Intelligent Systems for Computer Modelling: Proceedings of the 1st European-Middle Asian Conference on Computer Modelling 2015, EMACOM 2015 [Paperback]

This volumeof Advances in Intelligent Systems and Computing contains papers presented atthe 1st European-Middle Asian Conference on Computer Modelling, EMACOM 2015.This international conference was conceived as a brand new scientific andsocial event of mutual collaboration between the VSB - Technical University ofOstrava (Ostrava, Czech Republic) and the Kyrgyz National University namedafter J. Balasagyn (Bishkek, Kyrgyz Republic). The aim of EMACOM 2015 was topresent the latest development in the field of computer-aided modelling as anessential aspect of research and development of innovative systems and theirapplications. The conference showed that together with simulations, variousmodeling techniques, enabled and encouraged by the rapid development ofhigh-performance computing platforms, are crucial for cost-efficient design,verification, and prototyping of solutions in many diverse industrial fieldsspanning the whole range from manufacturing, mining, machinery, and automotiveindustries to infrastructure planning and development, economics, energy, andmodern agriculture and food industry.

Finite Element Modelling of T-Plate for Treatment of Distal Radius.- The Enlarged D-Q Model of Induction Motor with the Iron Loss and Saturation Effect of Magnetizing and Leakage Inductance.- Fuzzy Model Based Intelligent Prediction of Objective Events.- Using Multiple Scanning Devices for 3-D Modeling.- More Effective Control of Linear Switched-Reluctance Motor Based on the Research of Electromagnetic Processes of Field Theory Methods Linear Electrical Machines.- Numerical Simulation of the Structural Elements of a Mobile Micro-Hydroelectric Power Plant of Derivative Type.- Complex System Modeling with General Dierential Equations Solved by Means of Polynomial Networks.- Monte Carlo Probabilistic Approach Applied for Solving Problems in Mining Engineering.- Investment Funds Management Strategy Based on Polynomial Regression in Machine Learninlãk