基于雷达高度计-星敏感器的卫星自主导航精度分析

考虑到采用多个雷达高度计进行卫星自主定位的不足,提出采用单个雷达高度计,结合星敏感器提供的测量信息,构成雷达高度计—星敏感器的卫星组合自主导航系统。通过大量的仿真实验,分析了地形起伏、可见导航星数目、星敏感器精度、地平仪精度等因素对定位精度的影响,并总结其变化规律,对提高卫星自主定位精度有重要的借鉴意义。     

两种典型网络中三种病毒传播模型的传播特性研究

传染性疾病在人类社会的流行,计算机蠕虫病毒在Internet上的频频爆发,都给人类社会造成了巨大的损失。因此,病毒传播研究一直是国际上科学家所关注的焦点。近年来兴起的复杂网络研究为人类认识病毒传播特征、抑制和防御病毒传播提供了一条新的途径。本文研究了两种典型网络中三种病毒传播模型的传播特性。     

SIS model of epidemic spreading on dynamical networks with community

We present a new epidemic Susceptible-Infected-Susceptible (SIS) model to investigate the spreading behavior on networks with dynamical topology and community structure. Individuals in themodel are mobile agentswho are allowed to perform the inter-community (i.e., long-range) motion with the probability p. The mean-field theory is utilized to derive the critical threshold (λ _C ) of epidemic spreading inside separate communities and the influence of the long-range motion on the epidemic spreading. The results indicate that λ _C is only related with the population density within the community, and the long-range motion will make the original disease-free community become the endemic state. Large-scale numerical simulations also demonstrate the theoretical approximations based on our new epidemic model. The current model and analysis will help us to further understand the propagation behavior of real epidemics taking place on social networks.     

Periodic solutions of an SIS epidemic model with fixed-time birth pulses and state feedback pulse treatments

Fixed-time pulse and state pulse, which were discussed separately in previous works, are introduced in a susceptible-infected-susceptible (SIS) epidemic model in this paper. Periodic solutions of the SIS epidemic model with fixed-time birth pulses and state feedback pulse treatments are discussed by means of both theoretical and numerical ways. The conditions for the existence of epidemic period-(1, 1), period-(2, 1), and period-(3, 2) solution are investigated.     

多相活动轮廓模型在卫星云图分割中的应用研究

应用基于水平集的多相活动轮廓模型对云图进行多类别分割,云图被自动分割成几个区域,不同区域就对应着不同的云顶高度,区域分割结果可以使对一幅云图中不同高度云的分布以及哪种类型的云占主体有总体的认识和了解,从而对天气系统的分析具有一定的辅助参考作用。     

WCDMA卫星移动通信RAKE接收机性能分析

研究了基于WSSUS模型的UHF频段卫星移动信道模型,利用该模型在四种典型的信道环境下进行相干RAKE接收,在搭建的RAKE接收仿真平台上,比较了三种不同RAKE合并方式下的误码率性能.仿真结果表明,公共导频符号信道估计能有效地克服传统数据符号中时分插入导频符号传输方式在高速运动和快速变化信道中估计不准确的问题,特别在机载信道环境下,接收机性能有明显提高,同时不会产生多余的时隙延迟,信道跟踪实时性强,算法简单.     

Remote health monitoring adoption model based on artificial neural networks

The purpose of this research is to utilize the adoption model of remote health monitoring established by artificial neural networks (ANNs). The adoption model by the naming is the healthcare information adoption model (HIAM) that it is created first time by myself. The HIAM focused on citizens in Taiwan as research subjects. The main research result showed that people’s perceived usefulness and benefits (PUB) must be raised in order to effectively increase the adoption of remote health monitoring. Moreover, this research has proved that the utilization of the adoption model of remote health monitoring established by ANN based on the HIAM is feasible. These findings may offer significant reference for subsequent studies.     

