乘性和加性噪声中谐波恢复的循环统计量方法

本文从循环平衡的观点出发来研究乘性和加性噪声中的谐波恢复问题.首先，在一定条件下建立了一般复过程的有限长付里叶变换的大样本性质．然后，得到了任意阶循环矩的样本估计关于循环频率的一致收敛速度．对于乘性和加性噪声中的谐波信号，建立了一、二、三阶循环矩样本估计的统计性质．在此基础上，分别提出了基于不同阶循环矩的谐波分最个数和频率的估计方法，并得到了估计的强相容性质和强收敛速度，最后给出了模拟实验结果．     

无线传感网络覆盖中概率Voronoi模型及算法研究

覆盖是无线传感网络中最重要的问题之一,随机覆盖是目前研究的主流。基于Voronoi图的随机覆盖算法是无线传感网络领域的研究热点。目前研究中采用的Voronoi图,主要采用基于距离的Voronoi边赋权值模型,存在两个主要问题,即模型粗糙和监测节点不全。以实测的分段概率传感模型为基础,从多传感协同监测的角度构造一种概率Voronoi模型,试图解决以上问题。并且用基于概率Voronoi模型的最大突破路径算法验证了模型有效性。就掌握的文献来看,该模型是首次提出,具有较好的实用推广价值。     

PARAMETER ESTIMATION IN EXPONENTIAL MODELS

Abstract. In this paper the problems of parameter estimation and order determination of an exponential (EX) model are studied in the time domain. In order to estimate the parameters, the parameter equations of an EX model are given in terms of the autocorrelation function, which is similar to the Yule-Walker equations of an autoregressive moving-average model. Estimates of parameters are obtained with the aid of the parameter equations and theorems are proved relating the convergence rate and asymptotic distribution of the estimates. We present two kinds of methods for estimating the order and prove that the estimates of the order are consistent.     

利用Monitor对NetWare网络参数进行优化

介绍了ＮｅｔＷａｒｅ网络操作系统的监控工具Ｍｏｎｉｔｏｒ的使用方法，重点分析了Ｍｏｎｉｔｏｒ屏幕上各种标志信息的含义以及如何根据这些信息来对ＮｅｔＷａｒｅ网络系统的参数进行优化。     

利用Monitor对NetWare网络参数进行优化

介绍了NetWare网络操作系统的监控工具Monitor的使用方法，重点分析了Monitor屏幕上各种标志信息的含义以及如何根据这些信息来对NetWare网络系统的参数进行优化。     

微波合成含氮掺杂碳量子点改性水性聚氨酯的制备与性能

以柠檬酸和尿素分别为碳源和氮源,微波法合成含氮掺杂碳点N-CDs。以异氟尔酮二异氰酸酯（IPDI）、聚氧化丙烯（PPG-2000）为主要原料,加入不同量的N-CDs制备一系列碳点水性聚氨酯复合材料 （N-CDs/WPU）。通过FTIR、TEM、XPS、UV-vis、荧光光谱和力学性能测试等对复合材料结构和性能进行表征。测试结果表明N-CDs主要由碳氮氧元素组成,表面具有胺基、羟基等活性基团,N-CDs的加入,提高了复合胶膜的力学性能,赋予了胶膜荧光性能。当N-CDs质量分数为0.6%时胶膜拉伸强度达到最大35.00MPa,加入量质量分数为0.8%时荧光强度最佳。     

钻井液处理剂及体系对聚合醇JLXI浊点的影响

聚合醇类水基防塌钻井液体系(简称 PEM)作为适应环境保护要求的一种新型钻井液体系应用越来越广泛。PEM 的核心处理剂为聚合醇类水基防塌润滑剂 JLX,JLX是一种非离子型低分子量聚合物,其作用机理是具有浊点特性。研究了钻井液体系及常用处理剂对两种聚合醇 JLX1 和JLX2 浊点的影响。实验结果表明,无机处理剂如 KCl 对 JLX浊点有显著的影响;而有机高分子处理剂的影响则较复杂,低浓度时能使 JLX 浊点上升,高浓度时则使 JLX 浊点下降;体系也能降低 JLX 浊点,并且对低浓度的无机处理剂如 KCl有一定的缓冲作用。     

Research on deepwater synthetic drilling fluid and its low temperature rheological properties

With the rapid development of deepwater drilling operations,more and more complex technical challenges have to be faced due to the rigorous conditions encountered.One of these challenges is that the drilling fluid used must had good rheological properties at low temperatures and high ability to inhibit hydrate formation.Synthetic drilling fluid has been widely applied to deepwater drilling operations due to its high penetration rate,excellent rheological properties,good ability to prevent hydrate formation,and high biodegradability.A synthetic drilling fluid formulation was developed in our laboratory.The rheological properties of this drilling fluid at low temperatures (0-20 °C) were tested with a 6-speed viscometer and its ability to inhibit hydrate formation was evaluated at 20 MPa CH 4 gas and 0 °C by differential scanning calorimetry (DSC).Several factors influencing the low temperature rheological properties of this synthetic drilling fluid were studied in this paper.These included the viscosity of the base fluid,the amount of CEMU and organic clay,and the water volume fraction.     

Simultaneous wavelet estimation and deconvolution of reflectionseismic signals

The problem of simultaneous wavelet estimation and deconvolution is investigated with a Bayesian approach under the assumption that the reflectivity obeys a Bernoulli-Gaussian distribution. Unknown quantities, including the seismic wavelet, the reflection sequence, and the statistical parameters of reflection sequence and noise are all treated as realizations of random variables endowed with suitable prior distributions. Instead of deterministic procedures that can be quite computationally burdensome, a simple Monte Carlo method, called Gibbs sampler, is employed to produce random samples iteratively from the joint posterior distribution of the unknowns. Modifications are made in the Gibbs sampler to overcome the ambiguity problems inherent in seismic deconvolution. Simple averages of the random samples are used to approximate the minimum mean-squared error (MMSE) estimates of the unknowns. Numerical examples are given to demonstrate the performance of the method     

Effective and efficient dimensionality reduction for large-scale and streaming data preprocessing

Dimensionality reduction is an essential data preprocessing technique for large-scale and streaming data classification tasks. It can be used to improve both the efficiency and the effectiveness of classifiers. Traditional dimensionality reduction approaches fall into two categories: feature extraction and feature selection. Techniques in the feature extraction category are typically more effective than those in feature selection category. However, they may break down when processing large-scale data sets or data streams due to their high computational complexities. Similarly, the solutions provided by the feature selection approaches are mostly solved by greedy strategies and, hence, are not ensured to be optimal according to optimized criteria. In this paper, we give an overview of the popularly used feature extraction and selection algorithms under a unified framework. Moreover, we propose two novel dimensionality reduction algorithms based on the orthogonal centroid algorithm (OC). The first is an incremental OC (IOC) algorithm for feature extraction. The second algorithm is an orthogonal centroid feature selection (OCFS) method which can provide optimal solutions according to the OC criterion. Both are designed under the same optimization criterion. Experiments on Reuters Corpus Volume-1 data set and some public large-scale text data sets indicate that the two algorithms are favorable in terms of their effectiveness and efficiency when compared with other state-of-the-art algorithms.