软起动器和变频器的时分多机复用技术及其应用

本文提出了时分多机复用的概念，并对其技术和应用加以具体说明，展现了该技术良好的应用前景。     

卷积积分和拉普拉斯变换在一阶电路中的应用

电路的任意激励的零状态响应,可以用卷积积分和拉普拉斯交换的方法求解。本文通过两个一阶电路的实例,阐述了两种方法的应用特点。     

Action categorization with modified hidden conditional random field

In this paper, we present a method for action categorization with a modified hidden conditional random field (HCRF). Specifically, effective silhouette-based action features are extracted using motion moments and spectrum of chain code. We formulate a modified HCRF (mHCRF) to have a guaranteed global optimum in the modelling of the temporal action dependencies after the HMM pathing stage. Experimental results on action categorization using this model are compared favorably against several existing model-based methods including GMM, SVM, Logistic Regression, HMM, CRF and HCRF.     

基于混合聚类的城市道路多时段控制研究*

多方案控制具有简单实用、可靠性好,稳定性高,性价比高等的优点,在城市交通控制中得到了广泛应用。本研究在利用指数平滑异同移动平均法对缺失交通数据进行修补的基础上,运用快速聚类与系统聚类相结合的混合聚类方法对交通数据进行分析,并以经典的Silhouttte评价指标作为聚类终止条件,同时兼顾/考虑了交通数据时序性,消除聚类结果中的三类“奇异点”,最后利用交通信号配时软件SYNCHRO制定了相应时段的控制方案。相关的案例分析显示,优化后的多时段控制方案可以更好地适应交通流的波动,车均延误减少9.79%,其中时序性考虑可以有效改善控制方案频繁切换对交通流的扰动,避免其负效应对混合聚类改善效果的影响。     

Web日志挖掘与上网行为习惯分析

本文以某校校园网Web日志挖掘为例,首先阐述如何进行Web挖掘,其次运用该理论分析该校Web日志得到用户上网行为习惯,并给出合理化建议,最后引出进一步探索的方向,为以后工作奠定基础。本文采用的挖掘方法具有通用性和实用性,对于类似时序序列趋势分析都可参考本文。     

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.     

Bacteria foraging optimization for protein sequence analysis on the grid

Scientific applications such as protein sequence analysis require a coordination of resources. This is due to hundreds and hundreds of protein sequences being deposited into data banks by the research community which results in an extensive database search when one wants to find a similar protein sequence. This search becomes easier and the time taken is reduced when it is conducted in a grid environment implemented using the Globus tool kit. This paper proposes the use of Bacteria Foraging Optimization (BFO) for finding similar protein sequences in the existing databases. Usage of BFO further reduces the time taken by a resource to execute the user’s requests. Also, the resources utilized in the proposed method are better balanced compared to the existing scheduling algorithms. Also, it is found that the number of tasks executed is more compared to the existing algorithms even though there is a fall in the execution of tasks as the number of resources increases which might be due to network failure etc. The proposed BFO has been compared with the existing First Come First Serve (FCFS) and Minimum Execution Time (MET) scheduling algorithms and it has been found that the proposed BFO performs well compared to the existing algorithms in terms of makespan, resource utilization and minimization in the case of non-execution of client requests.     

Geoadditive expectile regression

Quantile regression has emerged as one of the standard tools for regression analysis that enables a proper assessment of the complete conditional distribution of responses even in the presence of heteroscedastic errors. Quantile regression estimates are obtained by minimising an asymmetrically weighted sum of absolute deviations from the regression line, a decision theoretic formulation of the estimation problem that avoids a full specification of the error term distribution. Recent advances in mean regression have concentrated on making the regression structure more flexible by including nonlinear effects of continuous covariates, random effects or spatial effects. These extensions often rely on penalised least squares or penalised likelihood estimation with quadratic penalties and may therefore be difficult to combine with the linear programming approaches often considered in quantile regression. As a consequence, geoadditive expectile regression based on minimising an asymmetrically weighted sum of squared residuals is introduced. Different estimation procedures are presented including least asymmetrically weighted squares, boosting and restricted expectile regression. The properties of these procedures are investigated in a simulation study and an analysis on rental fees in Munich is provided where the geoadditive specification allows for an analysis of nonlinear effects of the size of flats or the year of construction and the spatial distribution of rents simultaneously.