A goodness-of-fit test for Archimedean copula models in the presence of right censoring

A goodness-of-fit testing procedure for Archimedean copula (AC) models is developed based on right-censored data. The proposed approach extends an existing method, which is suitable for the Clayton model, to general AC models. Asymptotic properties of the proposed test statistics under the true model assumption are derived. Simulation analysis shows that the proposed test has reasonable performance. Finally, two real data examples are analyzed for illustrative purposes.     

一种基于边界约束的流形展开方法

在流形学习的谱方法中, 流形展开被表述为优化问题. 这些优化问题的解是退化的, 即所有的样本将被嵌入到同一个点. 为了避免退化解, 谱方法对嵌入坐标人为地强加了一个单位协方差矩阵约束. 然而, 该约束往往导致流形展开的失真非常明显. 本文提出一种新的流形学习方法, 彻底抛弃了人为的单位协方差矩阵约束. 主要思路是先对流形边界进行嵌入, 然后再求流形内部的嵌入; 流形边界的嵌入位置被确定后, 流形内部样本的嵌入位置将被边界拉开, 使得它们不会都收缩到一个点上, 从而避免了退化解的出现. 将流形边界的嵌入位置作为边界条件, 求解一个线性方程组来得到内部样本的嵌入; 该线性方程组反映了尽量保持邻近样本间距离不变的要求. 流形边界的嵌入由简化流形的嵌入求出; 为此, 本文还设计了一种流形边界检测算法以及一种流形简化算法. 与目前代表性的几种流形学习方法进行了比较实验, 结果表明了本文方法的有效性, 其展开失真比谱方法明显要小.     

Inference for Weibull distribution under generalized order statistics

Based on generalized order statistics from Weibull distribution the approach of Bayesian and non-Bayesian estimation are discussed. We present a simple and efficient simulational algorithm for generating a generalized order statistics sample from any continuous distribution. Specializations to Bayesian and non-Bayesian estimators, some lifetime parameters and confidence intervals of progressive II censoring and record values are obtained and compared with the existing results. Two examples are given to illustrate the proposed estimators and the simulation algorithm.     

Chattering reduction of sliding mode control by low‐pass filtering the control signal

The conventional approach to reducing control signal chattering in sliding mode control is to use the boundary layer design. However, when there is high‐level measurement noise, the boundary layer design becomes ineffective in chattering reduction. This paper, therefore, proposes a new design for chattering reduction by low‐pass filtering the control signal. The new design is non‐trivial since it requires estimation of the sliding variable via a disturbance estimator. The new sliding mode control has the same performance as the boundary layer design in noise‐free environments, and outperforms the boundary layer design in noisy environments. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

车载环境下基于样本熵的语音端点检测方法

在语音处理中一个关键性问题是如何准确找到语音的起止位置,目前提出许多的语音端点检测算法不能得到理想的检测结果.由于样本熵是近似熵的改进算法,提出车载环境下基于样本熵的语音端点检测方法.并采用模糊C均值聚类算法和贝叶斯信息判决算法进行样本熵特征门限估计,以及使用双门限法进行语音端点检测.在TIMIT连续语音库上的实验表明,车载噪声环境下,样本熵法和近似熵法的检测正确率均远高于谱熵法和能量谱熵法,而样本熵法相对于近似熵法具有更好的检测效果,特别是当信噪比小于等于OdB时,样本熵法的检测性能优于近似熵法近10%.因此,样本熵法在车栽智能语音领域具有很好的应用前景,能够为车载导航提供准确的语音端点检测技术.     

光谱响应差异对高精度交叉定标的影响——以FY-3A/MERSI与EOS/MODIS为研究实例

分析了MERSI与MODIS间光谱响应差异对测量表观反射比的影响。正演模拟结果显示,在极区冰雪情况下,MERSI与MODIS测量得到的表观反射比之间的相对差异是关于MODIS所测表观发射比的二次函数。MERSI的前4个通道与对应的MODIS通道之间的相对差异在0~0.8%,0~2%,0.5%~2.5%,-1.8%~-0.8%之间。将正演模拟得到的对光谱响应差异的修正关系应用到具体的SNO测量后,MERSI前3个通道的表观反射比与修正后MODIS的表观反射比之间的相对差异减小到了3%以内。MERSI的1、2通道在红外波段也有响应,这部分响应在极区冰雪条件下对表观反射率的影响在0.5%左右,基本可以忽略。     

基于噪声倒谱阈值频谱估计的语音活动检测

针对低方差频谱估计的语音活动检测(VAD)中Welch频谱估计方法计算量大的问题,提出利用倒谱阈值方法估计VAD中的噪声功率谱.该方法在静音时期为噪声的倒谱设置阈值,利用快速傅里叶变换计算频谱,再更新VAD中的判决阈值.算法复杂度分析与仿真结果表明,该方法的检测性能与Welch方法相当,计算量降低约18%,同时降低整个...     

基于α分布的单矢量传感器多目标方位估计

研究 稳定分布条件下单矢量传感器的多目标方位估计问题。介绍多维 稳定分布特征函数的谱测度表示及谱测度估计方法,在此基础上推导出单矢量传感器输出速度正交分量满足特征函数谱测度表示方程,建立谱测度与目标方位间的对应关系,用特征函数法或一维投影法估计谱测度和目标源方位及数量。仿真结果表明,在 稳定分布假设下,基于谱测度的多目标方位估计法是合理有效的。     

基于信息势能的鲁棒估计器及其在同时定位与地图构建问题中的应用

提出了一种基于信息势能鲁棒估计器来解决机器人室内的同时定位与地图构建（SLAM）问题．结构化的室内环境可以用线段近似表示．然而动态环境中,测距传感器测得的数据通常湮没在大量的噪声信号中．本文采用“分割与合并”（split—and．merge）方法进行线段的分类,根据信息势能的性能指标衡量每个采样数据对该线段的信息贡献量．按照信息优化理论设计估计器,选择信息量贡献大的样本点作为信息内点提取线段参数,构建局部地图．采用粒子滤波器进行地图及机器人路径的更新．采用递推的方法估计信息势能,降低了对样本点的信息量贡献做估计时的复杂度．仿真和实验结果证明,本文所提出的方法具有较强的鲁棒性,提高了SLAM策略的准确性和实时性．     

On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images

In this work, neural network-based models involved in hyperspectral image spectra separation are considered. Focus is on how to select the most highly informative samples for effectively training the neural architecture. This issue is addressed here by several new algorithms for intelligent selection of training samples: (1) a border-training algorithm (BTA) which selects training samples located in the vicinity of the hyperplanes that can optimally separate the classes; (2) a mixed-signature algorithm (MSA) which selects the most spectrally mixed pixels in the hyperspectral data as training samples; and (3) a morphological-erosion algorithm (MEA) which incorporates spatial information (via mathematical morphology concepts) to select spectrally mixed training samples located in spatially homogeneous regions. These algorithms, along with other standard techniques based on orthogonal projections and a simple Maximin-distance algorithm, are used to train a multi-layer perceptron (MLP), selected in this work as a representative neural architecture for spectral mixture analysis. Experimental results are provided using both a database of nonlinear mixed spectra with absolute ground truth and a set of real hyperspectral images, collected at different altitudes by the digital airborne imaging spectrometer (DAIS 7915) and reflective optics system imaging spectrometer (ROSIS) operating simultaneously at multiple spatial resolutions.