Yazar "Kuncan, Melih" seçeneğine göre listele

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  • Küçük Resim
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    3 eksenli robot mekanizmasına monte edilmiş bir kamera vasıtasıyla farklı rotasyon ve boyutlardaki geometrik cisimlerin tanımlanarak vakum tutucu ile ayrılması
    (Otomati̇k Kontrol Ulusal Toplantısı, 2012) Bakır, Ahmet; Güney, Ömer F.; Kuncan, Melih; Ertunç, H. Metin
    Bu dokümanda üç eksenli bir taşıyıcı sisteme monte edilmiş bir kamera vasıtasıyla siyah bir zemin üzerindeki dikdörtgen, kare, üçgen, daire, altıgen gibi değişik geometrik cisimlerin görüntü işleme yöntemleri kullanılarak tanınması ve cisimlerin farklı koordinatlardaki kutularda ayırt edilmesi işlemi anlatılmaktadır. Sistemin kontrolü PLC ve servo sürücü kartlar vasıtasıyla sağlanmıştır. Robotun düşey eksenine yerleştirilen bir kameradan alınan görüntü bilgisayar ortamında işlenerek, farklı rotasyon ve boyutlardaki geometrik cisimler ve onların merkez noktaları bulunmaktadır. Sonrasında düşey eksende bulunan pnömatik silindir ve bir vakum tutucu vasıtasıyla farklı koordinatlardaki kutulara yerleştirilmektedir.
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    A Hybrid Study for Epileptic Seizure Detection Based on Deep Learning using EEG Data
    (Graz Univ Technolgoy, Inst Information Systems Computer Media-Iicm, 2024) Buldu, Abdulkadir; Kaplan, Kaplan; Kuncan, Melih
    Epilepsy, a neurological disease characterized by recurrent seizures, can be diagnosed using Electroencephalogram (EEG) signals. Traditional diagnostic methods often face limitations, leading to delays and potential misdiagnoses. In response, researchers have been developing low-cost assistive systems to enhance diagnostic accuracy and reduce life-threatening risks for epilepsy patients. In this study, a hybrid approach is proposed to diagnose epilepsy disease. To validate the success of the proposed algorithm, Hauz Khas and Bonn data sets were used. AlexNet, GoogleNet, VGG19, ResNet50, and ResNet101 classifiers were employed in this study along with the Continuous Wavelet Transform (CWT) and Short Time Fourier Transform (STFT). To increase the generalization capability, 10-fold cross-validation method was used in the classification process. Firstly, the preictal and ictal moments in the Hauz Khas dataset was classified with 99.5% success rate by CWT method and Resnet101. Similarly, 99.8% accuracy was achieved in the binary classification of the Bonn dataset using the CWT method with Resnet101. Finally, for the classification with the AB-CD-E group, 99.33% classification success rate was achieved by using the CWT method with the Resnet-101 model. These findings underscore the potential of the proposed assistive system to significantly improve the diagnosis and management of epilepsy, demonstrating high accuracy and reliability across different datasets and classification techniques.
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    A new approach for physical human activity recognition based on co-occurrence matrices
    (Springer, 2022) Kuncan, Fatma; Kaya, Yilmaz; Tekin, Ramazan; Kuncan, Melih
    In recent years, it has been observed that many researchers have been working on different areas of detection, recognition and monitoring of human activities. The automatic determination of human physical activities is often referred to as human activity recognition (HAR). One of the most important technology that detects and tracks the activity of the human body is sensor-based HAR technology. In recent days, sensor-based HAR attracts attention in the field of computers due to its wide use in daily life and is a rapidly growing field of research. Activity recognition (AR) application is carried out by evaluating the signals obtained from various sensors placed in the human body. In this study, a new approach is proposed to extract features from sensor signals using HAR. The proposed approach is inspired by the Gray Level Co-Occurrence Matrix (GLCM) method, which is widely used in image processing, but it is applied to one-dimensional signals, unlike GLCM. Two datasets were used to test the proposed approach. The datasets were created from the signals obtained from the accelerometer, gyro and magnetometer sensors. Heralick features were obtained from co-occurrence matrix created after 1D-GLCM (One (1) Dimensional-Gray Level Co-Occurrence Matrix) was applied to the signals. HAR operation has been carried out for different scenarios using these features. Success rates of 96.66 and 93.88% were obtained for two datasets, respectively. It has been observed that the new approach proposed within the scope of the study provides high success rates for HAR applications. It is thought that the proposed approach can be used in the classification of different signals.
