Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1A smartphone-based vibration dataset for induction motor fault diagnosis under different speed and load conditions
Ertarğın, Merve; Orhan, Ahmet; Yıldırım, Özal; Chauhan, Sumika; Vashishtha, Govind
Induction motors are foundational components across industrial applications, valued for their inherent robustness, operational simplicity, and cost-efficiency. Maintaining their reliable function is paramount for a wide range of mechanical and electrical systems. However, they are prone to various mechanical and electrical faults, such as bearing defects, rotor issues, and voltage imbalances, which can significantly impair their performance and reliability. This study presents a novel vibration dataset for induction motor fault diagnosis, uniquely acquired using a smartphone-based inertial sensor rather than conventional industrial accelerometers. Vibration signals were recorded along three orthogonal axes (gx, gy, gz), alongside gravity-compensated acceleration components (guserx, gusery, guserz), enabling detailed analysis of both raw and gravity-free vibration characteristics. Data were collected under diverse conditions, including healthy operation and several fault types, across varying rotational speeds and load states. The dataset features long-duration vibration recordings sampled at 100 Hz, suitable for both time-domain analysis and window-based feature extraction. Its inclusion of multiple operating speeds and load conditions is ideal for studying the impact of operational variability on fault signatures. By leveraging low-cost and readily accessible smartphone sensors, this dataset supports practical and accessible vibration data acquisition for supporting the development, benchmarking, and validation of data-driven fault diagnosis methods. This resource is expected to significantly advance research in condition monitoring of induction motor, particularly for machine learning and signal processing applications using vibration data.
- Derleme MakaleAçık Erişim2025Deep learning: evolution, innovations, and applications in the last decade
Yıldırım, Özal; Günay, Mıhrıban; Demir, Yakup
Deep learning has become a popular method in the last ten years with its superior performance in many fields, especially in health. Although it is a sub-branch of machine learning, one of the most important reasons for researchers to use this method is that it automates the difficult processes of feature extraction stages in traditional machine learning methods. With the advancement of technology every year, it has become easier to create large data sets or to access large data sets that have been used before on the web. Researchers who want to work on large data sets use deep learning methods effectively because of their advantages instead of using traditional machine learning methods. The foundations of deep learning were laid with the deep belief networks method, which was first developed in 2006. Later, with the significant success of the Convolutional Neural Network (CNN) method developed in 2012 in image classification, deep learning methods have been used in many applications in other disciplines. The success of the deep reinforcement learning algorithm that defeated Alpha Go champion Lee Sedol in 2016, the remarkable success of the contentious generating networks in creating their own unique images, the success of the Siamese networks with the ability to learn from little data in signature verification and facial recognition systems, the success of the artificial intelligence chat bot ChatGPT, which was launched in the last months of 2022, attracted attention in a short time, and the ability of DALL-E, a similar language model, to create images from texts, shows that deep learning is in constant innovation and development. This study aims to give an idea to researchers who will work in this field in the future by talking about the basic concepts of deep learning and the innovative and popular approaches used.
- MakaleAçık Erişim2023Motor Yataklarında Meydana Gelen Arızaları Tespit Etmek için Yeni Bir Tek Boyutlu Konvolüsyonel Sinir Ağı Modeli
Yıldırım, Özal; Orhan, Ahmet; Ertarğın, Merve
Elektrik motorları, çeşitli işlemleri otomatikleştirme ve kolaylaştırma yeteneklerinden dolayı endüstride önemli bir yere sahiptir. Elektrik motorlarında meydana gelen arızalar, cihazın veya sistemin çalışmasını etkileyebilmekte ve büyük maddi kayıplara neden olabilmektedir. Bu nedenle arızaların erken aşamada tespit edilmesi kritik bir öneme sahiptir. Arızaların tespitinde bilgisayar destekli yazılımlar kullanılması maliyetten ve zamandan tasarruf etme potansiyeli nedeniyle ön plana çıkmaktadır. Bu çalışmada, motor yatağı arıza türlerini tespit etmek için derin öğrenme tabanlı bir model önerilmiştir. Tek boyutlu konvolüsyonel sinir ağı (1D-CNN) mimarisi kullanan bu model ile sadece titreşim verileri kullanılarak arıza tipi tespiti sağlanmaktadır. Önerilen mimari, titreşim sinyallerini motor arıza teşhisinde hızlı ve güvenilir olarak kullanan etkin bir modeldir. Çalışma kapsamında farklı hız senaryoları kullanılarak eğitim ve test aşamalarının detaylı performans değerlendirmeleri sağlanmıştır. Genelleme kabiliyeti yüksek olan bu model ile, farklı senaryolarda yüksek doğruluk oranları ile arıza tespiti yapılmıştır.
