Applied Mathematics in Engineering and Reliability

Radim Briš is research worker and university professor in applied mathematics and technical cybernetics in the Faculty of Engineering and Computer Science of the VŠB-Technical University of Ostrava, Czech Republic. He is also senior researcher in applied statistics and operational research at the Centrum Excelence IT4Innovations at the same university, and team leader for the Stochastic reliability modelling and risk management group. Dr. Briš recently became Vice-President of European Safety and Reliability Association, and is a member of ESRA Management Board. Previously, he spent one year as invited research professor at the Laboratoire de modélisation et de Sureté des Systemes, at the Université de Technologie de Troyes, France. He also spent 5 years in the position of Vice-Dean of the Faculty of Electrical Engineering and Computer Science, VŠB TU Ostrava.

• I - Applied Mathematics in Reliability Engineering; Bayesian methods, Bayesian reliability: A conjugate prior distribution for Bayesian analysis of the Power-Law Process; Bayesian approach to estimate the mixture of failure rate model; Cost oriented statistical decision problem in acceptance sampling and quality control; High-dimensional simulation experiments with particle filter and ensemble Kalman filter; The prior probability in classifying two populations by Bayesian method; Efficient Methods to Solve Optimization Problems; Estimation of parameters of Rikitake systems by SOMA; Clustering for probability density functions based on Genetic Algorithm; Optimization of truss structures with reliability-based frequency constraints under uncertainties of loadings and material properties; Optimum revenue calculation method to generate competitive hydroelectric power on Hua Na hydropower; Maintenance Modelling and Optimization; Network and Wireless Network Reliability; Risk and Hazard Analysis; System Reliability Analysis; II - Advanced Mathematical Methods for Engineering; Advanced Methods to Solve Partial Differential Equations; Inverse Problems; Advanced Numerical Methods; Statistics and Applied Statistics.