Technical University of Ostrava

About: Technical University of Ostrava is a education organization based out in Ostrava, Czechia. It is known for research contribution in the topics: Artificial neural network & Evolutionary algorithm. The organization has 4186 authors who have published 8936 publications receiving 65393 citations. The organization is also known as: Vysoká škola báňská – Technická univerzita Ostrava & VŠB – Technical University of Ostrava.

Emerging regional innovation strategies in Central Europe: institutions and regional leadership in generating strategic outcomes

Abstract: The paper seeks to develop a comparative analysis of approaches to innovation support in three self-governing regions of the Czech Republic. Its analytical section presents an in-depth analysis of the development of innovation policies in three regions: the capital city of Prague, South Moravia and the old industrial region of Moravia-Silesia. Key dimensions of regional innovation strategy in each of the three regions are closely scrutinized and critically examined, within the context of state-of-the-art European approaches to innovation policy. Profound differences, both in approaches to innovation policy design and in the results so far achieved, have been found between the studied regions, reflecting differences in both structural and soft factors in the regions in question. Rapid progress, in terms of innovation strategy implementation, is evident in a region where strong knowledge creation capacity (in both the academic and the business spheres) exists in harmony with professional and enthusiastic ke...

On Concept Lattices and Implication Bases from Reduced Contexts.

Abstract: Our paper introduces well-known methods for compressing formal context and focuses on concept lattices and attribute implication base changes of compressed formal contexts. In this paper Singular Value Decomposition and Non-negative Matrix Factorisation methods for compressing formal context are discussed. Computing concept lattices from reduced formal contexts results in a smaller number of concepts (with respect to the original lattice). Similarly, we present results of experiments in which we show a way to control smoothly the size of generated Guigues-Duquenne bases and provide some noise resistance for the basis construction process.

Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network

Abstract: This paper presents one of the soft computing methods, specifically the artificial neural network technique, that has been used to model the temperature dependence of dynamic mechanical properties and visco-elastic behavior of widely exploited thermoplastic polyurethane over the wide range of temperatures. It is very complex and commonly a highly non-linear problem with no easy analytical methods to predict them directly and accurately in practice. Variations of the storage modulus, loss modulus, and the damping factor with temperature were obtained from the dynamic mechanical analysis tests across transition temperatures at constant single frequency of dynamic mechanical loading. Based on dynamic mechanical analysis experiments, temperature dependent values of both dynamic moduli and damping factor were calculated by three models of well-trained multi-layer feed-forward back-propagation artificial neural network. The excellent agreement between the modeled and experimental data has been found over the entire investigated temperature interval, including all of the observed relaxation transitions. The multi-layer feed-forward back-propagation artificial neural network has been confirmed to be a very effective artificial intelligence tool for the modeling of dynamic mechanical properties and for the prediction of visco-elastic behavior of tested thermoplastic polyurethane in the whole temperature range of its service life.