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    A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification
    (Taylor & Francis Ltd, 2021) Kaya, Yilmaz; Kuncan, Melih; Kaplan, Kaplan; Minaz, Mehmet Recep; Ertunc, H. Metin
    Recently, precise and deterministic feature extraction is one of the current research topics for bearing fault diagnosis. For this aim, an experimental bearing test setup was created in this study. In this setup, vibration signals were obtained from the bearings on which artificial faults were generated in specific sizes. A new feature extraction method based on co-occurrence matrices for bearing vibration signals was proposed instead of the conventional feature extraction methods, as in the literature. The One (1) Dimensional-Local Binary Patterns (1D-LBP) method was first applied to bearing vibration signals, and a new signal whose values ranged between 0-255 was obtained. Then, co-occurrence matrices were obtained from these signals. The correlation, energy, homogeneity, and contrast features were extracted from these matrices. Different machine learning methods were employed with these features to carry out the classification process. Three different data sets were used to test the proposed approach. As a result of analysing the signals with the proposed model, the success rate is 87.50% for dataset1 (different speed), 96.5% for dataset2 (fault size (mm)) and 99.30% for dataset3 (fault type - inner ring, outer ring, ball) was found, respectively.
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    A Novel Approach for Activity Recognition with Down-Sampling 1D Local Binary Pattern Features
    (Univ Suceava, Fac Electrical Eng, 2019) Kuncan, Fatma; Kaya, Yilmaz; Kuncan, Melih
    The sensors on the mobile devices directly reflect the physical and demographic characteristics of the user. Sensor signals may contain information about the gender and movement of the person. Automatic recognition of physical activities often referred to as human activity recognition (HAR). In this study, a novel feature extraction approach for the HAR system using the mobile sensor signals, the Down Sampling One Dimensional Local Binary Pattern (DS-1D-LBP) method is proposed. Feature extraction from signals is one of the most critical stages of HAR because the success of the HAR system depends on the features extraction. The proposed HAR system consists of two stages. In the first stage, DS-ID-LBP conversion was applied to the sensor signals in order to extract statistical features from the newly formed signals. In the last stage, classification with Extreme Learning Machine (ELM) was performed using these features. The highest success rate was 96.87 percent in the experimental results according to the different parameters of DS-ID-LBP and ELM. As a result of this study, the novel approach demonstrated that the proposed model performed with a high success rate using mobile sensor signals for the HAR system.
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    A novel feature extraction method for bearing fault classification with one dimensional ternary patterns
    (Elsevier Science Inc, 2020) Kuncan, Melih; Kaplan, Kaplan; Minaz, Mehmet Recep; Kaya, Yilmaz; Ertunc, H. Metin
    Bearing is one of the most critical parts used in rotary machines. Bearing faults break down the mechanism where it is located. Moreover, the faults may cause to malfunction by spreading to the entire system. Thus this may result in catastrophic failure eventually. Precise and decisive feature extraction from the raw vibration signal maintains to be one of the current topics explored for fault diagnosis in bearings. In this study, vibration signals are obtained from bearings which are formed with artificial faults of specific dimensions from a bearing test setup. Instead of employing traditional feature extraction methods found in the literature, a novel feature extraction