AtıfOpenAlex3TR Dizin2
İncele - MakaleYalnız künye2020Derin sinir ağları kullanarak türkiye’deki otomobil satışlarının tahmini için bir model
Kaya, Sema Kayapinar; Yıldırım, Özal
Tüm kararlarda itici güç olan tedarik zinciri sürecinde talep tahmini, tedarik zinciri sürecinin en önemli bileşenlerinden birisidir. Gelecekteki ürün ve hizmetlerin tahmin edilmesi, diğer tüm tahminlerin başlangıç noktası olup diğer tüm süreçlerin temel girdisini oluşturmaktadır. Bu çalışmada, otomobil satış tahmini için 8 katmanlı Derin Sinir Ağ (DSA) modeli önerilmiştir. Modelin girdileri, döviz kuru, gayrisafi yurt içi hasıla, tüketici güven endeksi ve tüketici fiyat endeksi gibi çeşitli ekonomik göstergelerden oluşmaktadır. Modelin çıkışına göre araç satış tahmini yapılmıştır. 2011 ve 2018 yılları arasında aylık bazda toplam 90 veri toplanılarak analizler yapılmıştır. Elde edilen sonuçlar, bu yaklaşımın çeşitli satış tahmin problemlerinde kullanılabileceğini göstermektedir.
AtıfTR Dizin5İncele - MakaleYalnız künye2020Scopus SJRQ1A deep convolutional neural network model for automated identification of abnormal EEG signals
Yıldırım, Özal; Baloglu, Ulas Baran; Acharya, U. Rajendra
Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual examination of these signals by experts is strenuous and time consuming. Hence, machine learning techniques can be used to improve the accuracy of detection. Nowadays, deep learning methodologies have been used in medical field to diagnose the health conditions precisely and aid the clinicians. In this study, a new deep one-dimensional convolutional neural network (1D CNN) model is proposed for the automatic recognition of normal and abnormal EEG signals. The proposed model is a complete end-to-end structure which classifies the EEG signals without requiring any feature extraction. In this study, we have used the EEG signals from temporal to occipital (T5–O1) single channel obtained from Temple University Hospital EEG Abnormal Corpus (v2.0.0) EEG dataset to develop the 1D CNN model. Our developed model has yielded the classification error rate of 20.66% in classifying the normal and abnormal EEG signals.
AtıfScopus164OpenAlex211
İncele - MakaleYalnız künye2019Scopus SJRQ1Deep long short-term memory networks-based automatic recognition of six different digital modulation types under varying noise conditions
Daldal, Nihat; Yıldırım, Özal; Polat, Kemal
In this paper, a new method based on deep learning has been proposed in order to recognize noise-digital modulation signals at varying noise levels automatically. The 8-bit data from six different modulations have been obtained by adding noise levels from 5 to 25 dB. The used digital modulation types are Amplitude Shift Keying, Frequency Shift Keying, Phase Shift Keying, Quadrature Amplitude Shift Keying, Quadrature Frequency Shift Keying, and Quadrature Phase Shift Keying. To recognize the noise-digital modulation signals automatically, a new deep long short-term memory networks (LSTMs) model has been proposed and then applied to these signals successfully. A significant advantage of the proposed system is that deep learning method has been trained and tested with raw digital modulation signals without applying any feature extraction from the signals. In this study, the noise modulation signals of 5–25 dB have been classified and compared with each other. The innovative aspect of the study is to classify the modulation with the LSTM method without dealing with the extraction of signal characteristics. Without noise, added digital modulation signals had been classified as the success rate of 97.22%, while with all noise-added signals have been classified as the success rate of 94.72% with deep LSTM model. The experimental results show that the proposed deep LSTM model has been achieved remarkable results in recognition of noised six different modulation signals with a fully end-to-end structure.
