Optimized High Frequency Lumped Parameters Model of Induction Motor Using Genetic Algorithm (GA)

Abstract: In this paper an optimized high frequency lumped model of Induction motor is presented. Model parameters are identified and optimized using Genetic Algorithm (GA). A novel model and approach in an improved high frequency based on GA for parameter identification are used. At first, parameters are limited and then fitted using GA for best fitting. The proposed model considered accurate simulation of both differential and common mode behavior in the EMI-frequency range from 100 Hz to 30MHz. Model parameters which extracted from GA are compared with experimental data in both magnitude and phase at the same time and results show a good accordance between the experimental results and simulation results of the proposed model. A least mean square (LMS) method was used with a GA optimization method to solve the identification problem. The proposed model is suitable to obtain the simulation models to predict high frequency conducted Electromagnetic Interference (EMI), over voltage on terminated motor and common mode current in cable fed induction motor

Human Linguatulosis Caused by Linguatula serrata in the City of Kerman, South-eastern Iran- Case Report

Human linguatulosis poses an important medical and veterinary concern in en-demic countries. Animals, as reservoir host, play a major role in transmission of infestation and epidemiology of the disease. This study reports a case of human linguatulosis caused by Linguatula serrata in the city of Kerman, South-eastern Iran. A woman suffering from upper respiratory symptoms is presented. The pa-tient consumed raw liver of sheep who was admitted to the Afzalipour University Hospital in Kerman for the symptoms of upper respiratory tract. In microscopic examination of the nasopharyngeal discharge, L. serrata was detected. This report has future medical implication in precise diagnosis of L. serrata in patients with complaints of nasopharyngeal symptoms