基于主成分分析的代谢网络中心化

中心化分析有助于识别复杂网络中的重要节点,已经被广泛应用于代谢网络研究中。当前,人们已经提出了多种中心化指标,然而如何合理地综合使用它们是一个严峻的挑战。本文使用主成分分析来整合多种中心化方法。首先简单介绍了主成分分析的基本概念及其原理等,随后构造了人类代谢网络的巨强连通成分,并使用10种中心化指标计算了该模型中各代谢物的中心化值作为样本进行主成分分析。最后,我们以第一主成分为例,论证了主成分分析可以合理地整合多种中心化方法用于代谢网络研究。     

基于神经网络的热电偶特性数学模型

简单介绍了当前热电偶应用存在的问题 ,分析了建立热电偶特性的数学模型的各种方法 ,并提出了应用前向多层神经网络建立热电偶特性数学模型的方法及其优势。文中对前向多层神经网络及其算法进行了深入剖析 ,并给出了MATLAB源代码。最后以镍铬 -镍硅热电偶为例 ,阐述了其特性数学模型的建立过程及其应用。     

Noise analysis of robot manipulator using neural networks

Due to a lot of robot manipulators application in industry, low noise degree is very important criteria for robot manipulator's joints. In this paper, joint noise problem of a robot manipulator with five joints is investigated both theoretically and experimentally. The investigation is consisted of two steps. First step is to analyze the noise of joints using a hardware and software. The hardware is a part of noise sensors. The second step; according to experimental results, some neural networks are employed for finding robust neural noise analyzer. Five types of neural networks are used to compare each other. From the results, it is noted that the proposed RBFNN gives the best results for analyzing joint noise of the robot manipulator.     

基于VSLMS-Rake模型的高阶多径BOC信号捕获方法

针对高阶多径BOC信号捕获存在模糊性的问题,提出了一种基于VSLMS-Rake模型的捕获方法;该方法首先将接收的多径BOC信号经过VSLMS-Rake模型,然后再对处理后的信号进行功率谱估计;通过二者结合,不仅可以抑制多径干扰增强信号能量,还可以减小估计误差提高捕获概率,最终实现对高阶多径BOC信号的精确捕获;理论和仿真表明,在较低信噪比下,该方法优于现有其它捕获算法.     

Robustness analysis of genetic regulatory networks affected by model uncertainty

A fundamental problem in systems biology consists of investigating robustness properties of genetic regulatory networks (GRNs) with respect to model uncertainty. This paper addresses this problem for GRNs where the coefficients are rationally affected by polytopic uncertainty, and where the saturation functions are not exactly known. First, it is shown that a condition for ensuring that the GRN has a globally asymptotically stable equilibrium point for all admissible uncertainties can be obtained in terms of a convex optimization problem with linear matrix inequalities (LMIs), hence generalizing existing results that mainly consider only the case of GRNs where the coefficients are linearly affected by the uncertainty and the regulatory functions are in SUM form. Second, the problem of estimating the worst-case convergence rate of the trajectories to the equilibrium point over all admissible uncertainties is considered, and it is shown that a lower bound of this rate can be computed by solving a quasi-convex optimization problem with LMIs. Third, the paper considers the problem of estimating the set of uncertainties for which the GRN has a globally asymptotically stable equilibrium point. This problem is addressed, first, by showing how one can compute estimates with fixed shape by solving a quasi-convex optimization problem with LMIs, and second, by deriving a procedure for computing estimates with variable shape. Numerical examples illustrate the use of the proposed techniques.     

Derivation of surface topography and terrain parameters from single satellite image using shape-from-shading technique

This paper evaluates and compares two area-based shape-from-shading algorithms for deriving detailed surface topography and terrain parameters. A Fourier transform-based linear algorithm and an iterative minimization algorithm with integrability constraint are devised and implemented using C++ programming language, and applied to a SPOT panchromatic image. The relative height measurements from the shape-from-shading technique are calibrated into an absolute elevation model using a parametric least-squares adjustment procedure based on a number of ground control points. Surface slope, orientation, and structural lines are also calculated based on the digital elevation model derived from the shape-from-shading technique. The comparative advantages of the two algorithms are discussed, and the combination of two algorithms is also explored. The fundamental requirement for the shape-from-shading algorithms is that the terrain surface under investigation has a relatively homogenous land cover. Source codes in C++ and test data sets are available.     

