Bücher von Aydin Azizi

This book presents improved numerical techniques and applied computer-aided simulations as a part of emerging trends in mechatronics in all areas related to complex fluids, with particular focus on using a combination of modeling, theory, and simulation to study systems that are complex due to the rheology of fluids (i.e., ceramic pastes, polymer solutions and melts, colloidal suspensions, emulsions, foams, micro-/nanofluids, etc.) and multiphysics phenomena in which the interactions of various effects (thermal, chemical, electric, magnetic, or mechanical) lead to complex dynamics. The areas of applications span materials processing, manufacturing, and biology.

This book provides an in-depth understanding of the fundamental scientific principles and technologies used in the design of modern computer-controlled machines and processes. It emphasizes the synergies in the design process and explores the challenges and opportunities for integrating diverse engineering disciplines. The book consists of six chapters that cover a wide range of topics related to mechatronics and control system engineering. Overall, the book is an excellent resource for professionals, engineers, researchers, and students who want to gain a comprehensive understanding of the trans-disciplinary field of mechatronics and control systems engineering.

This book provides a concise introduction to the behavior of mechanical structures and testing their stochastic stability under the influence of noise. It explains the physical effects of noise and in particular the concept of Gaussian white noise.

This book is to presents and evaluates a way of modelling and optimizing nonlinear RFID Network Planning (RNP) problems using artificial intelligence techniques. It uses Artificial Neural Network models (ANN) to bind together the computational artificial intelligence algorithm with knowledge representation an efficient artificial intelligence paradigm to model and optimize RFID networks.This effort leads to proposing a novel artificial intelligence algorithm which has been named hybrid artificial intelligence optimization technique to perform optimization of RNP as a hard learning problem. This hybrid optimization technique consists of two different optimization phases. First phase is optimizing RNP by Redundant Antenna Elimination (RAE) algorithm and the second phase which completes RNP optimization process is Ring Probabilistic Logic Neural Networks (RPLNN).The hybrid paradigm is explored using a flexible manufacturing system (FMS) and the results are compared with well-known evolutionary optimization technique namely Genetic Algorithm (GA) to demonstrate the feasibility of the proposed architecture successfully.