method for bearing faults called one-dimensional ternary pattern (1D-TP) is applied. The proposed approach is a statistical method that uses patterns obtained from comparisons between neighbors of each value on vibration signals. The study aims to identify the size (mm) of the fault by determining the bearing part (inner ring, outer ring, ball) from which the faults in the bearings are caused. Several classification techniques were performed by using ternary patterns with RF (Random Forest), k-NN (1<-nearest neighbor), SVM (Support Vector Machine), BayesNet, ANN (Artificial Neural Networks) models. As a result of analyzing the signals obtained from the experimental setup with the proposed model, 91.25% for dataset_1 (different speed), 100% for dataset_2 (fault type - inner ring, outer ring, ball) and 100% for dataset_3 (fault size (mm)) success rates are determined. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
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    Akıllı Ev Teknolojisi için Kablosuz Akıllı Kit
    (2019) Kuncan, Melih; Çaça, Ömer
    Akıllı ev teknolojisi, son zamanlarda özellikle enerji tasarrufunun sağlanması, geniş güvenlik önlemlerinin alınması, yaşamkonforunun arttırılması vb. birçok avantajlarından dolayı yaygın olarak kullanılmaktadır. Bu çalışmada akıllı ev otomasyonteknolojisinin mevcut yapılara daha ekonomik ve daha hızlı kurulabilmesi için, kablosuz olarak geliştirilen ve sistemin kontrolününandroid tabanlı cep telefonu ile sağlanarak, santral üzerinden kumanda edilen kablosuz akıllı kitler tasarlanmıştır. Geliştirilen kablosuzakıllı kitler, android tabanlı cep telefonu üzerinden santrale gönderilen veriye bağlı olarak santral tarafından kumanda edilmektedir.Mevcut yapıda kontrol edilmek istenen priz ve aydınlatmalara kablosuz akıllı kitler monte edilmektedir. Cep telefonundan gönderilenkomuta bağlı olarak aydınlatma sistemi ve prizler devreye alınmakta, devreden çıkarılmaktadır. Aynı zamanda geliştirilen sistem hibritbir sistem olup kablosuz sistemlerde (çamaşır makinesi, buzdolabı, aydınlatmalar, klima vb.) santrale doğrudan bağlanarak ceptelefonu üzerinden kumanda edilmektedir. Sistemde bulunan keypet ile doğru şifrenin girilmesi durumunda dış kapının açılması vecep telefonu üzerinden devreye alınan alarm sisteminin herhangi bir hareket durumunda ikaz vermesi geliştirilen akıllı evotomasyonunun bir diğer avantajı olarak dikkat çekmektedir. Bu çalışmada üretilen kablosuz akıllı kit ile mevcut bir enerji sisteminiuzaktan android tabanlı bir cihazdan kontrol etmek mümkündür. Tasarlanan ver üretilen system, santral ve kablosuz akıllı kitlerdenoluşmaktadır. Kontrol edilmek istenen priz, aydınlatma vb. diğer sistemlere akıllı kitler monte edilmektedir. Bu akıllı kitler, santraldengelen bilgiye göre devreye girmekte veya devreden çıkarılmaktadır. Sistemin kontrolü ise android tabanlı mobil cihaz üzerindensağlanmaktadır. Geliştirilen kablosuz akıllı kit hibrit özelliğe sahip olup hem kablolu sistemlerde hem de kablosuz olarak evimizdekicihazları kontrol edebilme yeteneğine sahiptir. Prototip olarak üretilen kablosuz akıllı kit mevcut bir yapı içinde bulunan aydınlatmave priz hatlarına takılarak test edilmiştir. Test sonuçlarına göre 150 $m^2$ile 200 $m^2$arasındaki bir evde kablosuz akıllı kit ile santralarasındaki veri iletimi sorunsuz olarak sağlanmaktadır. Açık alanda ise 150 m ile 200 m arasında bir çekim gücü ile veri iletimisağlandığı yapılan testler sonucunda gözlnemişitr. Benzer şekilde sistemin hibrit olma özelliği ile kablolu sistemlere de entegreedilerek test edilmiştir. Her iki durumda da test sonuçları başarılı bir şekilde gerçekleştirilmiş olup kullanılır duruma getirilmiştir.Sistemde bulunan güvenlik sistemlerinden alarm sistemi de testlerden başarıyla geçmiştir. Bu çalışmada tasarlanan ve üretilenkablosuz akıllı kitin başta akıllı ev teknolojisi alanı olmak üzere birçok farklı alanda kullanılabiliceği öngörülmektedir.
