محمدعلی افتخاری

The aim of this study is to develop imaging algorithms to improve the grade of nuts with shell defects such as oily stains, dark stains, adhering hull, damage seed defects, and fungal decay. All these defects indicate the risk of Aflatoxin contamination. Convolutional Neural Networks (CNNs) have become prominent in various fields of machine vision and image classification. In this study, a laboratory hardware setup based on a convolutional neural network is designed for sorting pistachios. The total number of collected data is 958 images, which includes 276 images of defective pistachios and 682 images of healthy pistachios. The classification of healthy and defective images has been accomplished by 3 types of deep convolutional neural networks including Google net, resnet18 and vgg16. The accuracy and specificity of the results obtained using the pre-trained deep neural network models of Google net, resnet18 and vgg16 are 95.8% -97.1%, 97.2% -96.7%, and 95.83% -97.08%, respectively.

The basis of the photovoltaic systems is the conversion of solar energy into electrical energy. Therefore, the maximum efficiency of photovoltaic systems is achieved by placing the system at a vertical angle to the sun. To achieve the maximum power and efficiency of the photovoltaic cell, tracking the position of the sun with high precision is important. A new linear-rotational dual axis sun tracker is designed and built to be robust against environmental impacts and to minimize the energy consumption of motors. Meanwhile, controller parameters have been optimized with Imperialism Competitive Algorithm (ICA) in order to track faster and more energy saving. Optimizing control system parameters for two cases--one axis and two axes-- has optimzed power production so much so that in addition to reducing power and time, the energy consumption of motors is also reduced. Meanwhile, in order to demonstrate the efficiency and performance of the system, a comparison was made between the tracking the speed and energy consumption of motors with Genetic Algorithms (GA), Artificial Bee Colony (ABC), Chaotic Particle Swarm Optimization algorithm (CPSO) and Imperialism Competitive Algorithm (ICA). The results indicate that the ICA has a better performance than other algorithms.