13 research outputs found
Soft-Sensor Method Based on Least Square Support Vector Machines Within Bayesian Evidence Framework
System Integration at Karlovarsky kraj Municipality
Import 26/06/2013Práce se zabývá možností řešení systémové integrace vybraných aplikací informačního systému Krajského úřadu Karlovarského kraje včetně řešení řízení přístupů uživatelů do informačního systému Krajského úřadu Karlovarského kraje.
Práce obsahuje analýzu současného stavu řízení v oblasti informačních a komunikačních technologií, analýzu legislativy a její implementace vnitřními předpisy do prostředí Krajského úřadu Karlovarského kraje. Dále jsou analyzovány vybrané aplikace pro integraci a jejich moduly z hlediska obsahu a funkčnosti včetně analýzy přidělených oprávnění těchto systémů a aktuální využívání jednotlivými pracovníky.
Provedená analýza je základem doporučeného řešení integrace a řízení přístupů v rámci informačního systému Krajského úřadu Karlovarského kraje a je podloženo metodikou návrhu řešení.
Výsledkem práce je doporučení řešení řízení přístupů a integrace aplikací informačního systému Krajského úřadu Karlovarského kraje.This text deals with the possibility of solving problem where chosen applications in the information system of the Country council of Carlsbad region should be systematically integrated, including the solution managing access of the users to this information system.
The text contains an analysis of contemporary administration state in the information and communication technologies area, legislation analysis and its implementation into the Country council of Carlsbad region environment by the internal regulations means. Chosen applications which serves for integration and theirs modules, are also analyzed from the point of view of theirs content and functionality, including the analysis of assigned authorization to these applications and contemporary usage of them by individual workers.
Completed analysis should be the basic recommended ground for solution of integration and access governing in the framework of information system of the Country council of Carlsbad region and is underlain by the methodology of solution drafting.
Recommended solution of managing access and integration of applications in information system of the Country council of Carlsbad region is the result of this text.
This text results in the recommended solution of managing access and applications integration in information system of the Country council of Carlsbad regionPrezenční545 - Institut ekonomiky a systémů řízenívýborn
A Systematic Fire Detection Approach Based on Sparse Least-Squares SVMs
In this paper, a systematic approach adopting sparse least-squares SVMs (LS-SVMs) is proposed to automatically detect fire using vision-based systems with fast speed and good performance. Within this framework, the features are first extracted from input images using wavelet analysis. The LS-SVM is then trained on the obtained dataset with global support vectors (GSVs) selected by a fast subset selection method, in the end of which the classifier parameters can be directly calculated rather than updated during the training process, leading to a significant saving of computing time. This sparse classifier only depends on the GSVs rather than all the patterns, which helps to reduce the complexity of the classifier and improve the generalization performance. Detection results on real fire images show the effectiveness and efficiency of the proposed approach
Forecasting KOSPI Using a Neural Network with Weighted Fuzzy Membership Functions and Technical Indicators
Kernel Trick Embedded Gaussian Mixture Model
Abstract. In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Moreover, it can tackle nonlinear problems better than the traditional GMM. To avoid great computational cost problem existing in most kernel methods upon large scale data set, we also employ a Monte Carlo sampling technique to speed up kernel GMM so that it is more practical and efficient. Experimental results on synthetic and real-world data set demonstrate that the proposed approach has satisfing performance.