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Go to Editorial ManagerPorosity is one of the most important reservoir characteristics because it indicates to fluid collection. Several techniques used to get good porosity prediction, so, in this study we employed seismic attributes and well log data in a genetic algorithm to get the best porosity prediction. The study attempt to enhance the performance of genetic algorithm for attribute selection and therefore porosity prediction by applying genetic algorithm on different types of fitness functions like average mean square error fitness, average correlation coefficients fitness and performance index fitness. Also, used two methods to represent attributes in genetic algorithm. Different witnesses applied to choose the appropriate fitness function that gives high porosity prediction.
The daily peak load forecasting for the next day is the basic operation of generation scheduling. The approach of using ANN methodology alone is limited which has generated interest to explore hybrid system. In this paper a search of genetic programming to a short term load forecasting is presented. A genetic architecture with the fitness normalization has been used to find as optimum data peak load of Baghdad city. The optimize data applied to the ANN to be trained and tested to estimate the daily peak load of Baghdad city. Different cases for load forecasting are considered with the aid of MATLAB 7 package to get the estimation of the next day. So an improvement method of genetic optimization is proposed to get a better solution for the load estimation rather than artificial neural network.
The Unified Power Flow Controller (UPFC) is a most complex power electronic device, which can simultaneously control a local bus voltage and optimize power flows in the electrical power transmission system. This paper presents the effect of installing the UPFC on the Iraqi (400 kV) grid transmission system to control the active and reactive power flow by choosing the optimal location and parameters of Unified Power Flow Controllers (UPFCs), which were specified based on the Genetic Algorithm (GA) optimization method. The objectives are improving voltage profile, reducing power losses, treating power flow in overloaded transmission lines, and reducing power generation. The steady state model of UPFC has been adopted on (400 kV) Iraq transmission lines and simulated using the MATLAB programming language. The Newton-Raphson (NR) numerical analysis method has been used for solving the load flow of the system. The practical part has been solved through Power System Simulation for Engineers (PSS\E) software Version 32.0. The Comparative results between the experimental and practical parts obtained from adopting the UPFC were too close and almost the same under different loading conditions, which are (5%, 10%, 15% and 20%) of the total load.
In this paper, we suppose a method for reducing the dispersion in the plastic optical fiber (POF) Bragg gratings based on optimizing the grating coupling-strength (?) using genetic algorithms. The effects of average refractive index (?n) and temperature (T) change on the dispersion properties are investigated numerically. It is found that the amplitude of the ?n for low dispersion performance needs to be reduced at the cost of the design complexity of the POF Bragg gratings. Owing to the unusually large and negative thermo-optic coefficient of the POF, the dispersion due to the wavelength shift induced by the temperature variation will be reduced by operating at high ? value. Results showed that by optimizing the ? value a very large dispersion reduction range has been obtained, from 1178 to 11.5 ps/nm at 30 mm grating length.
The effect of defect on structures and machines has negative consequences on them and it always takes researchers concern and attention in order to find feasible solutions to trace and detect the location of the defect accurately.In this research, the effect of a hole with different diameters on a square composite plate is studied as well as the effects of both the boundary condition and the plate thickness, furthermore, Vibration analysis of composite plate has been studied numerically and experimentally. The Numerical analysis has been carried out by using FEM by building MATLAB program as well as (ANSYS 15). The experimental part of this research is done by using vibration measuring instruments. The rate of error among the experimental tests and the numerical solution is less than 15%. These results have been used an inputs to the Genetic Algorithm model that the defect is located by, with a high percentage of success.
Mobile robots use simultaneous localization and mapping (SLAM) techniques for generating maps of unknown environments through navigating its. In this work, firstly SLAM technique was considered based on extended Kalman filter (EKF) which it was implemented and evaluated at unknown environments with different number of landmarks to estimate mobile robot’s position and build a map for navigated environment at the same time. Then, the detectable landmarks will play an important role in controlling the overall navigation process as well EKF-SLAM technique’s performance. After that, three intelligent optimization algorithms are proposed to enhance the performance of the EKF-SLAM trajectory for the mobile robot, these algorithms are: particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO) and genetic optimization (GA). MATLAB simulation results show that CPSO algorithm outperforms PSO and GA algorithms in terms of minimizing the mean square error (MSE1) with increasing the number of landmarks, where MSE1 is the mean square error of EKF-SLAM according to the actual trajectory. The simulation results show also the performance of EKF-SLAM trajectory is better than the performance of the Odometry trajectory and becomes best with using intelligent optimization algorithms.