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Güncel Gönderiler

Risk analysis by using artificial neural networks at a plastic production plant

Adnanoğlu, Omran (Türk-Alman Üniversitesi Fen Bilimler Enstitüsü, 2024)

Bu çalışma, plastik üretim tesislerinde iş sağlığı ve güvenliği risklerini belirlemeyi ve bu risklere karşı otomasyon sistemlerini kullanarak korunma sağlamayı amaçlamaktadır. Çalışmanın temel yöntemi, FMEA (Failure ...
Prediction of psychological disorder levels with deep learning models based on electroencephalography (EEG) signals

Memişoğlu, Oğuzhan (Türk-Alman Üniversitesi Fen Bilimler Enstitüsü, 2024)

Psychological disorders, diverse and complex, paint a spectrum of challenges across the human experience. Depression is one of the life-threatening psychological disorders that impacts millions of individuals worldwide. ...
Additive manufacturing and mechanical properties of open-pore resin foam structures developed by image processing on the computed tomography data

Rastanawi, Nouh (Türk-Alman Üniversitesi Fen Bilimler Enstitüsü, 2023)

Malzemelerin bilgisayarlı tomografi (BT) verilerinden veri dönüştürme, modelleme için yaygın olarak kullanılmaktadır. Bununla birlikte, BT verileri aracılığıyla açık hücreli köpüklerin fiziksel üretimi köpükler için ...
Prediction of gait kinetics from joint angles using machine learning in patients with cerebral palsy

Özateş, Mustafa Erkam (Türk-Alman Üniversitesi Fen Bilimler Enstitüsü, 2023)

Joint moment and ground reaction force (GRF) during gait provide valuable information for clinical decision-making in patients with cerebral palsy (CP). Joint moments are calculated based on GRF using inverse dynamics ...
Egomotion estimation by fusing events and depth

Subutay, Ebru (Türk-Alman Üniversitesi Fen Bilimler Enstitüsü, 2023)

Bir robotun kendini konumlandırması, ¸cevresini algılamasıyla ba¸slar. Bir robo tun konumundan bilgi ¸cıkarmak, haritalar olu¸sturmaya, robotun genel konum de˘gi¸sikli˘gini hesaplamaya yardımcı olur. G¨orsel sens¨orler, ...
Explanatory comparative study of AI models in face expression recognition

Yenilmez, Fulya (Türk-Alman Üniversitesi Fen Bilimler Enstitüsü, 2023)

This thesis provides an analysis and comparison of five widely used CNN architec tures in face expression recognition task. The objective is to evaluate the performance of different CNN architectures available in the Keras ...
Predicting ground reaction forces during normal gait by solving tracking optimization problem

Atmaca, Kerim (Türk-Alman Üniversitesi Fen Bilimler Enstitüsü, 2024)

Ground Reaction Force (GRF) is a central biomechanical parameter in gaitanalysis, critical for understanding human locomotion mechanics. Traditional methods for GRF data acquisition, though precise, are often constrained ...

Tez Koleksiyonu

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Güncel Gönderiler

Risk analysis by using artificial neural networks at a plastic production plant ﻿

Prediction of psychological disorder levels with deep learning models based on electroencephalography (EEG) signals ﻿

Additive manufacturing and mechanical properties of open-pore resin foam structures developed by image processing on the computed tomography data ﻿

Prediction of gait kinetics from joint angles using machine learning in patients with cerebral palsy ﻿

Egomotion estimation by fusing events and depth ﻿

Explanatory comparative study of AI models in face expression recognition ﻿

Predicting ground reaction forces during normal gait by solving tracking optimization problem ﻿

Risk analysis by using artificial neural networks at a plastic production plant

Prediction of psychological disorder levels with deep learning models based on electroencephalography (EEG) signals

Additive manufacturing and mechanical properties of open-pore resin foam structures developed by image processing on the computed tomography data

Prediction of gait kinetics from joint angles using machine learning in patients with cerebral palsy

Egomotion estimation by fusing events and depth

Explanatory comparative study of AI models in face expression recognition

Predicting ground reaction forces during normal gait by solving tracking optimization problem