AtıfScopus35WoS26OpenAlex35İncele - MakaleYalnız künye2019Scopus SJRQ3Application of deep transfer learning for automated brain abnormality classification using MR images
Talo, Muhammed; Baloglu, Ulas Baran; Yıldırım, Özal; Rajendra Acharya, U.
Magnetic resonance imaging (MRI) is the most common imaging technique used to detect abnormal brain tumors. Traditionally, MRI images are analyzed manually by radiologists to detect the abnormal conditions in the brain. Manual interpretation of huge volume of images is time consuming and difficult. Hence, computer-based detection helps in accurate and fast diagnosis. In this study, we proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images. Convolutional neural network (CNN) based ResNet34 model is used as a deep learning model. We have used current deep learning techniques such as data augmentation, optimal learning rate finder and fine-tuning to train the model. The proposed model achieved 5-fold classification accuracy of 100% on 613 MR images. Our developed system is ready to test on huge database and can assist the radiologists in their daily screening of MR images.
AtıfScopus473OpenAlex508İncele - Derleme MakaleYalnız künye2018Yeni Bir Çevrimiçi Elektrik Enerji Kalitesi İzleme Cihazı
Erişti, Hüseyin; Yıldırım, Özal; Demir, Yakup
Bu çalışmada, elektrik enerji kalitesi parametrelerini çevrimiçi izleyen yeni bir enerji kalite izleme cihazıgeliştirilmiştir. İzleme cihazı, bağlantı noktalarından üç faz akım ve gerilim bilgilerini gerçek zamanlı eldeederek güç kalitesi parametrelerini hesaplamaktadır. Ayrıca sahip olduğu haberleşme yapısı ile elde ettiği ölçümbilgilerini uzak bağlantı noktalarına anlık olarak aktarmaktadır. Cihaz yapısında, sinyal işleme alanında gittikçeyaygınlaşan Alanda Programlanabilir Kapı Dizileri (APKD) donanımı kullanılmıştır. Gömülü olarak hazırlananenerji izleme cihazı, kendi enerji akışlarını denetim altında tutmak isteyen kullanıcıların kolaylıklakullanabileceği etkin bir platform sunmaktadır.
- MakaleYalnız künye2018Classification of Human Actions in Outdoor Images Using Hybrid Deep Learning Methods
Tümen, Vedat; Algur, Özlem; Yıldırım, Özal
In this paper, a hybrid deep learning approach is proposed for automatic classification of human actions in outdoor images. Firstly, the detection of the humans in the image taken from the outdoor environment is provided. For this purpose, YOLO, a pre-trained deep object detection tool widely used in the literature, is used. The Google street view platform was preferred to acquire outdoor images. Action classes were created for the detected humans. These action classes; walking to the right, walking to the left, standing and sitting. Thus, a comprehensive dataset has been created for humans identified from the outdoor images. A convolutional neural network (CNN) model is designed for the automatic recognition of classes. With this designed CNN model, automatic recognition of the human actions is provided. This trained model is used in a hybrid manner with the YOLO object detection system to automatically recognize the human actions in the input images.Within the scope of the article, a four-class dataset was created and performance evaluations of the proposed system were made.
AtıfTR Dizin1İncele - MakaleYalnız künye2018Scopus SJRQ3Investigation of dose and development time for thin e-beam resist poly(methyl methacrylate) for large area dense nanopattern applications
Göktaş, Oktay; Çakıroğlu, Dilek; Yıldırım, Özal; Göktaş, Nebile Işık
With the advance of nanotechnology, the demand for very dense nanopatterns over large areas continuously increases. Electron beam lithography is one of the main tools to realise this type of nanopattern. In this study we investigate the effect and interrelation of dose and development time for electron beam resist poly(methyl methacrylate) (PMMA) for the realisation of very large area and very dense nanopatterns. For this purpose a pattern consisting of a lattice of holes with radius of 80 nm and lattice constant of 200 nm has been designed, electron beam lithography is realised on a thin resist and a detailed SEM image inspection of the pattern is recorded. Hough transform, a pattern recognition method for the digital images, is applied to SEM images to measure the radii of the holes on the images. Interrelation between dose and development time is investigated. It is found that for a very large area of dense nanopatterns the lithographic process works better for development times shorter than typically used ones.
