Analysis of the M/G/1 queue with discriminatory random order service policy

We consider an M/G/1 queue with different classes of customers and discriminatory random order service (DROS) discipline. The DROS discipline generalizes the random order service (ROS) discipline: when the server selects a customer to serve, all customers waiting in the system have the same selection probability under ROS discipline, whereas customers belonging to different classes may have different selection probabilities under DROS discipline. For the M/G/1 queue with DROS discipline, we derive equations for the joint queue length distributions and for the waiting time distributions of each class. We also obtain the moments of the queue lengths and the waiting time of each class. Numerical results are given to illustrate our results.     

概率逻辑结果支持度的合成算法

Haenni的概率推理系统在与D-S理论相互转化的过程中进行了投影,从而不可避免地导致一些有价值信息的丢失。为此提出一种新的概率逻辑结果支持度的合成算法来避免信息的丢失。     

一种新型覆盖连通率计算方法

针对在能量受限的无线传感器网络中传感器节点在部署时必须满足一定覆盖率和连通率的问题,提出一个基于正方形区域的新型覆盖率、连通率计算方法,该方法能够描述网络覆盖率、连通率、部署节点的数量、节点感应(通信半径)和网络区域大小之间的关系,计算出在满足一定覆盖率、连通率所需要部署的节点的数量。模拟实验结果表明该理论值和模拟结果之间的误差较小。     

A Family of Canonical Correlation Networks

We have previously introduced [2, 5] a neural implementation of Canonical Correlation Analysis (CCA). In this paper, we re-derive the learning method from a probabilistic perspective and then show that similar networks can be derived based on the pioneering work of Becker [1] if certain simplifying assumptions are made. Becker has shown that her network is able to find depth information from an abstraction of random dot stereogram data and so finally we note the similarity of the derived methods with those of Stone [3] which was used with a smooth stereo disparity data set to extract depth information.     

Evolving Teams of Predictors with Linear Genetic Programming

This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a realnumbered vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP.     

A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients

We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set. We also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures.     

多维逆M序列及其在多变量系统辨识中的应用

基于Ｍ序列和逆Ｍ序列,提出了多维逆Ｍ序列发生器的结构形式。多维逆Ｍ序列近似周期白噪声,无直流分量,各维序列之间互不相关。言语中研究了多维逆Ｍ序列的性质,给出了自相关函数的计算公式。通过使用多维逆Ｍ序列,将多变量系统参数辨识简化为各通道对应的单变量系统参数辨识。多维逆Ｍ序列具有很好的推广应用价值。     

卡通玩具的悦情功能与形象魅力塑造的设计探讨

"悦情"是新时代特征赋予现代玩具的一大主要功能,这一功能对于现代卡通玩具形象设计的标准和方法又提出了新的要求。本文以现代玩具中增长最为迅速的领域——静态卡通玩具形象设计为探讨内容,通过现代卡通玩具的"形象魅力"能给人们带来"悦情"的功能特点,从而提出了现代卡通玩具"形象魅力"塑造的原则——"形神兼备"的观点。并从"表情"、"性格魅力"与"肢体动态"等三个方面来阐述如何达到卡通玩具"形神兼备"的艺术造诣。     

利用客户端脚本实现复合查询的新思路

本文主要针对目前网站上常用的查询系统所存在的某些常见问题而提出了一种新的查询设计思路,这种新的设计思路主要是在客户端利用JavaScript脚本语言对用户设置的查询条件进行预处理,让用户设置的条件以两种形式出现在系统中,一种形式方便方便用户阅读和理解,另一种形式用于真正的数据查询;此外,通过脚本语言将用户设置的查询条件保存于页面上列表中,用户可以随意组合现有的条件形成更为复杂的查询条件;同时用户也可以在列表中随意选择某个条件执行数据查询。     

Combinatorial optimization under uncertainty

In this paper, the general problem of Euclidean combinatorial optimization under uncertainty is formulated for the first time and the concepts of a stochastic multiset, a multiset of fuzzy numbers, a stochastic Euclidean combinatorial set, and general Euclidean combinatorial set of fuzzy stochastic numbers that combines the properties of both types of uncertainty are introduced. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 35–44, September–October 2008.