Wrocław University of Technology

About: Wrocław University of Technology is a education organization based out in Wrocław, Poland. It is known for research contribution in the topics: Laser & Computer science. The organization has 13115 authors who have published 31279 publications receiving 338694 citations.

Energy price risk management

Abstract: The price of electricity is far more volatile than that of other commodities normally noted for extreme volatility. Demand and supply are balanced on a knife-edge because electric power cannot be economically stored, end user demand is largely weather dependent, and the reliability of the grid is paramount. The possibility of extreme price movements increases the risk of trading in electricity markets. However, a number of standard financial tools cannot be readily applied to pricing and hedging electricity derivatives. In this paper we present arguments why this is the case.

Filter theory of BL algebras

Abstract: In this paper we consider fundamental properties of some types of filters (Boolean, positive implicative, implicative and fantastic filters) of BL algebras defined in Haveshki et al. (Soft Comput 10:657–664, 2006) and Turunen (Arch Math Logic 40:467–473, 2001). It is proved in Haveshki et al. (2006) that if F is a maximal and (positive) implicative filter then it is a Boolean filter. In that paper there is an open problem Under what condition are Boolean filters positive implicative filters? One of our results gives an answer to the problem, that is, we need no more conditions. Moreover, we give simple characterizations of those filters by an identity form ∀ x, y(t(x, y) ∈ F), where t(x, y) is a term containing x, y.

Design of Compensated Coupled-Stripline 3-dB Directional Couplers, Phase Shifters, and Magic-T's—Part I: Single-Section Coupled-Line Circuits

Abstract: The problem of discontinuities in coupled-stripline 3-dB directional couplers, phase shifters, and magic-T's, regarding the connections of coupled and terminating signal lines, has been comprehensively investigated for the first time. The proposed equivalent circuit of these discontinuities, of which parameters can be computed in a process of fitting curves of the circuit and electromagnetic analyses, has been used for accurate modeling of coupled-stripline circuits. It has been shown that parasitic reactances, which result from connections of signal and coupled lines, severely deteriorate the return losses and the isolation of such circuits, and that these discontinuity effects can be substantially reduced by connecting compensating shunt capacitances to both coupled and signal lines. Results of measurements carried out for various designed and manufactured coupled-line circuits are most promising and prove the efficiency of the proposed compensation technique. This paper describes the technique of capacitive compensation of discontinuity effects in single-section coupled-line circuits

Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data

Abstract: Extending binary ensemble techniques to multi-class imbalanced data.OVO scheme enhancement for multi-class imbalanced data by ensemble learning.A complete experimental study of comparison of the ensemble learning techniques with OVO.Study of the impact of base classifiers used in the proposed scenario. Multi-class imbalance classification problems occur in many real-world applications, which suffer from the quite different distribution of classes. Decomposition strategies are well-known techniques to address the classification problems involving multiple classes. Among them binary approaches using one-vs-one and one-vs-all has gained a significant attention from the research community. They allow to divide multi-class problems into several easier-to-solve two-class sub-problems. In this study we develop an exhaustive empirical analysis to explore the possibility of empowering the one-vs-one scheme for multi-class imbalance classification problems with applying binary ensemble learning approaches. We examine several state-of-the-art ensemble learning methods proposed for addressing the imbalance problems to solve the pairwise tasks derived from the multi-class data set. Then the aggregation strategy is employed to combine the binary ensemble outputs to reconstruct the original multi-class task. We present a detailed experimental study of the proposed approach, supported by the statistical analysis. The results indicate the high effectiveness of ensemble learning with one-vs-one scheme in dealing with the multi-class imbalance classification problems.