Bilal Kabas

I am a Machine Learning Engineer at Aselsan and an M.Sc. student in the Department of Electrical and Electronics Engineering at Bilkent University.

As an engineer, I specialize in acoustic signal classification using deep learning, working with multi-channel acoustic data collected by a DAS (distributed acoustic sensing) interrogator. I engage in exploratory data analysis and focus on designing and developing machine learning models to enable data-driven decision-making.

As a researcher, my passion lies in pioneering innovative deep learning techniques to address complex challenges in medical image synthesis, with my current work centering on diffusion models for achieving high-fidelity image translation. In my previous endeavors, I focused on deep learning and autonomous systems, working on drone projects for tasks like object avoidance and source seeking using deep reinforcement learning.

Previously, I earned my B.Sc. degree in Electrical and Electronics Engineering from Abdullah Gul University in 2020, graduating as the class valedictorian. During my undergraduate studies, I gained invaluable research experience at DIREC Lab, collaborating with Dr. Samet Guler on projects related to autonomous UAV navigation and mobile robotics.

latest posts

Oct 30, 2023	Analyzing Rank-Frequency and Type-Token Relationships in Literary Texts
Apr 2, 2023	Attention Mechanism in Transformers

selected publications

Autonomous UAV Navigation via Deep Reinforcement Learning Using PPO

Bilal Kabas

In 2022 30th Signal Processing and Communications Applications Conference (SIU), 2022

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In this paper, a computer vision-based navigation system is proposed for autonomous unmanned aerial vehicles (UAV). The proposed navigation system is based on a deep reinforcement learning-based high-level controller. In this paper, proximal policy optimization (PPO), which is a deep reinforcement learning method, is used to train the artificial neural network in an end-to-end way using a continuous reward function. The proposed method has been tested on images obtained from different modalities (RGB and depth) in simulation environments that are created using Unreal Engine and Microsoft AirSim. For the navigation problem that this work is concerned with, a success rate of 96% has been obtained by using RGB cameras. Since RGB cameras are lighter than depth cameras and the trained artificial neural network has a parameter number less than 170.000, the proposed method is suitable to be deployed in micro aerial vehicles.
Design optimization of a three-phase transformer using finite element analysis

Ahmet Furkan Hacan, Bilal Kabas, and Samet Oguten

arXiv preprint arXiv:2201.11769, 2022

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Optimization of design parameters of a transformer is a crucial task to increase efficiency and lower the material cost. This research presents an approach to model a three-phase transformer and optimize design parameters to minimize the volume and loss. ANSYS Maxwell 2D is used to model the transformer and analyze it for different design parameters. The multi-objective differential evolution algorithm is used to find optimum design parameters that minimize the volume and loss. In this paper, we present the optimum design parameters for a 1 kVA transformer with a particular input and output voltage specification. The transformer with these optimum design parameters is then tested for different loading conditions and power factor values. The results show that the maximum efficiency is obtained for 75% loading condition with unity power factor. As the power factor decreases, the efficiency decreases as well.