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Ridvan Ozdemir 's Projects

material-handling-system-simulation icon material-handling-system-simulation

In this study, Petri nets are used for modeling framework. The model is constructed and implemented in MATLAB’s Simulink Stateflow environment. An interface is designed in MATLAB’s GUI tool for setting system’s specific parameters, choosing the type of input load, demonstrating the performance graphs, monitoring the system online and real-time controlling. The RGV’s route is divided into 2 zone with an interlocking application for reducing unnecessary movements after the simulation of system was completed and these changes were implemented on the simulation. Finally, the performance of the system for with/without interlocking application are compared with each other according to the performance indices in literature under three different type load inputs which are low, medium and high frequency.

mlep-public icon mlep-public

Public repo for DeepLearning.AI MLEP Specialization

quality_control_application_using_dl icon quality_control_application_using_dl

The number of smart factories is increasing day after day to reach the vision of Industry 4.0. Computer vision and image processing have important roles in the systems whose aim is unmanned production. In the industrial automation applications, computer vision is mostly used at the quality control stage. In this stage, there are many applications which use image-processing methods for object detection and classification but deep learning-based applications are rarely seen. In this work, a visual quality control automation application is proposed by using a camera placed over the assembly line in a smart factor model. The product is detected in an image obtained from the assembly line and then classified as “okay” or “not okay” using deep learning methods. After the deep learning-based quality control, the “okay” products continue their production stages and the “not okay” products are separated from the production line using a PLC, which controls the line. It is seen with this application that deep learning methods in automation applications will have an important role in transitioning to the industry 4.0.

rail_fault_detection_with_yolov4 icon rail_fault_detection_with_yolov4

Training YOLO algorithm on a custom dataset which has multi-class for Rail Fault Detection on Google Colab. Then used this detector as a teacher and labeled all unseen image to create pseudo labeled images. Finally, pseudo labeled images used to train the detector again to boost model performance.

real_time_fer_using_dl icon real_time_fer_using_dl

In this study, we detected the faces on real-time video data to recognize the anger, fear, happy, surprise, sad and neutral emotions upon these detected faces using deep learning methods. We created our own dataset to use in this study for six different facial emotions. At first stage, we created a convolutional neural network and trained it over our dataset by scratching method and we achieved 50% accuracy rate. Then, we increased the number of images in our database by 3 times, and get better accuracy which is 62%. Thanks to transfer training method and AlexNet's pre-trained networks, we reached 74% accuracy rate after increasing the number of images 80% in the dataset.

robust_fer_using_dl_with_ridnet icon robust_fer_using_dl_with_ridnet

In this study, emotion recognition process is performed by using deep learning methods for seven different facial expressions from the dataset (RidNet) which is created by using images that are publicly accessible from internet. Afterwards, transfer learning over RidNet is done with well-known convolutional neural network architectures such as AlexNet, GoogLeNet and ResNet101. Compound Facial Expressions of Emotion (CE) and Static Facial Expressions in the Wild (SFEW) datasets are determined to be used as test datasets. In the first experimental studies, convolutional neural network architecture with the best classification performance is determined. This convolutional neural network is trained using AffectNet, The Karolinska Directed Emotional Faces and RidNet. Similar classification performances are achieved when the AffectNet, KDEF, and RidNet-trained networks are tested with the dataset (CE) generated in a controlled environment. In the test dataset (SFEW) in an uncontrolled environment, RidNet-trained network gives a significant advantage over the other networks.

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