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Öğe 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, MelihEpilepsy, 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.Öğe 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. MetinRecently, 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.Öğe 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. MetinBearing 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.Öğe 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, KaplanBearings 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.Öğe 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. MetinBearings 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.Öğe 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, MelihDevelopments 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.Öğe Araç süspansiyon sistemi kontrolüne PID ve bulanık mantık yaklaşımları(Otomati̇k Kontrol Ulusal Toplantısı, 2015) Doğan, Hasan; Kaplan, Kaplan; Kuncan, Melih; Ertunç, H.MetinAktif 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.Öğe 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. MetinDönel 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.Öğe Brain tumor classification using modified local binary patterns (LBP) feature extraction methods(Churchill Livingstone, 2020) Kaplan, Kaplan; Kaya, Yilmaz; Kuncan, Melih; Ertunc, H. MetinAutomatic 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.Öğe Classification of Bearing Fault Size by Using Support Vector Machines(International Conference on AdvancesandInnovations in Engineering (ICAIE), 2017-05-12) Kaplan, Kaplan; Melih, Kuncan; H.Metin, ErtunçBearings are generally used as rolling elements in rotation machines. Faults in the rolling elements causes breakdown, and this may lead downtime and huge damages in rotating machines. On the other hand, bearings are often employed under high load and high running speed conditions. In this study, artificial faults are created on bearing inner rings by a laser beam in certain size namely 0.15 cm, 0.5 cm, 0.9 cm diameter. Vibration signals are collected by a data acquisition device in a shaft-bearing test setup. Before classifying the data, feature extraction is performed to characterize the signal. Statistical features are calculated and they are used as input to classification method. SVM classification model is employed to diagnose the size of the faults. The SVM model developed in this study classify the size of bearings faults with no prediction error. In addition, 0.1 mm error band is determined to eliminate minor bugs.Öğe 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. MetinBearings 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.Öğe 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. MetinIn 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.Öğe Design, production and novel NC tool path generation of CNC tire mold processing machine(Gazi Univ, Fac Engineering Architecture, 2018) Kuncan, Melih; Kaplan, Kaplan; Ertunc, H. Metin; Kucukates, SelimIn this study, a mechanical design, mathematical modeling and software algorithm have been realized for CNC prototype machine which is mainly used in tire sector and in the others (shoe sole plate and medical prosthetic manufacturing, aviation, automotive, jewelry sector etc.). For this aim, a mechanical design and manufacturing of CNC tire mold machine was firstly performed. The most feasible design model has been determined as a result of the research and analysis carried out for the mechanical design stage. Thus, it is aimed to provide the designed machine to be used in other areas besides tire mold processing sector. The main contribution of the study is the development of an original and mathematical transformation algorithm that transfer the texts and patterns to 3D complex surfaces with CNC machines. Moreover, an interface for the users is developed based on C # compiler using the outputs from the software algorithm. The NC codes of the algorithm output were tested on the designed prototype machine. Then, the prototype machine was tested with different materials (wood, iron, steel etc.) and the test results were observed. Based on the test results, pattern and character processing with the desired precision is carried out successfully.Öğe Diagnosing bearing fault location, size, and rotational speed with entropy variables using extreme learning machine(Springer Heidelberg, 2024) Akcan, Eyyuep; Kuncan, Melih; Kaplan, Kaplan; Kaya, YilmazBearings play a crucial role in transmitting motion in rotating machines and are considered fundamental equipment. Any errors occurring in these machines can lead to a reduction in mobility and complete machine failure if not addressed promptly. Condition monitoring of bearings through the utilization of vibration information is a widely researched and advanced field. Analyzing irregularities in vibration data using entropy methods enables the extraction of valuable information that characterizes the health status of bearings. In accordance with this purpose, vibration signals were collected from artificially defective bearings in special dimensions, using a dedicated experimental test setup. Three different scenarios were considered for evaluating the proposed model performance. Data set 1 encompassed bearing signals collected at various speeds (1500, 1740, 1800, 1860, and 2100 RPM). Data set 2 consisted of vibration signals using different fault location (ball, inner, and outer ring faults), while data set 3 comprised bearing vibration signals with faults of varying sizes (0.15 cm, 0.5 cm, 0.9 cm) under the same speed. For feature extraction from bearing vibration signals, 18 distinct entropy methods were employed in all experiments. The extracted entropy features were utilized as inputs for the extreme learning machine (ELM) model. ELM offers a fast and efficient approach for training neural networks, making it a valuable tool in various machine learning applications. The experiment conducted using all features achieved an accuracy rate ranging from 98.48% to 100%. To assess the individual effectiveness of entropy features, separate trials were conducted for each feature. Fuzzy entropy demonstrated the highest success rates in data sets 1 and 2, while the slope entropy feature exhibited superior performance in data set 3. The proposed approach has been compared with relevant studies in the literature, and its significant results have been duly acknowledged. This comparison further affirms the efficacy of the proposed approach and highlights its potential contribution to the field.