基于线性预测的数字语音参数估计

介绍了数字语音信号分析中的线性预测的基本原理。讨论了用线性预测估计基本语音参数的方法、并对实验仿真结果进行了分析，从实验分析结果可知：线性预测分析是一种估计精度较高、运算速度较快的语音参数估计方法。     

一种基于同伦非线性模型的语音盲分离方法

采用同伦非线性模型对语音信号进行建模,将非线性可预测性作为盲源分离的准则,推导了基于同伦模型的盲源分离算法,成功地实现了语音信号的分离。这种方法是对基于线性预测模型盲源分离算法的推广,它既适用于分离采用线性预测模型建模的信号,也可以分离采用同伦非线性模型建模的信号(如语音)。理论分析和仿真表明,这种方法比基于线性可预测性的盲分离算法具有更广的适用范围,对于语音信号的分离,此种算法具有更快的收敛速度。     

采用线性回归方法的发动机活塞参数优化设计

活塞是发动机的重要零件,一般的活塞参数优化方法采用反复的实验、数据分析、参数改进,最终确定其主要参数.这种方法效率低,花费大量的人力与物力.作者提出了基于线性回归实现活塞主要参数优化设计的方法,即建立回归模型分析现有的成熟发动机和活塞参数,找出发动机参数与活塞参数之间的相关函数,利用这些函数并根据发动机的要求进行活塞主要参数的设计.     

基于灰色线性回归组合模型的机床热误差建模方法

为了控制机床热误差和提高机床加工精度,考虑到测得的热误差数据同时存在着线性和非线性因素,提出了采用具有处理线性和非线性能力的灰色线性回归组合热误差模型的建模方法.用此方法对某卧式加工中心热误差进行了建模和预测,并引入BP神经网络对热误差模型的残差进行修正,从而获得了比较准确的热误差预测值.与用指数函数来模拟生成数据的灰色模型所获得的预测值进行了比较,证明了灰色线性回归组合及BP神经网络模型在机床热误差补偿建模应用中的优越性.     

基于熵函数的语音端点检测方法

根据语音信号的波形特征,利用熵函数的性质,构造了一种特殊的熵函数,通过判断此函数值的大小确定是语音还是无声状态。实验表明,此方法计算简单而且具有很高的准确性。     

基于第二类切比雪夫多项式的语音线性谱对参数的计算

对线性谱对(LSP)参数的计算方法提出改进算法，该算法利用第二类切比雪夫多项式的迭代性质对初始函数降阶。理论分析表明，改进算法可以获得更简洁的数学表达式。实验结果显示，改进算法中基本消除了乘法运算，同时随着线性预测分析阶数的增加可以进一步降低算法复杂度。     

基于线性预测模型的氦语音增强算法研究

潜水员在水下工作时,由于生理方面的原因,需要以氦氧混合气体作为呼吸气体,由于气体结构的变化出现了氦语音现象,使得语音发生畸变,降低了清晰度。通过介绍基于线性预测模型的氦语音增强算法,将频域的线谱对(LSP)分析应用于氦语音增强中,由于线谱对系数与语音信号谱包络有紧密的联系,用线谱对参数(LSP)构成合成滤波器时更容易保证稳定性,所以提出了一种基于线谱对系数(LSP)的氦语音增强算法。通过实验将这种算法与基于线性预测LPC的增强算法进行了比较,实验结果表明,两种算法均能对氦语音进行矫正,并且新的算法能够对共振峰进行单独调节,在不影响清晰度的同时最大限度地保持了原有语音的细节,提高了可懂度。     

基于多元线性回归分析的水资源供需问题研究

据统计,世界上大约四分之一的人口生活在缺水地区,缺水已成为全人类需要解决的问题.本文通过建立一个模型来评价一个国家或地区提供清洁水的能力,找出水资源短缺的原因.认为一个国家或地区的供水主要与年降水量、森林覆盖率和年平均气温有关.对水的需求主要由工业用水、农业用水和生活用水组成.通过多元线性回归分析,得出了供水与年降水量...     