Consistent merging of satellite ocean color data sets using a bio-optical model

While many (and more on the way) ocean color satellite sensors presently provide routine observations of ocean biological processes, limited concrete effort has taken place to demonstrate how these data can be used together in any systematic way. One obvious way is to merge these data streams together to provide robust merged climate data records with measurable uncertainty bounds. Here, we present and implement a formalism for merging global satellite ocean color data streams to produce uniform data products. Normalized water-leaving radiances (L_wN(λ)) from SeaWiFS and MODIS are used together in a semianalytical ocean color merging model to produce global retrievals of 3 biogeochemically relevant variables (chlorophyll, combined dissolved and detrital absorption coefficient, particulate backscattering coefficient). The model-based merging approach has various benefits over techniques that blend end products, such as chlorophyll concentrations; (1) merging at the level of water-leaving radiance ensures simultaneity and consistency of the retrievals, (2) it works with single or multiple data sources regardless of their specific bands, (3) it exploits band redundancies and band differences, (4) it can account for the uncertainties of the incoming L_wN(λ) data streams and, (5) it provides confidence intervals for the derived products. These features are illustrated through several examples of ocean color data merging using SeaWiFS and MODIS Terra and Aqua L_wN(λ) imagery. Compared to each of the original data source, the products derived from the merging procedure show enhanced global daily coverage and lower uncertainties in the retrieved variables.     

A new learning method using prior information of neural networks

In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering interrelated weights values. Thus, the training results are usually not good. The reason for this in that each parameter has its influence on others during learning. To overcome this problem, we first give an exact mathematical equation that describes the relation between weight values given a set of data conveying prior information. The we present a new learning method that trains part of the weights and calculates the others using these exact mathematical equations. This method often a priori keeps the given mathematical structure exactly the same during learning; in other words, training is done so that the network follows a predetermined trajectory. Numerical computer simulation results are provided to support this approach. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999.     

基于递归神经网络的网络流量组合预测模型

为了提高网络流量的预测精度,提出了一种基于Elman递归神经网络,小波和自回归的网络流量组合预测模型.对流量时间序列进行小波分解,得到小波变换尺度系数序列和小波系数序列,对具有平稳特征的尺度系数序列用AR模型进行预测:而对体现了网络流量非线性、非平稳特性的小波系数序列使用Elman递归神经网络进行预测,最后通过Mallat算法重构得到网络流量的预测值.     

浅析信息和网络安全体系模型

信息技术和网络安全在网络安全技术领域颇为人们关心,随着信息技术和网络水平的不断发展,信息和网络安全成为人们最注重的问题.本文就对信息和网络的安全体系模型进行了简单的探讨.     

A new learning method using prior information of neural networks

In this paper, we present a new learning method using prior information for three-layered neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their interrelation of weight values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give an exact mathematical equation that describes the relation between weight values given by a set of data conveying prior information. Then we present a new learning method that trains a part of the weights and calculates the others by using these exact mathematical equations. In almost all cases, this method keeps prior information given by a mathematical structure exactly during the learning. In addition, a learning method using prior information expressed by inequality is also presented. In any case, the degree of freedom of networks (the number of     

Reliability analysis of a satellite structure with a parametric and a non-parametric probabilistic model

The reliability of a satellite structure subjected to harmonic base excitation in the low frequency range is analyzed with respect to the exceedance of critical frequency response thresholds. Both a parametric model of uncertainties and a more recently introduced non-parametric model are used to analyze the reliability, where the latter model in the present analysis captures the model uncertainties.With both models, the probability of exceedance of given acceleration thresholds is estimated using Monte-Carlo simulation. To reduce the computational cost of the parametric model, a suitable meta-model is used instead.The results indicate that for low levels of uncertainty in the damping, the non-parametric model provides significantly more pessimistic - and hence conservative - predictions about the exceedance probabilities. For high levels of damping uncertainty the opposite is the case.