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    Design and Implementation of Educational Neural Networks Visual Interface Program
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ergin, Ahmet Sait; Özgüven, Ömerülfaruk
    In this study, a visual computer application was carried out in order to be a support material for the students studying on Artificial Neural Networks, which were developed inspired by the operation of the nerve cells of living things and used in solving nonlinear problems such as classification or regression. CSharp.Net, an object-oriented software development tool, has been used for easy management of the application in which complex algorithms will be executed due to its structure. The most important purpose of the implemented software and its advantage over other similar applications is that it is user-friendly, that is, easy to use. The student who has studied the subject theoretically will be able to easily design the artificial neural network, the diagram of which can be followed visually, train the network and instantly monitor the minimum error graph reached at the end of the training. While performing these processes, an application has been designed in which the student can manage the application with the skills of the mouse, without the need to know any programming language, that is, without writing any code, even using the keyboard at a minimum. Since the main purpose of my application is to comprehend the subject with the help of visual elements, it is focused on a single problem solution (regression). The data of the logical logic gates, which are widely used in teaching the subject, are given ready in the application interface, and for other problem scenarios, an external data entry procedure is provided from the Excel application in order to easily enter the data set into the application.
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    Low Cost effective Real Time Detection of Fabric Defects with CCD Linear Image Sensor
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ergin, Ahmet Sait; Özgüven, Ömerülfaruk
    In this study, real-time detection of defects of fabrics produced on knitting machines was carried out by using CCD (Charge Coupled Device) linear image sensor. To simulate knitting machines, a cylindrical drum rotated with a direct current motor was designed, and work was carried out with faulty and faultless fabrics wound on this drum. A lowcost linear line camera sensor containing 1500 active pixels was chosen for this study. The driver circuit of the selected image sensor requires interdependent and very precise timer signals. For this reason, a development board containing a 32-bit microcontroller with an operating frequency of 100 MHz with a superior price/performance ratio was used to control the sensor. In addition, a light source with a wavelength between 500 nm and 600 nm, to which the sensor is maximum sensitive, was used. Linear dash camera sensor has analog output. For this reason, every image pixel detected by the sensor is converted into digital. Since the microcontroller used in the study supports direct memory access, the data converted from analog to digital are automatically saved in the memory. Thus, both the maximum speed supported by the development board in analog-digital conversion has been reached, and it has been possible to perform the control in real time by processing the previous line data in this hardware-driven processing time period. Since two-dimensional image analysis algorithms require very high processing power and it is not possible for microcontrollers to perform this process in real time, image analysis was performed for one dimension, ie row. The fabric defect was detected by monitoring the statistical parameters, the drum was stopped by the control circuit of the motor, and the machine operator was simulated to be informed of the error with the audible warning system at the same time.