使用频域极大似然法辨识飞机等效系统参数

在飞机飞行试验的数据处理过程中,采用频域方法是比较方便的。频域极大似然法则是一种有效的方法,它具有频域计算简单,同时估计又是渐进无偏和一致的优点。由于飞行试验数据是时域数据,使用这种方法需要使用FFT的方法将实测的时域数据转换成频域数据后再进行辨识。在军用飞机等效系统参数辨识过程中,取得了很好的计算结果。     

一种基于遗传算法的动力学系统辨识方法

把遗传算法与最大似然法相结合，形成了一种基于遗传算法的动力学系统辨识方法，并以某轴对称战术导弹的非线性气动力参数仿真辨识为例，利用仿真技术，检验了该方法的实用性和有效性。结果表明，该方法是十分有效的。     

极大似然法测定p—S—N曲线的置信度

对极大似然法测定的Ｐ－Ｓ－Ｎ赋予置信度，提出了极大似然法珠Ｐ－Ｓ－Ｎ曲线的置信限曲线公式，通过该公式确定的Ｐ－Ｓ－Ｎ能使估算的疲劳寿命具有置信度一给出了单点法测定出构件Ｐ－Ｓ－Ｎ的数据自理。     

非线性最优解在气动参数辨识中的应用

由于采用最大似然算法进行了扭动参数辨识通常存在着局极值问题，为了寻求非线性全局最优解，提高估计精度，介绍了以混沌优化算法作为搜索工具。与最大似然处算法相结合，求解非凸优化问题全局最优解的方法，算例表明，该方法是有效可行的，并且估计精度也较高。     

分离流中多段翼型的优化计算

 本文给出带有分离流动的多段冀型的优化计算方法。以最大升力系数为目标函数,对前缘缝翼或襟翼的位置和偏角进行优化。文中对带有后缘富勒式襟翼的NLR 7301翼型和GA(W)-1翼型进行最大升力计算和优化计算,所得结果和实验值相比符合得较好。     

具有通信约束的分布式SOR多智能体轨迹估计算法

针对传统多智能体轨迹估计算法信息交换量大，计算量随群规模指数增长，可扩展性差等诸多不足，提出了一种基于超松弛迭代（SOR）的分布式多智能体轨迹估计算法，通过将最大似然（ML）准则下的轨迹估计转化为两级线性优化问题，并综合利用分布式超松弛迭代（Distributed SOR）和标记初始化方法，加快求解速度并简化信息交换流程，最终实现了多智能体位姿轨迹优化和协作定位。实验表明，所提的分布式方法能达到集中式算法的精度水平，在49个智能体规模条件下，位置估计误差小于0.15 m，姿态估计误差小于0.03°，且数据交换量仅到现有主流分布式方法DDF-SAM的0.06%，能很好用于大规模集群的场景。     

Effects of Mo on the Microstructure and Hydrogen Sorption Properties of Ti-Mo Getters

The effects of Mo on the microstructure evolution, porosity and hydrogen sorption properties of Ti-Mo getters are investigated in this work. The results show that the addition of Mo prolongs the densification process of Ti-Mo getters and results in a significant amount of sintered pores. With the Mo content increasing, the porosity of getters firstly increases reaching the maximum value as it at- tains about 7.5wt.%, and then drops. At the room temperature, the hydrogen sorption property of getters increases progressively with the Mo content increasing, but the tendency is not very clear before its content lies below 2.5wt.%. When the Mo content achieves about 7.5wt.%, the hydrogen sorption property proves to be the best. The discussion is made about the above mentioned phenomena inclusive of hydrogen sorption properties of getters under different activation conditions (from 500-750 ℃).     

天津航空旅客流失的主客观原因

在分析天津航空旅客流失的客观因素时,以详细的旅客调查为依托,使用"航线可用座位数"这一参数与"旅客需求"之间的供求关系来阐述天津航空旅客流失的客观原因,通过对流失旅客出行心理的梳理和分析,对于航空旅客流失的原因从主观方面也进行了深入的剖析,找出天津航空旅客流失的深层次原因。     

H6飞机最大起飞质量数学模型的研究

正确确定最大起飞质量，对于轰炸机和大型运输机的起飞安全有着非常重要的意义。确定最大起飞质量，以往多采用作图的方法，由于影响最大起飞质量的因素比较复杂，所以比较准确的数学模型未曾建立。近几年来对这个问题进行了多方面的探讨，得出一个计算Ｈ６最大起飞质量比较准确的数学模型。此数学模型的建立为其它轰炸机、运输机最大起飞质量的计算开辟了一条新路，对工程应用具有一定的参考价值。     

Primary mass discrimination of high energy cosmic rays using PNN and k-NN methods

Probabilistic neural network (PNN) and k-Nearest Neighbors (k-NN) methods are widely used data classification techniques. In this paper, these two methods have been used to classify the Extensive Air Shower (EAS) data sets which were simulated using the CORSIKA code for three primary cosmic rays. The primaries are proton, oxygen and iron nuclei at energies of 100?TeV–10?PeV. This study is performed in the following of the investigations into the primary cosmic ray mass sensitive observables. We propose a new approach for measuring the mass sensitive observables of EAS in order to improve the primary mass separation. In this work, the EAS observables measurement has performed locally instead of total measurements. Also the relationships between the included number of observables in the classification methods and the prediction accuracy have been investigated. We have shown that the local measurements and inclusion of more mass sensitive observables in the classification processes can improve the classifying quality and also we have shown that muons and electrons energy density can be considered as primary mass sensitive observables in primary mass classification. Also it must be noted that this study is performed for Tehran observation level without considering the details of any certain EAS detection array.