基于线性回归的相关查询推荐

在分析搜索引擎查询日志的基础上,提出了一种基于线性回归的相关查询推荐方法。该方法考虑了查询串之间的多种关联关系,包括查询串会话共现、点击页面共享和查询串文本相似性,以避免因直接应用查询串之间的部分关联关系造成易受查询日志中噪音的影响。相比以往的方法,采用线性回归的方法来识别相关查询推荐的有效特征,能更好地解决噪音问题和进行有效的相关查询推荐。实验证实,采用线性回归挖掘的识别中文相关查询串的特征非常有效,且所提出的回归模型的预测准确率优于现有的方法。     

基于多元线性回归分析的用电量研究

本文简要阐述了当前电力市场负荷预测的手段,并通过引入多元线回归分析的主要理论,研究电网企业在用电量与宏观经济其他相关因素方面存在的内有关系,揭示其运行规律。     

基于多元线性回归的低温容器蒸发流量影响因素分析

以液氮为试验工质,对一个容积为35立方米的高真空多层绝热容器进行了日蒸发率试验,将得到的蒸发流量与环境压力、气相空间压力及环境温度之间的关系进行了多元线性回归分析,给出了蒸发流量的回归方程,并通过数理统计理论证明此方程具有较高的显著性。分析了每个影响蒸发流量的因素在多元回归中所起的作用。分析表明,环境压力、环境温度是影响蒸发流量的主要因素,并且环境压力的影响比环境温度大;气相空间压力对蒸发流量的影响比较小,是次要因素。     

Sparse multivariate regression with missing values and its application to the prediction of material properties

In the field of materials science and engineering, statistical analysis and machine learning techniques have recently been used to predict multiple material properties from an experimental design. These material properties correspond to response variables in the multivariate regression model. In this study, we conduct a penalized maximum likelihood procedure to estimate model parameters, including the regression coefficients and covariance matrix of response variables. In particular, we employ $l_1$-regularization to achieve a sparse estimation of The regression coefficients and inverse covariance matrix of response variables. In some cases, there may be a relatively large number of missing values in the response variables, owing to the difficulty of collecting data on material properties. We therefore propose a method that incorporates a correlation structure among the response variables into a statistical model to improve the prediction accuracy under the situation with missing values. The expectation maximization algorithm is also constructed, which enables application to a dataset with missing values in the responses. We apply our proposed procedure to real data consisting of 22 material properties.     

基于正交设计与多元非线性回归分析Cu-Ce/TiO_2的光-湿性能

以Cu(NO_3)_2·3H_2O和Ce(NO3)3·6H_2O为改性剂,采用溶胶-凝胶法制备Cu-Ce/TiO2。通过正交设计与多元非线性回归结合分析Cu-Ce负配载量、Cu与Ce摩尔比、烧结温度对Cu-Ce/TiO2湿性能和光催化性能的显著性。获得优化Cu-Ce/TiO2制备参数,运用SEM、LPSA、XRD和UV-Vis对优化Cu-Ce/TiO2进行性能研究和表征分析。结果表明,各因素对Cu-Ce/TiO2湿性能和光催化性能的影响重要程度顺序:Cu-Ce负配载量Cu与Ce摩尔比烧结温度空白因素。优化Cu-Ce/TiO2制备参数:Cu-Ce负配载量为3.26%、Cu与Ce摩尔比为0.87∶1、烧结温度为505℃。优化Cu-Ce/TiO2的湿性能为0.0869g·g-1,光催化性能为50.7%。Cu-Ce/TiO2形貌较好,粒径分布为1 183.68~3 916.05nm,促使吸收边带发生红移。     

基于ARM-WINCE的音视频监控系统设计

监控系统在工业生产和日常生活中有着广泛的应用,设计了一种采用嵌入式处理器与嵌入式操作系统相结合的音视频监控系统。系统硬件平台由CCD图像传感器、GO7007SB视频编码器、S3C2410嵌入式处理器组成,负责视频数据的采集和MPEG-4压缩编码;软件平台采用Windows CE 5.0作为操作系统,软件实现了数据流的RTP封装和发送。详细论述了系统设计、硬件设计和软件设计,实际运行效果表明该系统稳定可靠,能较好地满足监控要求。此设计架构可大大降低开发难度,缩短开发周期,具有很好的可维护性和性价比。     

