基于组合预测模型的某型导弹贮存寿命评估方法

科学确定导弹的贮存寿命对提高部队装备保障能力和战斗力具有重要的现实意义。针对导弹贮存数据具有“少数据”和随机波动性较大特点,建立了灰色GM（1,1）评估模型和马尔可夫模型的组合预测模型,并应川此组合预测模型解决了某型导弹贮存寿命评估的问题。结果表明,该方法对现有数据拟合程度较好,预测精度较高,克服单一预测方法的不足,有效性强,使用价值大。     

基于支持向量机的多类分类算法综述

作为一种新兴的机器学习方法,基于统计学习理论的支持向量机,最初是用来解决二类分类问题的。对于实际中主要遇到的多类分类问题,目前常用的两大类改进推广方法为"分解—重组"法和"直接求解"法。文章对二类方法进行了介绍和分析,指出其优缺点和未来的改进方向。     

民用飞机电传飞控系统功能危害性评估方法研究

民机系统级功能危害性评估（Functional Hazard Assessment, 简称FHA）是指对系统功能进行系统性的综合分析过程,即依据系统功能失效状态对飞机安全性影响严重程度进行评估,从而实现对功能的等级进行识别和分类的过程,是系统顶层关键的设计过程。在分析大量相关资料和实际型号经验的基础上,得出适用于民机电传飞控系统的功能定义、功能失效状态分析和确定功能失效影响等级并对其进行确认的思路和方法,以及保证民机电传飞控系统级FHA正确性和完整性的具体可行的措施等,该方法已应用到某民机电传飞控系统级FHA的评估工作中,取得了良好的效果。     

基于传递闭包法的航班分类模型及在航班排序中的应用

提出了一种基于传递闭包法的进/离场航班分类方法.首先对模糊集合理论和传递闭包算法作了简要的介绍,然后在考虑4种不同因素的条件下建立了航班分类模型,并给出了各类航班单位时间延误成本的计算公式.最后以终端区航班排序模型为例,对该方法进行了仿真,并与传统航班延误成本分类方法进行了比较.仿真结果表明,该方法有助于减少航班延误损失,优化进/离场航班队列,提高空中交通管理效率.     

A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data

The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted ‘k’ nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model.     

Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.     

飞行员头部特征分析及头型分类方法研究

通过对飞行员进行实地调查,发现目前装备的飞行头盔舒适度并不理想。由此,本文介绍了一种新的头部分析方案,借助光栅投影图像测量技术构建的飞行员头部三维模型,通过数值拟合方法分析获得飞行员的头部特征外形参数。同时按照统计学原理,根据头高、头宽、头长尺寸对飞行员的头型进行了科学地分类。本文描述的外形参数分析方法和头型分类方法可以用在今后大规模飞行员头部特征测量分析以及改进头盔设计的工作中。相关标准对今后头盔设计的改进工作有参考意义。     

民用发动机状态混沌预测算法

首先应用Haar小波和DB16小波对航空发动机排气温度的原始数据序列进行去噪处理,并且证明了处理后的数据序列具有混沌特征.其次应用混沌理论建立发动机状态预测算法,实现对排气温度的预测.通过检验排气温度预测值是否超过所规定的红线,以及该曲线是否平稳,从而进行发动机的健康状态排查.作为验证实例,使用一组某机型发动机实际飞行...     

A reflectance spectra-based approach to mapping salt fields using PCA-fused Landsat TM data

In this paper a method based on in situ measured reflectance spectra was developed for accurately mapping salt fields in the Taibei Salt Farm in Lianyungang City of East China. After radiometric correction and geometric rectification, six multispectral ETM+ bands were fused with the panchromatic band via principal components analysis (PCA). The fused data were used to map salt fields in the farm. Salt fields were mapped 91.95% correctly in comparison with ground statistics from raw multispectral bands. This accuracy level rose to 96.4% with the use of the panchromatic band (15 m resolution). However, PCA-fused data produced the highest accuracy of 98.8%. At the zone level, coarse resolution data resulted in an accuracy of 98.93% for crystallization ponds, but only 82.68% for condensation ponds. Use of the panchromatic band alone improved the accuracy for condensation ponds but decreased the accuracy for crystallization and evaporation ponds. Results derived from PCA-fused data are highly consistent with the ground statistics at a discrepancy between 0.35% for evaporation zone and 1.80% for condensation zone. It is concluded that a fine spectral resolution is important to separation of ponds of a varying salt content while a fine spatial resolution is important to their mapped area. Ponds of a lower salt content can be mapped more accurately from fused data than from raw spectral bands.     

基于灰色模型的故障预测方法研究

随着人们对系统发生故障所带来财产损失、人员伤亡以及危害性的无法承受,故障预测与健康管理（PHM）技术应用越来越广泛。介绍了灰色模型,并利用某型号发控设备压控振荡器电路检测积分器输入信号检测的历史数据作为研究依据,验证了灰色模型在故障预测中的应用是可行的,准确度能够达到要求。