Print ISSN: 1681-6900

Online ISSN: 2412-0758

Author : A. R. Akkar, Hanan


Intelligent controller Design based on wind-solar system

Areeg F. Hussein; Hanan A. R. Akkar

Engineering and Technology Journal, 2021, Volume 39, Issue 2, Pages 326-337
DOI: 10.30684/etj.2021.168115

This paper presents an Intelligent controller designed to mastery the output power flow from the Solar System, the Wind system, the sum of the two systems or from the battery system, according to the Maximum power point tracking algorithm, to ensure the continuity of the output power at fast time response. The proposed controller has been designed using MATLAB m-file and trained with the different number of hidden neurons using two different algorithms to get as fast a response time with minimum Mean Square Error (MSE) as possible which resulted in six hidden neurons using Levenberg-Marquardt training algorithms.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

Hanan A. R. Akkar; Sameem A. Salman

Engineering and Technology Journal, 2020, Volume 38, Issue 2, Pages 255-264
DOI: 10.30684/etj.2021.168194

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

Artificial Intelligent Technique for Power Management Lighting Based on FPGA

Hanan A. R. Akkar; Sameh J. Mohammed

Engineering and Technology Journal, 2020, Volume 38, Issue 2, Pages 232-239
DOI: 10.30684/etj.2021.168191

The modern technological advances gave rise to new intelligent ways of performance and management in various fields of our lives. The employment of the artificial intelligent techniques proved influential in enhancing the technological developments and in meeting the demands for new, more efficient, more reliable and faster ways of performing activities and tasks. Lighting systems are an important part of human life. For this reason, it is important to reduce and manage energy consumption properly. Light dimming paves the way for massive energy saving in lighting applications. The options include simply reducing the output during the night and achieve maximum saving with variable dimming. Advantage can be taken of off-peak times (no light needed) to reduce energy consumption significantly. Pulse Width Modulation (PWM) technique is used as dimming method. The proposed system offers intelligent management of lighting to reduce power consumption, extend lamp life and reduce maintenance. In this work, we will be using multiple sensors such as light dependent resistor (LDR) and Motion Sensor (PIR) for LED dimming system to achieve intelligent LED lighting system to manage energy consumption. The data collected by sensors is processed by Artificial Neural Network (ANN), which is implemented by using Field Programmable Gate Arrays (FPGAs), Spartan 3A starter kit that controls the light intensity of LED from changing the duty cycle of the PWM signals. FPGA was used to implement the design, because of the re-programmability of the FPGAs, which can support the re-configuration necessary to implement the design. VHDL program was used to describe the functions of all necessary components used. Xilinx ISE 14.7 design suite and MATLAB R2012A were used as software tools to perform Spartan 3A starter kit program. The Simulation results were obtained with Xilinx blocks found in MATLAB program.