As a result of the developments in rail technologies, the vehicles and equipment used in these systems are also developing rapidly. It is in a rapid technological development in vehicles used in rail systems. The resistance forces affecting the movement of the rail system vehicles try to prevent its movement. In addition, the incident adherence and skidding that occur during the course of the rail system vehicles play an important role in the movements of these vehicles. Calculation of forces affecting the movement of rail system vehicles and designing these vehicles accordingly is also important for the safety of these vehicles. When braking is applied on a train running at a certain speed; It stops for a certain time and distance according to road condition, braking and speed on the train. The distance traveled until braking and stopping is the braking distance. Trains have an emergency braking system, but this system locks the train wheels. In this study along with the Anti-Lock Braking System (ABS), the rail clamping system was designed with PLC and SCADA. By using the ABS used in cars on the locomotive and wagons of the train, it is aimed to lock the wheels when the train brakes and to prevent the train from derailment in case of sudden braking with the rail clamp system.

This study is designed to investigate effective methods for artificial intelligence-based detection of hyperkinetic speech disorders using audio signals. The main objective of the study is to compare several machine learning models and create a platform for automatic diagnosis of speech disorders using the Hyperkinetic Dysarthria Speech (HDS) dataset. The data was pre-processed and acoustic features (MFCC, Chroma, Spectral Centroid, Zero-Crossing Rate) were extracted. The models compared included Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, AdaBoost, Extra Trees, XGBoost, LightGBM, CatBoost, and Multi-Layer Perceptron Classifier (MLPClassifier). According to the results, the Logistic Regression, KNN, SVM, CatBoost, and MLPClassifier models reached the level of 1.0 in terms of Accuracy, Precision, Recall, and F1-Score metrics, which indicates that pathological features in speech signals are classified with high accuracy. The research resulted in the development of the Streamlit platform based on the MLPClassifier model, which allows users to upload audio files or record speech in real time. This system reduces the subjectivity of traditional clinical assessments, facilitates early diagnosis, and helps improve the quality of life of patients. However, limitations of the study include the availability of data mainly in Russian and the relatively small size of the dataset (approximately 760 files), which limits the generalizability of the models to other languages. Also, the stability in noisy environments requires additional testing. Future studies are planned to incorporate multilingual data, use deep learning architectures, and validate the platform in clinical settings. This work will strengthen the role of computational hearing and machine learning in the detection of speech pathology and contribute to innovations in the field of neurological diagnostics and therapy.

Patients confined to bed for extended periods must frequently change sleeping posture to prevent pressure ulcers, which are difficult and undesirable to treat. In this study, three IMU sensors were placed on 108 bedridden patients to collect data for five different postures, resulting in 1,800,000 data points per sensor. These were converted into Eulerx, Eulery, and Eulerz values. The goal was to detect the sleep posture using a single IMU sensor. Four cases were defined: Case 1 used only IMU1, Case 2 only IMU2, Case 3 only IMU3, and Case 4 combined all three. Euler signals were converted into RGB images, framing the problem as an image classification task. A total of 2000 images were used, with 400 for training and 100 for testing in each case. A U-Net model was applied, achieving high IoU scores: 98.00%, 99.56%, 99.72%, and 98.20% respectively. Accuracy scores were 98.98%, 99.78%, 99.86%, and 99.09%, confirming U-Net’s effectiveness.

Orcik, Elazığ yöresinde üzümden ya da duttan elde edilen şıranın un ve özel bir toprakla bulamaç haline getirilip ceviz dizili ipin bu bulamaca istenilen kalınlığa göre bir veya daha fazla kez batırılıp güneşte kurutulmasıyla elde edilen yöresel bir üründür. Bu çalışmada, Türkiye'de tüketiminin yanısıra Avrupa ülkelerine de ihraç edilen orciğin fabrika otomasyon sistemi ile üretimine yönelik bir benzetimi yapılmıştır. Doğal klasik yöntemlerle üretilen orciğin her bir üretim aşaması ele alınarak Programlanabilir Mantıksal Denetleyici (PLC) otomasyon sistemi ile tasarımı yapılmış ve SCADA üzerinde benzetimi gerçekleştirilmiştir. Tasarlanan sistemle, orcik üretiminin klasik yöntem yerine PLC tabanlı otomasyon sistemine geçişi amaçlanmıştır. Elektrikli makineler, hızla büyüyen otomasyon hızının bir sonucu olarak sektörün bir parçası haline gelmiştir. Geleneksel endüstriyel işlemler makinelerin manuel çalıştırılmasıyla olur ve bu işlemler manuel müdahaleye bağlıdır. Bu süreç zaman alıcı ve pahalıdır. Endüstrilerdeki yönlendirme görevlerinin otomatikleştirilmesi verimliliği arttırır. Minimal insan müdahalesiyle PLC gibi otomasyon araçları kullanılarak tekrarlayan sürecin otomasyonu, geliştirilmiş verim, azaltılmış işletme maliyetleri, geliştirilmiş üretim hacimleri, daha iyi kalite kontrol ve endüstriyel güvenlik sağlanır