Öğe E,Experimental ınvestigation of cutting speed on the surface roughness for cnc machine(IETS'18 International Engineering and Technology Symposium, 03-05 May, 2018, 3-5 Mayıs 2018) Kuncan, Melih; Kaplan, Kaplan; Ertunç, H.Metin; Küçükateş, SelimCNC (Computer Numerical Control) machines have high precision and fast processing capabilities and are the result of the optimization of conventional machines. Because of their superiority, they now have a widespread use in many different industrial manufacturing sectors. The most common usage areas of CNC machines are the machining sector. The method of processing during machining depends on the type of cutter and the material being processed. During machining, surface roughness is formed on the machined surfaces due to the physical, chemical and thermal factors, mechanical movements between cutting and cutting. In other words, the surface roughness is irregular deviations below and above the nominal surface line. Minimizing surface roughness during machining is an important issue for the industrial sector. The quality of the processed surfaces plays an important role on the machining performance. A quality machined surface improves fatigue strength, corrosion resistance and friction life significantly. Surface roughness also affects the various functional properties of parts such as contact, abrasion, heat conduction, oil flame retention and dispersibility, coating or resistance life, which cause surface friction. For this reason, the desired surface finish is generally determined and appropriate procedures are selected to achieve the required quality ....Öğe The effect of bearings faults to coefficients obtained by using wavelet transform(IEEE 22nd Signal Processing and Communications Applications SIU, 2014) Bayram, Samet; Kaplan, Kaplan; Kuncan, Melih; Ertunç, H. MetinIn this study, artificial defects in various diameters are formed on inner race, outer race and ball bearing which are essential components of a bearing and vibration signals are collected by a data acquisition card from bearing-shaft setup. The signals acquired are decomposed from noise with wavelet transform; thus vibration signal resulting from normal operation of the system is obtained. The energy of noisy and noise-free signal is calculated and the wavelet coefficients that will be used in classifying are obtained. As a conclusion of experimental studies, the technique based on wavelet transform coefficients accomplishes the classifications of different bearings fault types successfully.Öğe Farklı Rulman Hatalarından Elde Edilen Titreşim Sinyalleri Üzerindeki Radyal Yüklerin Etkisi(Otomati̇k Kontrol Ulusal Toplantısı, 2012-10-11) Kuncan, Melih; Kaplan, Kaplan; Samet, Bayram; H. Metin, ErtunçBu çalışma rulmanlarda hata analizi ve artan radyal yüklemenin hata üzerindeki etkisini ele almaktadır. Rulman test düzeneğinde incelenmek için, rulman üzerinde farklı bölgelerde ve farklı büyüklüklerde hatalar oluşturulmuş, titreşim sinyalleri toplanmıştır. Frekans uzayında çeşitli yöntemler kullanılarak sinyal Matlab ortamında analiz edilmiştir. Hatalı rulman üzerine sabit eksenel yük ve belirli miktarlarda radyal yük verilerek hata frekans bileşenin değişimi incelenmiştir. Rulmanda oluşturulmuş hatanın boyutu ve verilen radyal yük miktarının arttırılmasıylabirlikte hata frekans genliğinin yükselişe geçtiği görülmüştür.Öğe Feature extraction of ball bearings in time-space and estimation of fault size with method of ANN(Proceedings of the 16th Mechatronika, 2014) Kaplan, Kaplan; Bayram, Samet; Kuncan, Melih; Ertunç, H. MetinFaults in bearings used in machines cause downtime and leads to catastrophic results on the machining operations. In this study, specific sizes of the artificial bearings defects are created and vibration signals were obtained from a shaft-bearing system. The purpose of this study is to diagnose the size of the defects occurring in bearings by using Artificial Neural Networks(ANN) model. Features of vibration data are extracted in real time and are multiplied with specific weights; then they were given as input to the ANN model. Statistical properties of bearings faults are observed that their values vary depending on fault dimensions in real-time. These features are examined by using ANN and the size of the defects occurring in bearings are classified with 100% success, on the other hand the prediction permonfance of actual error for a ANN model is found 2%.Öğe Görüntü işleme ile 3 eksenli robot mekanizması üzerinde nesne ayırt edilmesi ve sıralanması(Otomati̇k Kontrol Ulusal Toplantısı, 2015) Çubukçu, Aykut; Kuncan, Melih; İmren, Mehmet; Erol, Fatih; Ertunç, H.Metin; Öztürk, Sıtkı; Kaplan, KaplanBu çalışmada, üç eksenli taşıyıcı sisteme monte edilmiş bir kamera vasıtasıyla ara yüz üzerinden girilen metnin siyah zemin üzerinde harf/sayılar ile oluşturulması üzerinde çalışılmıştır. Kelimedeki harfler analiz edildikten sonra veri tabanındaki veriler ile karşılaştırılması yapılmakta ve harf/sayıların konumu belirlenmektedir. Konum belirleme işlemi MATLAB ortamında görüntü işleme metotlarıyla yapılmaktadır. Konumu belirlenen harf/sayılar vakum tutucu ile tutulur ve kelime tekrardan oluşturulmaktadır. Harf/sayıların belirlenen konumlara taşıma işlemi S7-1200 PLC ve servo sürücü kartlar kontrolünde servo motorlar ile yapılmaktadır. In this study, the creation of the text entered via the interface with letters/numbers on a black background have been studied with a camera mounted on three-axis carrier system. After analyzing the letters of the word and is compared with data in the database and the position of the letter/number is determined. The location determination is done with MATLAB image processing methods. Letters/numbers whose position is determined is held with vacuum holder and words is formed again. Carrying operation to determined position of letters/numbers.Öğe Human Face Recognition Using Deep Neural Networks(2022) Kaplan, Kaplan; Kuncan, FatmaIn recent years, many researchers have been using computer-based systems containing artificial intelligence applications for different applications. Human recognition application is one of the studies carried out in this field. Face and object recognition applications, which were originally designed for security measures, are also used in the entertainment and shopping sectors recently. These applications are gaining even more popularity with the mobile application development of various companies. In face recognition applications, deep learning methods can be preferred if the data is large and complex. In this study, a 3-layer Convolutional Neural Network (CNN) has been developed for a face recognition application. The developed model was applied to the Libor Spacek's Facial Images Databases dataset. As a result of the application of the proposed method on the data set, it was determined that the accuracy rate was 99.29%. This means that the application can be adapted for real recognition systems.
