Forecasting magnetopause crossing locations by using Neural Networks

Given the highly complex and nonlinear nature of Near Earth Space processes, mathematical modeling of these processes is usually difficult or impossible. In such cases, modeling methods involving Artificial Intelligence may be employed. We demonstrate that data driven models, such as the Neural Network based approach, shows promise in its ability to forecast or predict the behavior of these processes. In this paper, modeling studies for forecasting magnetopause crossing locations are summarized and a Neural Network algorithm is presented to describe the nonlinear time-dependent response of the subsolar region of the magnetopause to varying solar wind conditions. In our approach the past history of the solar wind has, for the first time to the best knowledge of the authors, been included in forecasting the subsolar region of the magnetopause. It is proposed that the data driven approach is a valid approach to understanding and modeling the physical phenomena of Near Earth Space. The only basic requirement for the data driven approach is the availability of representative data for the phenomena. The objective of this paper is to demonstrate that by using WIND and GEOTAIL satellite data a Neural Network based model can be adapted to the modeling of the Earth’s magnetopause.     

Automatic landing system using neural networks and radio-technical subsystems

The paper focuses on the design of a new automatic landing system(ALS) in longitudinal plane; the new ALS controls the aircraft trajectory and longitudinal velocity. Aircraft control is achieved by means of a proportional-integral(PI) controller and the instrumental landing system– the first phase of landing(the glide slope) and a proportional-integral-derivative(PID) controller together with a radio-altimeter – the second phase of landing(the flare); both controllers modify the reference model associated with aircraft pitch angle. The control of the pitch angle and longitudinal velocity is performed by a neural network adaptive control system, based on the dynamic inversion concept, having the following as components: a linear dynamic compensator, a linear observer, reference models, and a Pseudo control hedging(PCH) block. The theoretical results are software implemented and validated by complex numerical simulations; compared with other ALSs having the same radio-technical subsystems but with conventional or fuzzy controllers for the control of aircraft pitch angle and longitudinal velocity, the architecture designed in this paper is characterized by much smaller overshoots and stationary errors.     

Prediction of ionospheric total electron content using adaptive neural network with in-situ learning algorithm

The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results.     

基于改进BP算法的复杂武器系统健康状态评估CSCD

根据复杂武器系统指标体系的确定原则:系统性原则、完整性原则、独立性原则、可比性原则、可操作性原则和定性与定量相结合的原则,建立了某复杂武器系统健康指标体系;分析了基本BP神经网络的优点及BP算法的缺陷,研究了一种改进的BP算法;建立了某复杂武器系统健康管理BP神经网络结构;对某复杂武器系统健康状态进行了评估,验证了指标体系的合理性。     

运动工件上字符识别系统研究

在恶劣的工业现场环境中，拍摄的工件图像会失真，为此提出了在一定噪声和模糊范围内能够正确识别字符标号的方法。工件在传送过程中可能发生偏移，使得被测件与摄像镜头的相对位置不确定，所获得的图像会发生偏转或平移，为此提出了解决字符的平移，缩放及旋转问题的方法；设计了神经网络识别系统。     

基于神经网络的飞机襟翼机构磨损失效判据确定方法

飞机襟翼使用过程中存在失效判据不易确定的问题,目前襟翼机构滑轨滑轮磨损间隙许用量主要是通过试验及使用统计数据分析确定。针对新研机构缺乏试验及使用数据的问题,通过对影响磨损间隙最大许用量的因素分析,提出应用神经网络学习确定襟翼滑轨滑轮间隙最大许用量的预测方法。算例验证表明,运用神经网络方法预测得到的滑轨滑轮架的间隙最大许...     

经济视角下的航空货运网络结构分析

随着经济的快速发展和信息流通的加快，航空货运在经济发展中的作用日益凸显。航空货运的发展依赖于货运网络的完善，体现出构建中国航空货运网络的重要性。以中国城市经济发展形态为切入点，以中国各城市间航段货邮运输量为样本数据，绘制出2004--2009年中国航空货运网络结构图，建立航空货运网络核心一边缘结构模型．并对其平均路径长度、网络集聚系数、度分布、边权分布和点权分布进行分析。结果表明：中国航空货运网络具有聚集效应，对经济发展具有一定的推动作用，并在此基础上呈现中国航空货运网络的进一步完善发展方向．     

基于片上网络的路由模型研究

主要探讨了片上系统网络目前的发展状况和趋势,研究了片上网络模型的拓扑结构和通信方式以及功耗.着重设计了一个片上网络的路由器模型.此路由器基于2D Mesh网络,有简洁,高效的特点.     

一种电子政务数据交换覆盖网络

数据交换是电子政务应用的核心问题.构建一种应用层数据交换覆盖网络成为解决这类问题的关键.分析了电子政务数据交换的特点,提出了电子政务数据交换覆盖网络eGON(e-Government data exchange Overlay Network)的概念模型,在树型结构组织网络的基础上,引入结点间组织距离和结点适应度概念,提出了一种eGON拓扑结构模型——双尺度异质组织网络模型DSHON(Dual Scale Heterogeneous Organization Network),该模型按照与组织距离和能力适应度2种尺度相关的概率添加快捷边,实现网络性能改善.设计了eGON的总体结构、构建与维护机制、路由维护协议和基于组织距离优先的消息路由算法,并进行了路由算法实验验证.      

基于BP神经网络的纳卫星轨道温度预测

为了实现纳卫星在轨温度的预测,在对纳卫星热系统动态特性模型分析的基础上,建立BP神经网络预测模型实现纳卫星在轨温度的预测.通过分析纳卫星热系统动态特性模型,得到用于BP神经网络预测模型的输入、输出变量以及训练神经网络所需的数据样本.BP神经网络预测模型分别以纳卫星外壳、辐射器、舱内仪器的热流及温度值为神经网络输入、输出,预测纳卫星10s后的轨道温度.经验证,神经网络预测模型预测结果与纳卫星实际轨道温度吻合较好,表明神经网络预测模型是快捷有效的.