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    Akıllı toprak sulama sistemi
    (İKSAD, 2020) COGAY, Selman; Kandilli, İsmet; Kuncan, Melih
    Son yıllarda küresel ısınma başta olmak üzere temiz su kaynakları ve suyun kullanımı çok önem arz etmeye başlamıştır. Ayrıca son yüzyıldaki tarımsal faaliyetler ve su gereksinimi başta olmak üzere birçok çiftçi için suyun önemi ayrı bir önem kazanmıştır. Geçmiş yıllardaki sulamanın yerine akıllı sistemler kullanılarak optimal su kullanımı, enerji tüketimi ve ürün miktarının arttırılması için birçok akademik ve ticari çalışmaların bu anlamda önem kazandığı fikri yaygınlaşmıştır. Hatta ülkeler kendi kendine yetecek gıda politikalarını da gündeme getirmişlerdir. Özellikle 2020 yılında tüm dünyada görülen Covid 19 pandemisinden dolayı tüm dünya ülkeleri sınır kapılarını kapattıklarından dolayı hem insanlarda hem de tedarik kısmında önemli rol oynayan gıda firmalarında gıda yetecek mi endişesinin olduğu bilinen bir gerçektir. Bu çalışmada toprak sulama sistemlerinin akıllı sistemler (sensörler, yenilenebilir enerji dönüşümleri vb.) ile donatılarak tarımsal sulama için örnek bir tasarım çalışması olması amaçlanmıştır. Toprak sulama sistemleri, her iklimde ihtiyaç olan ürün verimliliğini arttıran ve su tasarrufu sağlayan sistemlerdir. Sistem ihtiyacı suyun değerli olmasından veya ürün verirmliliğinin yetersiz olmasından kaynaklanmaktadır. Ürünlerde veya su tasarrufunda görülen bu problemler, yetiştirilen bitkinin gereksinimi kadar bitkiye su verilerek çözülmektedir. Bu makalede anlatılan sistemde belirli bir alanda ölçüm yapan sensörler kullanılmış ve bu sensörlerden bilgi alınarak sulama sistemine komut gönderilmiştir. Gönderilen komut ile motor çalışmakta ve sisteme su verilmektedir. Yapılan sulama sistemi, her sensörün ölçtüğü bölgeyi sensörün ölçüm değerine göre ayrı ayrı sulayarak, aynı arazide farklı bitkilerin yetiştirilebilmesine imkan sağlamaktadır. Her arazide şehir şebekesi bulunmadığından sistemlerin beslemesi için gerekli enerjinin bulunması zor olabilmektedir. Bu nedenle yapılan sistemde, güneş panelleri ve jel akü kullanılarak şehir şebekesinin olmadığı noktalarda da toprak sulama sisteminin kullanılabilmesi sağlanmıştır.
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    An efficient approach based on a novel 1D-LBP for the detection of bearing failures with a hybrid deep learning method
    (Elsevier, 2024) Kaya, Yilmaz; Kuncan, Melih; Akcan, Eyyup; Kaplan, Kaplan
    Bearings serve as fundamental components in the transmission of motion for rotating machinery. The occurrence of mechanical wear and subsequent bearing failures within these rotating systems can lead to diminished operational efficiency and, if left unaddressed, may result in the complete cessation of the system's function. Hence, there exists a critical need for effective monitoring methodologies aimed at accurately detecting faults in such systems, preferably in their nascent stages. This study presents a novel approach to fault diagnosis leveraging vibration data obtained from bearings. Initially, a feature extraction technique is devised, which incorporates localized signal variations. Subsequently, these features, extracted via MM-1D-LBP, are utilized in conjunction with a hybrid deep learning network based on Long Short-Term Memory (LSTM) and onedimensional Convolutional Neural Network (1D-CNN) architectures for diagnostic purposes. To assess the efficacy of the proposed methodology, experiments were conducted on two distinct datasets acquired from realworld bearing assemblies. In the first dataset, the aim was to predict various failure types (Inner Ring, Outer Ring, Ball). In the second dataset, the objective was to estimate defect sizes using bearing vibration signals corresponding to defects of different dimensions (0.15 cm, 0.5 cm, 0.9 cm) under consistent operating conditions. Remarkably high success rates of 99.31 % and 99.65 % were achieved for the two datasets, respectively, thus underscoring the efficacy of the proposed MM-1D-LBP+1D-CNN-LSTM approach. These findings not only demonstrate the feasibility of the proposed method for fault diagnosis in bearing systems but also suggest its potential applicability across diverse signal categories. Ultimately, this research contributes to advancing the state-of-the-art in fault diagnosis methodologies for rotating machinery, offering enhanced accuracy and early detection capabilities.
