Browsing by Author "ISMAIL YABANOVA"

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    Denetleyici alan ağı üzerinden mekatronik bir sistemin kontrolü
    (2010) ISMAIL YABANOVA; SEZAI TASKIN; Hüseyin EKİZ; Hasan ÇİMEN
    Bu çalısmada denetleyici alan ağı (DAA) ve LabVIEW grafiksel programlama dili kullanılarak Esnek Üretim Sistemi (EÜS) istasyonları üzerinden veri toplama ve kontrol uygulaması gerçeklestirilmistir. Olusturulan yapı ile mekatronik bir sistemin gözlemlenmesi ve kontrolü mevcut yapısına göre daha güvenli ve ekonomik bir sekilde gerçeklestirilmistir. DAA üç düğümden olusturulmustur. Her düğümde PIC 18F4580 mikro denetleyicisi ve MCP 2551 DAA alıcı-verici entegresi bulunmaktadır. Dağıtım ve test istasyonları DAA’dan haberlesmekte ve gerekli bilgileri yine DAA vasıtasıyla veri toplama düğümüne göndermektedir. Veri toplama düğümü ise diğer düğümden aldığı bilgileri seri porttan bilgisayara göndermekte ve bilgisayardan gelen komutları DAA vasıtasıyla diğer düğümlere göndermektedir. LabVIEW grafiksel programla dili kullanılarak olusturulan kullanıcı arayüzü vasıtasıyla, EÜS istasyonlarının konum ve kontrol bilgileri online olarak gözlemlenmektedir.
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    THE DETECTION OF EGGSHELL CRACKS USING DIFFERENT CLASSIFIERS
    (2022) MEHMET YUMURTACI; Zekeriya BALCI; Semih Ergin; ISMAIL YABANOVA
    Chicken eggs, which are widely consumed in daily life due to their rich nutritional values, are also used in many products. The increasing need for eggs must be met quickly for various circumstances. Eggs are subjected to various impacts and shaken from production to packaging. In some cases, these effects cause an eggshell to crack. While these cracks are sometimes visible, they are sometimes micro-sized and cannot be seen. The cracks on the egg allow harmful micro-organisms to spoil the egg in a short time. In this study, acoustic signals generated by a mechanical effect to the eggs were recorded for 0.2 seconds at 50 kHz sampling frequency using a microphone. To determine the active part in the collected acoustic signal data, a clipping process was implemented by a thresholding process. Thus, the exactly correct moment of mechanical contact on the eggshell was easily detected. After passing the determined threshold value, statistical parameters such as min, max, difference, mean, standard deviation, skewness and kurtosis were extracted from the data obtained, and 7-dimensional feature vectors were created. Finally, the Common Vector Approach (CVA) is applied on the extracted feature vectors, 100% success rate has been achieved for the test data set. The ANN and SVM classifiers in where the same feature vectors are treated were used for the comparison purpose, and exactly the same classification rates are attained; however, the less number of eggs are tested with the ANN and SVM classifiers in the same amount of time. With the proposed mechanical system and classification methodology, it takes about 0.2008 seconds to determine whether the shells of eggs are cracked/intact. Therefore, the proposed combination of the feature vectors based on statistical features and CVA as a classifier for the detection of cracks on eggshells is notably appropriate especially for industrial applications in terms of speed and accuracy aspects.
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    Artificial Intelligence Based Determination of Cracks in Eggshell Using Sound Signals
    (2022) Zekeriya BALCI; ISMAIL YABANOVA
    Although the egg is a cheap food source, it is one of the valuable nutritional sources for people because of its rich nutritional values. It is also among the most consumed foods in daily nutrition. With the increase in egg production, it is very difficult to collect them with the human power in the egg production farms, to classify them according to their weights and to separate the defective (dirty and broken) eggs. Therefore, the mechanization has become a necessity in large capacity production farms. Cracks and fractures may occur in the egg shell as a result of exposure to external factors such as the transportation of eggs. The cracks or fractures that are formed leave the egg vulnerable to disease-causing micro-organisms. Before the egg sorting and packing, the broken and cracked eggs must be separated. This process is commonly carried out with manpower by which it is very difficult to obtain the necessary efficiency. In this study, the egg crack detection was performed by using Support Vector Machines (SVM) and Artificial Neural Network (ANN). As a result of the application of studied methods, the accuracy values of crack detection process were 0.99 for ANN and 1 for SVM. In addition, a data acquisition and processing program was developed in LABVIEW environment to detect cracks in real time.