一种抗拜占庭攻击的随机网络编码

网络编码自提出以来得到了巨大的发展,并且在许多方面被广泛应用。网络安全是网络编码的重要的应用领域之一。安全网络编码最主要的两方面就是防窃听和防拜占庭攻击,实质上就是防搭线窃听与网络纠错。本文构造了一种基于范德蒙德行列式的随机网络编码,并通过添加Target位的方法来实现抗拜占庭攻击;对于同时具备窃听和修改能力的攻击者,设计了一种改进的借助数据位的Target位构造方法;针对攻击者可能仅修改部分数据的具体情况给出了一种变步进多Target位的解决方案,即通过构造步进函数的方式来选取多个数据,构造多个Target位来提高数据安全性,并且通过部分数据验证了其可行性。     

Optimal linear estimation for continuous stochastic systems with random observation delays

This paper is concerned with the linear minimum mean square error estimation for Itô‐type differential equation systems with random delays, where the delay process is modeled as a finite‐state Markov chain. By first introducing a set of equivalent delay‐free observations and then defining two reorganized Markov chains, the estimation problem of random delayed systems is reduced to the one of delay‐free Markov jump linear systems. The estimator is derived by using the innovation analysis method based on the Itô differential formula. And the analytical solution to this estimator is given in terms of two Riccati differential equations that are of finite dimensions. Conditions for existence, uniqueness, and stability of the steady‐state optimal estimator are studied for time‐invariant cases. In this case, the obtained estimator is very easy to implement, and all calculation can be performed off line, leading to a linear time‐invariant estimator. Copyright © 2011 John Wiley & Sons, Ltd.     

一种相控阵天线安装精度检测与校准

文中论述了一种相控阵雷达天线安装后的精度检测方法。按照雷达天线安装后的几何形态,采用光学测量仪器进行相关的数据采集,建立检测的数学模型,利用线性回归法以及最小二乘法进行数据拟合,计算出安装后雷达天线底面与水平面的最大夹角和天线边缘与正北方向的夹角,并进行了试验误差评估和分析,依据检测数据对雷达天线的水平和方位角度进行校...     

用聚类预报模型预报三峡水库来水量的探讨

为了开展三峡水库入库来水量长期预报,从北半球500 hPa高度场、太平洋海温场、北半球100 hPa高度场、北半球地面气压场与74项环流特征指数作为预报因子,采用回归方程求解和回归效果检验,建立回归聚类预报物理模型,对三峡水库6～8月各月平均来水量进行了长期预报分析。结果表明,三峡水库6~8月份各月来水量回归聚类预报的技术路线是可行的,正、负距平符号一致的概率能达到80%以上,效果较好,基本上可以满足生产需要。回归聚类预报物理模型具有一定的应用推广价值。     

Nonlinear model predictive control with relevance vector regression and particle swarm optimization

In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable random exploration velocity （PSO-CREV） is applied to a catalytic continuous stirred tank reactor （CSTR） process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function （RBF） kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control （MPC） using a deterministic sparse kernel learning technique called Least squares support vector machines （LS-SVM） regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.     

基于无线网络的森林防火系统的研究与改进

论文针对现行的无线传感器技术方式的森林防火系统中的弊端,提出了一种改进的系统设计方案.改进后的方案主要采用二级Ad-Hoc模式的网络,对传统的无线传感器进行有效的改造的同时,也增加了稳定可靠的中心网络节点,无线通信网络采用改进后的AODV作为多跳路由协议,中心节点的数据处理采用卡尔曼滤波预处理,数据发送采用ALOHA机制进行随机延迟发送.系统经过反复实验,最终验证并总结出一套可以有效应用于实际工程中的系统部署方案.     

基于LMS自适应算法的随机信号去噪仿真与分析

分析了LMS自适应滤波的基本原理和固定步长算法,介绍了Auan方差分析法。基于Matlab实现了滤波系统的搭建,并针对某光纤陀螺原始数据设计了仿真实验,得到固定步长标度因数的可行取值范围为0．2～0．9。利用仿真实验比较了LMS自适应算法与小波分析、均值滤波的去噪效果,采用Allan方差给出了误差源分析。结果表明LMS自适应滤波能有效降低角度随机游走等随机噪声。     

Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.     

参数自适应的条件随机场视频分割方法

针对已有算法需要采用一系列参数经验值得到最优视频分割结果的问题,提出根据视频特征自适应地计算视频邻域关系的特征强度函数,构造参数自适应的条件随机场视频分割模型。算法的核心思想是采用视频中像素之间的邻域关系自适应计算各个模型的特征函数,通过条件随机场模型对这些特征能量函数进行约束并利用Gibbs采样算法对该模型进行求解,获得全局优化的分割结果。针对不同环境下的视频分割实验表明,该算法能够很好地逼近最优经验参数所得到的视频分割结果,从而避免定义经验值所导致的算法局限性问题。     

信息安全评估算法研究

针对目前各企业在网络信息安全测评中采用的对核查结果统计处理算法中的缺点,通过对计算机安全特点的研究,对比通常采用的加权算术平均法中权数的选取,本文提出采用多元线性回归算法求解加权算术平均法中的权数。该算法可以在使用的过程中减少人为因素对评估结果的影响,提高评估结果的可信度。