- MakaleYalnız künye2018Scopus SJRQ2Arrhythmia detection using deep convolutional neural network with long duration ECG signals
Yıldırım, Özal; Pławiak, Paweł; Tan, Ru-San; Acharya, U. Rajendra
This article presents a new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis. Cardiovascular disease prevention is one of the most important tasks of any health care system as about 50 million people are at risk of heart disease in the world. Although automatic analysis of ECG signal is very popular, current methods are not satisfactory. The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias. Described research are based on 1000 ECG signal fragments from the MIT - BIH Arrhythmia database for one lead (MLII) from 45 persons. Approach based on the analysis of 10-s ECG signal fragments (not a single QRS complex) is applied (on average, 13 times less classifications/analysis). A complete end-to-end structure was designed instead of the hand-crafted feature extraction and selection used in traditional methods. Our main contribution is to design a new 1D-Convolutional Neural Network model (1D-CNN). The proposed method is 1) efficient, 2) fast (real-time classification) 3) non-complex and 4) simple to use (combined feature extraction and selection, and classification in one stage). Deep 1D-CNN achieved a recognition overall accuracy of 17 cardiac arrhythmia disorders (classes) at a level of 91.33% and classification time per single sample of 0.015 s. Compared to the current research, our results are one of the best results to date, and our solution can be implemented in mobile devices and cloud computing.
AtıfScopus801OpenAlex854
İncele - MakaleAçık Erişim2018Heartbeat type classification with optimized feature vectors
Yıldırım, Özal; Baloglu, Ulas Baran
In this study, a feature vector optimization based method has been proposed for classification of the heartbeat types. Electrocardiogram (ECG) signals of five different heartbeat type were used for this aim. Firstly, wavelet transform (WT) method were applied on these ECG signals to generate all feature vectors. Optimizing these feature vectors is provided by performing particle swarm optimization (PSO), genetic search, best first, greedy stepwise and multi objective evoluationary algorithms on these vectors. These optimized feature vectors are later applied to the classifier inputs for performance evaluation. A comprehensive assessment was presented for the determination of optimized feature vectors for ECG signals and best-performing classifier for these optimized feature vectors was determined.
AtıfScopus7OpenAlex7İncele - MakaleAçık Erişim20172D-DOST Özellik Çıkarımı Yöntemi ve LS-SVM Sınıflandırıcı Tabanlı Doku Sınıflandırma Sistemi
Baloglu, Ulas Baran; Yıldırım, Özal
Bu çalışmada, doku görüntülerinin sınıflandırılması için 2D-DOST (İki Boyutlu Ayrık Orthonormal Stockwell Dönüşümü) ve LS-SVM (En Küçük Kareler Destek Vektör Makineleri) tabanlı yeni bir sınıflandırıcı sistemi önerilmiştir. Önerilen sistem, özellik çıkarımı ve sınıflandırma olmak üzere iki ana bölümden oluşmaktadır. Özellik çıkarımı aşamasında, görüntüleri temsil eden ait ayırt edici özellik vektörleri 2D-DOST tabanlı özellik çıkarım yöntemi ile elde edilmektedir. Sınıflandırma aşamasında ise yüksek başarım oranı ve doğruluğa sahip olan LS-SVM sınıflandırıcısı ile doku görüntüleri sınıflandırılmaktadır. LS-SVM’nin eğitimi her bir doku görüntüsüne ait ayırt edici özellik vektörleri üzerinde gerçekleştirilmiştir. Test verisi olarak hazırlanan doku görüntüleri eğitilmiş LS-SVM sınıflandırıcının girişine uygulanmıştır. Önerilen yöntemin performans testleri için alt-görüntüler ile elde edilen farklı veri setleri üzerinde gerçekleştirilmiştir. Bu veri setleri hem normal doku görüntülerini hem de gürültülü doku görüntülerini içermektedir. Veri setleri içerisindeki alt görüntüler Brodatz ve Kylberg doku veri tabanlarından türetilmiştir. Gürültülü verilerin oluşturulmasında, farklı seviyelerde Gaussian ve Tuz&Biber gürültüsü kullanılmıştır. Çalışma sonuçları, önerilen 2D-DOST ve LS-SVM tabanlı sınıflandırıcının doku görüntülerini yüksek başarım oranı ile sınıflandırabildiğini ve gürültüye karşı gürbüz olduğunu göstermektedir.
AtıfOpenAlex4İncele