complex Projects Using Design Structure Matrix Clustering Methods

To reduce the uncertainty and reworks in complex projects,a novel mechanism is systematically developed in this paper based on two classical design structure matrix(DSM) clustering methods:Loop searching method(LSM) and function searching method(FSM). Specifically,the optimal working areas for the two clustering methods are first obtained quantitatively in terms of non-zero fraction(NZF) and singular value modularity index(SMI),in which the whole working area is divided into six sub-zones. Then,a judgement procedure is proposed for conveniently choosing the optimal DSM clustering method,which makes it easy to determine which DSM clustering method performs better for a given case. Subsequently,a conceptual model is constructed to assist project managers in effectively analyzing the network of projects and greatly reducing reworks in complex projects by defining preventive actions. Finally,the aircraft design process is presented to show how the proposed judgement mechanism can be utilized to reduce the reworks in actual projects.