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    An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis
    (Elsevier, 2020) Kaplan, Kaplan; Kaya, Yilmaz; Kuncan, Melih; Minaz, Mehmet Recep; Ertunc, H. Metin
    Bearings are one of the most widespread components used for energy transformation in machines. Mechanical wear and faulty bearings reduce the efficiency of rotating machines and thus increase energy consumption. The feature extraction process is an essential part of fault diagnosis in bearings. In order to diagnose the fault caused by the bearing correctly, it is necessary to determine an effective feature extraction method that best describes the fault. In this study, a new approach based on texture analysis is proposed for diagnosing bearing vibration signals. Bearing vibration signals were first converted to gray scale images. It can be understood from the images that the signals of different bearing failures form different textures. Then, using these images, LBP (Local Binary Pattern) and texture features were obtained. Using these features, different machine learning models and bearing vibration signals are classified. Three different data sets were created to test the proposed approach. For the first data set, the signals composed of very close velocities were classified. 95.9% success rate was observed for the first data set. The second data set consists of faulty signals at different parts of the bearing (inner ring, outer ring and ball) measured in the same RPM. The type of fault has been determined, and a 100% success rate was obtained for this data set. The final data set is composed of the fault size dimensions (mm) of different ratios. With the proposed approach, a 100% success rate was obtained in the classification of these signals. As a result, it was observed that the obtained feature had promising results for three different data types and was more successful than the traditional methods. (C) 2019 Elsevier B.V. All rights reserved.
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    An Integrated LSTM Neural Networks Approach to Sustainable Balanced Scorecard-Based Early Warning System
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Ayvaz, Ednan; Kaplan, Kaplan; Kuncan, Melih
    Developments in the economic environment in the 2000s have become increasingly dynamic and complex. Rapid developments in this kind of economic environment threaten and restrain the sustainability of enterprises. Enterprises need to respond quickly to these burdens and threats to survive and sustain their operations efficaciously in a competitive market in the long run. In order to reduce possible uncertainties in the future and to anticipate economic crises, early risk warning systems should be developed. However, it is seen that management accounting researches are very limited or insufficient on the demand of enterprises for coping with such crises. The aim of this study is to diminish the deficiency in the strategic cost management and prediction of economic crises. Sustainable Balanced Scorecard (SBSC), which was developed as a strategic cost management tool, is constructed in a dynamic way by integrating the early warning system developed for enterprises with an innovative approach into SBSC. Additionally, early warning system model is developed in a manner that successfully predicts economic crises with long short time memory (LSTM) networks using economic macro variables in micro field. As a result of the integration of risk early warning system with SBSC, economic crises will be predicted and necessary strategies will be developed to cope with problems of the crises. Furthermore, predicting economic crises will be turned into opportunities or cause enterprises to make measures with minimum losses. In this model, crisis periods are successfully predicted two crises of 2002 and 2008 with 95.41& x0025; accuracy with macroeconomic data between 1998 and 2011.
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    An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Kuncan, Melih
    Bearings are vital automation machine elements that are used quite frequently for power transmission and shaft bearing in rotating machines. The healthy operation of the bearings directly affects the performance of the rotating machines. Bearing faults may cause more vibration than normal in rotating machines, which wastes power. However, further bearing failures can cause vital damage to rotating machines. In this study, bearing vibration values are obtained through a special test setup. Different types and different sizes of artificial faults have been created in the bearings for the testing process. Data on these bearings are collected at different speeds. The purpose of the study is to diagnose faults in the bearings. In this context, a new approach is proposed. First, the one-dimensional local binary pattern (1D-LBP) method is applied to vibration signals, and all signal data are carried to the 1D-LBP plane. Statistical features are obtained from the signals in the 1D-LBP plane by using these features, and then the vibrational signals are classified by the gray relational analysis (GRA) model. Four different data sets are organized to test the proposed approach. The results of the test process with this proposed model have an accuracy of 99.044% for Dataset1 (different speed -300 rpm intervals), 94.224% for Dataset2 (different speed -60 rpm intervals), and 99.584% for Dataset3 (fault size (mm)); a 100% average success rate is observed for Dataset4 (fault type - error free bearing (EFB), inner ring fault (IRF), outer ring fault (ORF), and ball fault (BF)).
