Analysis of the cross-effects in a cross-lagged panel study

Panel studies are statistical studies in which two or more variables are observed for two or more subjects at two or more points in time. Cross-lagged panel studies are comprised of continuous variables which divide naturally into two sets, and otten the primary statistical issue Is to estimate and test the cross-effects which indicate the degree to which each set is related to the other over time. By taking a regression approach to modeling the relationships, we apply multivariate regression methodology to make inferences about the regression coefficients in a cross-lagged panel model. In particular we develop a test of the hypothesis that the regression coefficients indicating the cross-effects are equal and develop simultaneous confidence bounds for various linear combinations of these regression coefficients.     

Reliability evaluation subject to assured accuracy rate and time for stochastic unreliable-node computer networks

In many real-life networks such as computer networks, branches and nodes have multi-state capacity, lead time, and accuracy rate. The network with unreliable nodes is more complex to evaluate the reliability because node failure results in the disabled of adjacent branches. Such a network is named a stochastic unreliable-node computer network (SUNCN). Under the strict assumption that each component (branch and node) has a deterministic capacity, the quickest path (QP) problem is to find a path sending a specific amount of data with minimum transmission time. The accuracy rate is a critical index to measure the performance of a computer network because some packets are damaged or lost due to voltage instability, magnetic field effects, lightning, etc. Subject to both assured accuracy rate and time constraints, this paper extends the QP problem to discuss the system reliability of an SUNCN. An efficient algorithm based on a graphic technique is proposed to find the minimal capacity vector meeting such constraints. System reliability, the probability to send a specific amount of data through multiple minimal paths subject to both assured accuracy rate and time constraints, can subsequently be computed.     

多特征结合的中文垃圾邮件过滤特征选择方法研究

在中文垃圾邮件过滤系统中,基于内容过滤的Na？ve Bayes算法得到了广泛应用。本文将多种特征结合构建邮件文本向量,应用八种文本分类特征选择方法在Na？ve Bayes算法上进行实验验证,通过准确率和召回率结合的综合性能指标F1值进行性能评价,结果表明,采用类别区分词、优势率、信息增益、期望交叉熵、CHI统计和文本证据权等六种特征选择方法应用于多特征结合邮件文本向量的过滤取得了较好的垃圾邮件过滤性能,反垃圾邮件效果较好。     

S变换时变滤波在去噪处理中的应用研究

针对常用的滤波去噪方法都受到使用条件的限制，实际资料的滤波去噪不能达到良好效果，S变换时变滤波克服了传统滤波去噪方法滤波因子不能随时间、频率变化而变化的缺陷。将地震资料用S变换方法变换到时频域，对不同时间内不同频率的噪声部分充零，再将去噪后的地震数据利用S反变换到时间域，以获得所需要的有效信号。通过理论计算分析和实例计算表明，S变换时变滤波能够有效去除不同时段、不同频率的噪声。该方法具有一定的可行性。     

Modified repeated median filters

We discuss moving window techniques for fast extraction of a signal composed of monotonic trends and abrupt shifts from a noisy time series with irrelevant spikes. Running medians remove spikes and preserve shifts, but they deteriorate in trend periods. Modified trimmed mean filters use a robust scale estimate such as the median absolute deviation about the median (MAD) to select an adaptive amount of trimming. Application of robust regression, particularly of the repeated median, has been suggested for improving upon the median in trend periods. We combine these ideas and construct modified filters based on the repeated median offering better shift preservation. All these filters are compared w.r.t. fundamental analytical properties and in basic data situations. An algorithm for the update of the MAD running in time O(log n) for window width n is presented as well.     

一种新型SOA-MZI光功率均衡器的研究

提出了一种基于SOA-MZI结构的光功率均衡器,利用SOA的相移随输入光功率变化的特性,改变 MZI两臂的相位差,实现了在一段输入光功率范围内达到钳制输出光功率的目的．仿真分析表明该光功率均衡器能够减小光功率波动,提高接收灵敏度:光脉冲经过SOA导致的脉冲畸变是限制该光功率均衡器性能的主要原因．     

小波包变换在股市预测中的应用研究

应用小波包变换对股市预测进行了研究,提出了股市预测的小波包方法。首先将股指时序进行小波包分解,并对分解后得到的各部分进行混沌判别,以确定其混沌特性;然后对各部分分别建立混沌模型进行预测;再将混沌模型预测的结果进行小波包重构,则得到原始时序的预测结果。对上证综指日收益率进行了单步预测和多步预测研究,效果很好。     

Deterministic approximate inference techniques for conditionally Gaussian state space models

We describe a novel deterministic approximate inference technique for conditionally Gaussian state space models, i.e. state space models where the latent state consists of both multinomial and Gaussian distributed variables. The method can be interpreted as a smoothing pass and iteration scheme symmetric to an assumed density filter. It improves upon previously proposed smoothing passes by not making more approximations than implied by the projection onto the chosen parametric form, the assumed density. Experimental results show that the novel scheme outperforms these alternative deterministic smoothing passes. Comparisons with sampling methods suggest that the performance does not degrade with longer sequences.     

滤波初值对测向定位的影响及其消除方法

基于运动单站测向交叉定位中将滤波初值分成两类,该文从几何原理上解释了不同类型的滤波初值对定位结果的影响;提出了通过增加相位差观测量,利用相位差的变化率和方位角信息,将非线性系统转换成线性系统,再利用卡尔曼滤波来改善定位结果对滤波初值依赖性的方法。转换后的线性系统在利用卡尔曼滤波时,初始值可以根据以前的测量值得到。模拟仿真表明该方法能有效摆脱定位结果对滤波初值的依赖,且定位精度在2%以内。     

网络民意的失控及其舆论传播影响分析

网络舆论提供了个体与大众之间平等对话的条件,但网站信息的同类搜集和网址链接,以及"虚拟社区"的集体民意同质性,往往导致信息的自主"协同过滤"(collaborative filtering).网民受众所能听到和看到的只能是自己的"回音"和"影子",这种狭隘的民意氛围又很可能导致"群体极化"(group polarization)现象的产生,"民意"很可能只是完全的自我情绪发泄和不可控的符号暴力.网络舆论环境极有可能导致"假民意"的流行,并为那些偏离常规的行为提供某种合法性和不可控性,出现"多数人暴政"的危险.对网络舆论必须进行有效的调控和引导.