基于自适应阈值设置的运动目标检测算法

提出一种基于自适应阈值设置的运动目标检测算法,该算法不同干传统的全局阈值设置方法,而是利用核密度对背景像素点进行密度估计,给出一种新的全局和局部阈值相结合的自适应阈值设置方法。该方法考虑不同位置的像素颜色分布复杂度不同,针对每个像素点自适应设置局部阈值,能克服全局阈值的不足,提高检测的精度。对多个标准视频进行实验,实验结果证明了该算法的有效性。     

基于三维分割的图像加密算法

为了实现图像安全、快速加密,设计了一种新的数字图像混沌加密算法.仿真结果表明,该算法在没有扩散函数的情况下也能很好地改变图像的直方图,可以有效地抵制已知(选择)明文攻击,而且只有1次左映射或者右映射的情况下,该方法具有高置乱程度、良好的扩散性能和足够大的密钥空间,可获得足够高的安全性.     

高速重轨平立复合矫直控制工艺模型研究

以高速重轨矫直实际生产为依据,从重轨轧制工艺的能耗、前滑、矫直压力、平直度、应力应变五个方面建立了高速重轨平立复合矫直控制工艺模型.将该模型应用于实际生产,结果表明,模型能够准确地计算出不同规格重轨的压下分配量,所预测的平直度数值与实测数据相符合,达到了控制工艺模型设计初始的参数要求.     

Effective multiplicative updates for non-negative discriminative learning in multimodal dimensionality reduction

Fisher discriminant analysis gives the unsatisfactory results if points in the same class have within-class multimodality and fails to produce the non-negativity of projection vectors. In this paper, we focus on the newly formulated within and between-class scatters based supervised locality preserving dimensionality reduction problem and propose an effective dimensionality reduction algorithm, namely, Multiplicative Updates based non-negative Discriminative Learning (MUNDL), which optimally seeks to obtain two non-negative embedding transformations with high preservation and discrimination powers for two data sets in different classes such that nearby sample pairs in the original space compact in the learned embedding space, under which the projections of the original data in different classes can be appropriately separated from each other. We also show that MUNDL can be easily extended to nonlinear dimensionality reduction scenarios by employing the standard kernel trick. We verify the feasibility and effectiveness of MUNDL by conducting extensive data visualization and classification experiments. Numerical results on some benchmark UCI and real-world datasets show the MUNDL method tends to capture the intrinsic local and multimodal structure characteristics of the given data and outperforms some established dimensionality reduction methods, while being much more efficient.     

Modelling concurrency with comtraces and generalized comtraces

Comtraces (combined traces) are extensions of Mazurkiewicz traces that can model the “not later than” relationship. In this paper, we first introduce the novel notion of generalized comtraces, extensions of comtraces that can additionally model the “non-simultaneously” relationship. Then we study some basic algebraic properties and canonical representations of comtraces and generalized comtraces. Finally we analyze the relationship between generalized comtraces and generalized stratified order structures. The major technical contribution of this paper is a proof showing that generalized comtraces can be represented by generalized stratified order structures.     

基于近似熵的VDR人声识别技术研究

利用话者识别原理和语音数字信号处理技术对人声建模方法进行研究,建立了基于GMM模型的VDR环境下的人声识别基准系统;从分析影响人声识别率因素的角度出发,指出传统算法的不足,并提出一种基于近似熵的语音端点检测算法。理论分析和实验结果证明:新算法能有效屏蔽大动态冲击性噪声,解决了语音的虚检现象,并且在低信噪比0 dB情况下的识别率提升66%。     

Symbolic hierarchical analysis in currency markets: An application to contagion in currency crises

In this paper we introduce a new method to describe dynamical patterns of the real exchange rate co-movements time series and to analyze contagion in currency crisis. The method combines the tools of symbolic time series analysis with the nearest neighbor single linkage clustering algorithm. Data symbolization allows us obtaining a metric distance between two different time series that is used to construct an ultrametric distance. By analyzing the data of various countries, we derive a hierarchical organization, constructing minimal-spanning and hierarchical trees. From these trees we detect different clusters of countries according to their proximity. We show that this methodology permits us to construct a structural and dynamic topology that is useful to study interdependence and contagion effects among financial time series.