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    Analysis of efficiency and torque effect of 1,1 kw induction motor gear by 2-d finite elements method, 1,1 kw'lık indüksiyon motorun oluk sayısının verime ve torka etkisinin sonlu elemanlar yöntemiyle analizi
    (IETS'18 International Engineering and Technology Symposium, 03-05 May, 2018, 3-5 Mayıs 2018) Yavuz, İzzet; Minaz, Mehmet Recep; Kuncan, Melih
    In this study, the effect of varying the design parameters of the asynchronous motor was investigated. An asynchronous motor design parameter of 1.1 kW was taken as reference. This design has been analyzed with computer aided 2-D finite element method (SEY) and the results are compared. The number of stator gutters, number of rotor gutters and other motor parameters have been investigated in terms of reduction and increase in torque and torque. An asynchronous motor with design parameters has been shown to reduce the slot length and reduce the number of stator gutters and rotor gutters, resulting in an increase in efficiency of about 2%. Optimum efficiency and torque are obtained when the rotor slot length of the asynchronous motor is 2mm, the number of rotor gutter is 30 and the number of stator gutter is 24.
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    Application of artificial neural network to evaluation of dimensional accuracy of 3D-printed polylactic acid parts
    (Wiley, 2024) Gunes, Seyhmus; Ulkir, Osman; Kuncan, Melih
    Additive manufacturing (AM) has begun to replace traditional fabrication because of its advantages, such as easy manufacturing of parts with complex geometry, and mass production. The most important limitation of AM is that dimensional accuracy cannot be achieved in all parts. Dimensional accuracy is essential for high reliability, high performance, and useful final products. This study investigates the impact of printing parameters on the dimensional accuracy of samples fabricated through fused deposition modeling (FDM), an additive manufacturing (AM) method utilizing polylactic acid (PLA) material. The experimental design process was performed using Taguchi methodology. ANOVA was used to determine the most important parameter affecting accuracy. Based on experimental studies, the optimal printing parameters for parts are determined as follows: concentric infill pattern, 3 mm wall thickness, 70% infill density, and a layer thickness of 200 mu m. Artificial neural network (ANN) was used in the evaluation and prediction of the results. The R-square (R2) performance evaluation criterion was above 95% from the ANN results. This value shows that the results are significant. The data acquired from this study may assist in identifying optimal parameters that contribute to the fabrication of samples with high dimensional accuracy using the FDM method. image
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    Araç süspansiyon sistemi kontrolüne PID ve bulanık mantık yaklaşımları
    (Otomati̇k Kontrol Ulusal Toplantısı, 2015) Doğan, Hasan; Kaplan, Kaplan; Kuncan, Melih; Ertunç, H.Metin
    Aktif süspansiyon sistemleri yolcuların sürüş konforunu ve araç yol tutuşunu iyileştirebilmektedir ve bu hususlar sürüş sırasında güvenlik açısından oldukça önemlidir. Aktif süspansiyon sistemleri böylesi avantajları ile süspansisyon sistemlerinin kontrolü için önemli bir yaklaşımdır. Bu çalışmada MATLAB/Simulink yazılımı kullanılarak bir çeyrek araç modeli tasarlanmıştır. Tasarlanan bu modelin kontrolü, sırasıyla, geleneksel bir kontrol yöntemi olan PID kontrol yöntemi ve modern kontrol yöntemlerinden birisi olan Bulanık Mantık kontrol yöntemi ile gerçeklenmiştir. PID kontrol ve bulanık mantık kontrol yöntemlerinde, kontrol parametreleri kontrol performansının iyileştirilmesi için oldukça önemlidir. Parametrelerin optimal olarak belirlenmesi süspansiyon sisteminden daha iyi sonuçlar elde edilmesine imkan verir. Bu çalışmalar ışığında PID kontrol yönteminin kullanılan çeyrek araç modelindeki etkinliği bulanık mantık kontrol yöntemine göre daha iyi olduğu saptanmıştır. Active suspension systems can improve the drive comfort of passengers and road grip capacity of the vehicle and it is also important for the safety of drive. For such advantages of the active suspension systems, it plays major role to control of suspension system. In this study, a quarter car model was designed in MATLAB/Simulink and then this model was controlled respectively by using the methods PID controller and Fuzzy Logic controller for comparing this methods. Comparing both methods, PID control method outperforms fuzzy logic one.
