Browsing by Author "Yumurtaci, M"

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    Designing a Real-time Remote Control System for Undergraduate Engineering and Engineering Technology Students
    Yabanova, I; Taskin, S; Ekiz, H; Oguz, Y; Akaslan, D; Yumurtaci, M
    This paper explains the design of a control system intended to enhance the quality of education in engineering and engineering technology departments and considers the use of various concepts: remote-access, reconfigurable properties, and multi-user controls. Controlling the system's hardware is possible by using fuzzy logic and PID control methods to teach several engineering concepts in the field of automatic control (i.e., DC motor and temperature control). MD parameters and fuzzy logic membership rules can be modified by authorizing remote users. A Web interface was also designed as part of the system to create an opportunity for the end-users to observe real-time changes in DC motor and temperature control. Modifications to the control system's parameters also enable users to record output to their own personal computers via the Internet; this allows users from. other engineering institutions to access and modify the PID and fuzzy logic membership rules for more experiences. In ibis way, various theoretical and practical concepts associated with the field of automatic control can be implemented without any limitations. The overall findings from this study indicate that engineering and engineering technology curricula in any institution can effectively be delivered using the Internet to overcome the limitations of distance, as remote-access experiments are becoming highly applicable anywhere and anytime.
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    Autoencoder-Based Eggshell Crack Detection Using Acoustic Signal
    Yabanova, I; Balci, Z; Yumurtaci, M; Ünler, T
    Breaks or cracks in eggshells offer substantial food safety issues. Bacteria and viruses, in particular, are more likely to enter the egg through breaks and cracks, increasing the risk of food poisoning. Furthermore, deformations in the shell may compromise the integrity of the protective shell, exposing the egg to more external variables and causing it to lose freshness and decay faster. To reduce such hazards, this research created an innovative crack detection system based on an autoencoder (AE) that uses acoustic signals from eggshells. A system that creates an acoustic effect by hitting the eggshell without damaging it was designed, and these effects were recorded through a microphone. Acoustic signal data of size 1 x 1000 was fed into k nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM) classifiers. AE was employed to reduce data size in order to accommodate the raw data's unique features. This AE model, which reduces data size, was used with many classifiers and was able to accurately distinguish between intact and cracked eggs. The built AE-based classifier model completed the classification procedure with 100% accuracy, including microcracks that are invisible to the naked eye.