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Klagenfurt am Worthersee, Austria

The University of Klagenfurt is a federal Austrian University and the largest research and higher education institution in the Austrian province of Carinthia. It has its main campus in Klagenfurt, with additional facilities in Austria's two largest cities Vienna and Graz. Efforts to found the University began in 1964, and succeeded in 1970, during the term of Governor Sima. Today, the University holds faculties of Humanities & Social science, Management & Economics, Technology, and Interdisciplinary Studies. The School of Interdisciplinary Studies also runs departments in Vienna and Graz; the Dean of IFF, Verena Winiwarter, was elected Austrian "Scientist of the Year" 2013. In addition to the departments and units of the four faculties, the University also hosts a number of central facilities such as the Robert Musil Institute , the University Cultural Center , the build! Gründerzentrum , the University Sports Center , and the Klagenfurt University Library.The incumbent President of the University is Oliver Vitouch, a cognitive psychologist and former faculty member of the University of Vienna and the Max Planck Institute for Human Development in Berlin. Norbert Frei chairs the Academic Senate, Robert Rebhahn the Board of Trustees.Klagenfurt is the southernmost university in the German-speaking countries and supports bi- and multilingualism, especially in the context of the Slovenian minority in Carinthia . Wikipedia.

Potzsche C.,Klagenfurt University
Nonlinear Analysis: Real World Applications

This paper investigates local and global bifurcation, as well as continuation properties for discrete-time periodic dynamical models in arbitrary (finite) dimension. Our focus is to provide explicitly verifiable conditions which guarantee or prevent bifurcations of, say ω1-periodic solutions for ω0-periodic difference equations. In doing so, we give concrete branching relations ensuring bifurcations of e.g. fold, transcritical, pitchfork or flip type, including information on the global branches. Beyond that we obtain formulas indicating the local behavior of mean population sizes under parameter variation or bifurcation, and furthermore tackle stability issues. Our results are applied to various real-world population models. Thus, the paper will be useful for a thorough analysis and understanding of general periodic time-discrete models in population dynamics, life sciences and beyond. © 2012 Published by Elsevier Ltd. Source

The aim of this paper is to show the economic potential of demand response (DR) on household level at Central European market conditions. Thereby, required economic benefits for consumers' participation, the realistic load shifting potential at household level and the estimation of essential intelligent infrastructure costs are discussed. The core of this paper builds a case-study applying spot market-oriented load shifting from the supplier's point of view by using Austrian electricity market data, household load profiles as well as a heat pump and e-car charging load profile. It is demonstrated which cost savings for suppliers can be derived from such load shifting procedure at household level. Furthermore, upper cost limits for intelligent infrastructure in order to break-even are derived. Results suggest to take a critical look at European discussions on DR implementation on household level, showing that at Central European market conditions the potential for DR at household level is restricted to significant loads and hence, the applied load shifting strategy is only beneficial with application to heat pumps. In contrast, the frequently discussed shifting of conventional household devices' loads (such as washing machines) economically does not add up. © 2013 Elsevier Ltd. Source

Bouchachia A.,Klagenfurt University
Soft Computing

The persistence and evolution of systems essentially depend on their adaptivity to new situations. As an expression of intelligence, adaptivity is a distinguishing quality of any system that is able to learn and to adjust itself in a flexible manner to new environmental conditions and such ability ensures self-correction over time as new events happen, new input becomes available, or new operational conditions occur. This requires self-monitoring of the performance in an ever-changing environment. The relevance of adaptivity is established in numerous domains and by versatile real-world applications. The present paper presents an incremental fuzzy rule-based system for classification purposes. Relying on fuzzy min-max neural networks, the paper explains how fuzzy rules can be continuously online generated to meet the requirements of non-stationary dynamic environments, where data arrives over long periods of time. The approach proposed to deal with an ambient intelligence application. The simulation results show its effectiveness in dealing with dynamic situations and its performance when compared with existing approaches. © 2010 Springer-Verlag. Source

Bouchachia A.,Klagenfurt University

Self-adaptation is an inherent part of any natural and intelligent system. Specifically, it is about the ability of a system to reconcile its requirements or goal of existence with the environment it is interacting with, by adopting an optimal behavior. Self-adaptation becomes crucial when the environment changes dynamically over time. In this paper, we investigate self-adaptation of classification systems at three levels: (1) natural adaptation of the base learners to change in the environment, (2) contributive adaptation when combining the base learners in an ensemble, and (3) structural adaptation of the combination as a form of dynamic ensemble. The present study focuses on neural network classification systems to handle a special facet of self-adaptation, that is, incremental learning (IL). With IL, the system self-adjusts to accommodate new and possibly non-stationary data samples arriving over time. The paper discusses various IL algorithms and shows how the three adaptation levels are inherent in the system's architecture proposed and how this architecture is efficient in dealing with dynamic change in the presence of various types of data drift when applying these IL algorithms. © 2011 Elsevier B.V. Source

Kazianka H.,Klagenfurt University
Stochastic Environmental Research and Risk Assessment

The spatialCopula toolbox contains a set of Matlab functions that provides utilities for copula-based analysis of spatially referenced data, a topic which has re cently attracted much attention in spatial statistics. These tools have been developed to support the work flow in parameter estimation, spatial interpolation and visualization. They offer flexible and user-friendly software for dealing with non-Gaussian and extreme value data that possibly contain a spatial trend or geometric anisotropy. The objective of this paper is to give an introduction to the concept behind the software and to outline the functionality of the toolbox. We illustrate its usefulness by analyzing a data set here referred to as the Gomel data set, which includes moderately skewed radioactivity measurements in the region of Gomel, Belarus. The source codes are freely available in Matlab language on the author's website (fam.tuwien.ac.at/~hakazian/software.html). © 2012 Springer-Verlag. Source

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