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    Bilyeli rulmanlarda zaman uzayında istatistiksel öznitelik çıkarımı ve yapay sinir ağları metodu ile hata boyutunun kestirimi
    (Otomati̇k Kontrol Ulusal Toplantısı, 2013) Bayram, Samet; Kaplan, Kaplan; Kuncan, Melih; Ertunç, H. Metin
    Dönel makinelerde yataklama elemanı olarak kullanılan rulmanlarda meydana gelen arızalar, sistemin çalışmasını aksatan veya durduran nedenlerdendir. Bu çalışmada, bir milrulman sisteminde, belirli boyutlarda yapay hatalar oluşturulmuş rulmanlardan titreşim sinyalleri elde edilmiştir. Çalışmanın amacı, rulmanlarda meydana gelen arızaların boyutunu, yapay sinir ağları modelini kullanarak teşhis etmektir. Elde edilen titreşim verilerinin gerçek zamanda özellikleri çıkarılarak belirli ağırlıklarla çarpılmış, oluşturulan yapay sinir ağı modeline giriş olarak verilmiştir. Farklı arıza boyutlarına sahip rulmanların gerçek zamanda istatistiki özellikleri de farklı olmaktadır. Bu özellikler kullanılarak geliştirilen yapay sinir ağı ile rulmanlarda meydana gelen arızaların büyüklüğü, %100 bir başarı ile sınıflandırılırken, gerçek hata değerinin ise, ortalama %2 hata ile kestirildiği gözlemlenmiştir.
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    Brain tumor classification using modified local binary patterns (LBP) feature extraction methods
    (Churchill Livingstone, 2020) Kaplan, Kaplan; Kaya, Yilmaz; Kuncan, Melih; Ertunc, H. Metin
    Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient's survival rate. Today, MR images are used to determine the type of brain tumor. Manual diagnosis of brain tumor type depends on the experience and sensitivity of radiologists. Therefore, researchers have developed many brain tumor classification models to minimize the human factor. In this study, two different feature extraction (nLBP and alpha LBP) approaches were used to classify the most common brain tumor types; Glioma, Meningioma, and Pituitary brain tumors. nLBP is formed based on the relationship for each pixel around the neighbors. The nLBP method has a d parameter that specifies the distance between consecutive neighbors for comparison. Different patterns are obtained for different d parameter values. The alpha LBP operator calculates the value of each pixel based on an angle value. The angle values used for calculation are 0, 45, 90 and 135. To test the proposed methods, it was applied to images obtained from the brain tumor database collected from Nanfang Hospital, Guangzhou, China, and Tianjin Medical University General Hospital between the years of 2005 and 2010. The classification process was performed by using K-Nearest Neighbor (Knn) and Artificial Neural Networks (ANN), Random Forest (RF), A1DE, Linear Discriminant Analysis (LDA) classification methods, with the feature matrices obtained with nLBP, alpha LBP and classical LBP from the images in the data set. The highest success rate in brain tumor classification was 95.56% with the nLBPd = 1 feature extraction method and Knn model.
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    Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters
    (Springer, 2020) Kaya, Yilmaz; Kuncan, Melih; Kaplan, Kaplan; Minaz, Mehmet Recep; Ertunc, H. Metin
    Bearings are the most commonly used machine element in order to reduce rotational friction in machines and to compensate radial and axial loads. It is very important to determine the faults in the bearings in terms of the machine health. In order to accurately diagnose bearing-related faults with traditional machine learning methods, it is necessary to identify the features that characterize bearing fault most accurately. Therefore, a new feature extraction procedure has been proposed to determine the vibration signal velocities of different fault sizes and types in this study. The new approach has been employed to obtain features from the vibration signals for different scenarios. After different filtering based on 1D-LBP method, the F-1D-LBP method was used to construct feature vectors. The filters reduce the noise in the signals and provide different feature groups. In other words, it is aimed to generate filters in order to extract different patterns that can separate signals. For each filter applied, different patterns can be obtained for the same local point on signals. Thus, the signals can be represented by different feature vectors. Then, by using these feature groups with various machine learning methods, vibration velocities were separated from each other. As a result, it was observed that the obtained feature had promising results for classification of bearing vibrations.
