一种不确定时段的扩展时段对序逻辑：时间Petri网模型表示和线性推理

针对点－时段时序逻辑的不足,提出了一种新的时段时序逻辑－－扩展时段时序逻辑,对不确定时间段发生的事件具有较好的描述能力。时间Petri网模型表示的引入,增强了扩展时段时序逻辑的描述直观性及分析能力,为进行线性推理提供了有利的工具。同时还提出了几种变迁间的实施推理规则。运用这些规则可以简化复杂时序关系的Petri网模型,并在线性时间复杂度内定量地得到各变迁间的时序逻辑关系,因而是一种行这有效的方法。     

一种快速、鲁棒的唇动检测与定位方法

提出了一种利用唇色在彩色空间分布信息和唇在人脸区域的几何分布特性进行快速、鲁棒的唇动检测与定位的方法。在该方法中,首先提出采用有约束的Fisher变换进行嘴唇图像增强;然后利用嘴唇在人脸区域的几何分布特性,来提高唇动定位的速度和准确性,唇动定位主要利用两种几何分布特性：（1）利用唇的区域面积和人脸的区域面积比值变化范围不大这一特性自适应确定区分唇色和肤色的值。（2）利用外唇参数与内唇参数之间有近似线性关系的假设,用外唇参数预测内唇参数,克服内唇定位经常陷于局部最小、定位不准的缺陷。实验证明,该唇动检测与定位系统无论从速度还是从可靠性、准确性都能满足应用的要求。     

Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions

We consider on-line density estimation with a parameterized density from the exponential family. The on-line algorithm receives one example at a time and maintains a parameter that is essentially an average of the past examples. After receiving an example the algorithm incurs a loss, which is the negative log-likelihood of the example with respect to the current parameter of the algorithm. An off-line algorithm can choose the best parameter based on all the examples. We prove bounds on the additional total loss of the on-line algorithm over the total loss of the best off-line parameter. These relative loss bounds hold for an arbitrary sequence of examples. The goal is to design algorithms with the best possible relative loss bounds. We use a Bregman divergence to derive and analyze each algorithm. These divergences are relative entropies between two exponential distributions. We also use our methods to prove relative loss bounds for linear regression.     

Error Estimates for Semidiscrete Finite Element Approximations of the Stokes Equations Under Minimal Regularity Assumptions

Semidiscrete (spatially discrete) finite element approximations of the Stokes equations are studied in this paper. Properties of L ², H ¹ and H ^–1 projections onto discretely divergence-free spaces are discussed and error estimates are derived under minimal regularity assumptions on the solution.     

Non-Linear Scale-Spaces Isomorphic to the Linear Case with Applications to Scalar,Vector and Multispectral Images

A basic requirement of scale-space representations in general is that of scale causality, which states that local extrema in the image should not be enhanced when resolution is diminished. We consider a special class of nonlinear scale-spaces consistent with this constraint, which can be linearised by a suitable isomorphism in the grey-scale domain so as to reproduce the familiar Gaussian scale-space. We consider instances in which nonlinear representations may be the preferred choice, as well as instances in which they enter by necessity. We also establish their relation to morphological scale-space representations based on a quadratic structuring function.     

Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression

In this paper, we propose the application of the Kernel Principal Component Analysis (PCA) technique for feature selection in a high-dimensional feature space, where input variables are mapped by a Gaussian kernel. The extracted features are employed in the regression problems of chaotic Mackey–Glass time-series prediction in a noisy environment and estimating human signal detection performance from brain event-related potentials elicited by task relevant signals. We compared results obtained using either Kernel PCA or linear PCA as data preprocessing steps. On the human signal detection task, we report the superiority of Kernel PCA feature extraction over linear PCA. Similar to linear PCA, we demonstrate de-noising of the original data by the appropriate selection of various nonlinear principal components. The theoretical relation and experimental comparison of Kernel Principal Components Regression, Kernel Ridge Regression and ε-insensitive Support Vector Regression is also provided.     

线性多步法模糊逻辑系统

根据求解常微分方程的线性多步逼近方法构造了线性多步法模糊逻辑系统以辨识由常微分方程所描述的未知动态系统,这种新的模糊逻辑系统在预测状态的同时也能够逼近系统微分方程中的未知函数,文中从理论上对精度作了分析,并用仿真验证了所得的结果。     

基于投影的回归分析方法综述

回归分析方法是建立复杂对象外特性模型的一类重要方法.本文对现有各种回归方法进行了综述.采用一个统一的目标函数来解释各种回归方法,并以此为基础,系统介绍了各种回归分析方法(包括常见的主成分分析法和部分最小二乘法(PLS))的意义、结构、算法、特性及其相互关系.     

输出饱和线性系统的稳定性及L2增益性能

考虑输出饱和线性系统的稳定性以及L     

Efficient Solution of A, x ( k )?= b ( k ) Using A ?1

In this work, we consider the problem of solving , , where b ^(k+1) = f(x ^(k)). We show that when A is a full matrix and , where depends on the specific software and hardware setup, it is faster to solve for by explicitly evaluating the inverse matrix A ⁻¹ rather than through the LU decomposition of A. We also show that the forward error is comparable in both methods, regardless of the condition number of A.