A response surface method based on weighted regression for structural reliability analysis

Approximation methods such as the response surface method (RSM) are widely used to alleviate the computational burden of engineering analyses. For reliability analysis, the common approach in the RSM is to use regression methods based on least square methods. However, for structural reliability problems, RSMs should approximate the performance function around the design point where its value is close to zero. Therefore, in this study, a new response surface called ADAPRES is proposed, in which a weighted regression method is applied in place of normal regression. The experimental points are also selected from the region where the design point is most likely to exist. Examples are given to demonstrate the benefit of the proposed method for both numerical and implicit performance functions.     

基于顾客需求的业务过程分析

 应用QFD的思想,建立了顾客需求一绩效指标关系矩阵、绩效指标一过程关系矩阵,以此为基础获得了顾客需求过程关系矩阵从而建立了顾客需求与实现需求的过程的直接联系.提出了计算待改进过程优先度的方法,为识别出那些主要影响顾客需求的过程、确定过程管理的方向提供了可靠的依据.最后开展了案例研究.     

基于累积Logistic 回归分析的文本段落聚类策略研究

提出一种新的文本段落聚类策略,该策略采用多特征融合思想尽可能多地挖掘段落内的特征,并采用累积Logistic 回归分析方法来拟合这些特征与段落相似度之间的内在关联,使得段落相似度计算的结果更为理想.最后采用层次聚合聚类算法中的complete-link方法对段落集合进行聚类处理.通过网络真实文本进行了段落相似度度量实验和段落聚类实验,实验结果显示了方法的可行性.     

Univariate and multivariate process yield indices based on location-scale family of distributions

Several measures of process yield, defined on univariate and multivariate normal process characteristics, have been introduced and studied by several authors. These measures supplement several well-known Process Capacity Indices (PCI) used widely in assessing the quality of products before being released into the marketplace. In this paper, we generalise these yield indices to the location-scale family of distributions which includes the normal distribution as one of its member. One of the key contributions of this paper is to demonstrate that under appropriate conditions, these indices converge in distribution to a normal distribution. Several numerical examples will be used to illustrate our procedures and show how they can be applied to perform statistical inferences on process capability.     

A variance change point estimation method based on intelligent ensemble model for quality fluctuation analysis

For multivariable production process, knowing the first time of process really changes (change point) will help to accelerate the location of assignable causes and make measures for process adjustment. So effective estimating the change point is an important way to analyse the quality fluctuation of process. In the present study, an intelligent ensemble model for quality fluctuation analysis is proposed to estimate the variance change point in multivariable process. With the method, the process is decomposed based on moving window analysis, then different types of kernel functions are combined together to form the multi-kernel support vector machine model, which has combined the feature mapping capability of each basic kernel in the new feature space. The particle swarm optimisation is considered to search the optimised multi-kernel parameters. After that, each sub-characteristic is regarded as a pattern to be recognised to determine the change point by using the optimised intelligent ensemble model. Finally, a case study is conducted to evaluate the performance of proposed approach. It reveals that the method could estimate the time of variance change point in continuous production process accurately.     

Robot selection using an integrated approach based on quality function deployment and fuzzy regression

The decision-makers have been experiencing difficulties in determining the most suitable robot alternative due to the increase in number of robots and the diversity in their application areas. A robust decision framework for robot selection should consider multiple and conflicting criteria and the dependencies among them. This paper introduces a decision model for robot selection based on quality function deployment (QFD) and fuzzy linear regression. The proposed approach benefits from the fact that QFD focuses on delivering value by taking into account the customer requirements and then by deploying this information throughout the development process, and applies this perspective to robot selection. Fuzzy linear regression is considered as an alternative decision aid for robot selection problems where imprecise relationships among system parameters exist. An example robot selection problem is presented to illustrate the proposed decision-making approach.