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    Classification of CNC Vibration Speeds by Heralick Features
    (Graz Univ Technolgoy, Inst Information Systems Computer Media-Iicm, 2024) Kuncan, Melih; Kaplan, Kaplan; Kaya, Yilmaz; Minaz, Mehmet Recep; Ertunc, H. Metin
    In the contemporary landscape of industrial manufacturing, the concept of computer numerical control (CNC) has emerged due to the optimization of conventional machinery, distinguished by its remarkable precision and expeditious processing capabilities. These inherent advantages have seamlessly paved the way for the pervasive integration of CNC machines across a myriad of industrial manufacturing sectors. The present study embarks upon a comprehensive inquiry, delving into the intricate analysis of a specialized prototype CNC molding machine, encompassing a meticulous assessment of its structural rigidity, robustness, and propensity for vibrational occurrences. Moreover, an insightful exploration is undertaken to discern the intricate interplay between vibrational signals and intricate machining processes, particularly under diverse conditions such as the presence or absence of the cutting tool, and at varying rotational speeds denoted in revolutions per minute (RPM). The trajectory of this research voyage encompasses an extensive array of empirical experiments meticulously conducted on the prototype CNC machine, with synchronous real-time acquisition of vibrational data. This empirical journey starts by generating two distinct datasets, each meticulously designed to encompass an assemblage of seven distinct rotational speeds, spanning the spectrum from 18000 to 30000 RPM, thereby facilitating enhanced diversity within the dataset. In parallel, a secondary dataset is meticulously derived from the CNC machine operating in the absence of the cutting tool, thereby encapsulating an exhaustive range of 20 discrete RPM values. The extraction of pivotal features aimed at discerning between the vibrational signals arising from distinct conditions (i.e., those emanating from situations involving the presence or absence of the cutting tool) and the associated variance in CNC machine speeds is facilitated through an innovative framework grounded in co -occurrence matrices. The culmination of this methodological framework is the identification of discernible co -occurrence matrices, thereby facilitating the subsequent computation of Heralick features. The classification effort was performed systematically using 10 -fold cross -validation analysis, covering a number of different machine learning models. The outcomes emanating from this intricate sequence of systematic methodologies underscore remarkable achievements. Specifically, the classification of vibrational signals corresponding to varying CNC machine speeds, contingent upon the presence or absence of the cutting tool, yields commendable accuracy rates of 94.27% and 94.16%, respectively. Notably, an exemplary accuracy rate of 100% is attained when classifying differing conditions (i.e., situations involving the presence or absence of the cutting tool) across specific RPM settings, prominently at 22000 24000 26000 28000 and 30000 RPM.
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    Color Based Object Separation in Conveyor Belt Using PLC
    (2020) Şengül, Öznur; Öztürk, Sıtkı; Kuncan, Melih
    In today's production systems, industrial automation systems are preferred in order to provide high-efficiency and high-qualityproduction, and therefore it is continuously developing. PLC is the basis of programmable industrial automation systems. In thestudy, in the PLC controlled conveyor belt system, the process of separating the objects according to their color by image processingwas realized in real-time. In the image processing application, recognition is recognized by separating objects according to theircolors. The process of sending objects separated by classes to different containers in the system is also done in real-time. Theconveyor belt system used in the study was controlled by S7-1200 PLC. Image processing was performed in Matlab. Communicationbetween Matlab and PLC has been used with the OPC interface application, which is widely used in the industry.This study, experimental prototype of an automation system that is widely used in industrial applications, has been successfully made.Matlab program was used in the image processing part of the conveyor belt object separation system, PLC program was integratedinto communication and control part, and it was seen in experimental studies that the system was working efficiently in real-time. Ithas been observed that the image processing algorithm for this study has been successfully performed to sort objects according totheir colors, PLC-OPC system communication, and separation of objects according to specified positions. As a result of the studies onthe experimental prototype, it is foreseen that this system can be applied to a commercial or industrial system.