Elektrik - Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/202

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Deep Learning Based Formation Control of Drones
(Springer Science and Business Media Deutschland GmbH, 2021) Kabore, Kader M.; Güler, Samet; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Güler, Samet
Robot swarms can accomplish demanding missions fast, efficiently, and accurately. For a robust operation, robot swarms need to be equipped with reliable localization algorithms. Usually, the global positioning system (GPS) and motion capture cameras are employed to provide robot swarms with absolute position data with high precision. However, such infrastructures make the robots dependent on certain areas and hence reduce robustness. Thus, robots should have onboard localization capabilities to demonstrate a swarm behavior in challenging scenarios such as GPS-denied environments. Motivated by the need for a reliable onboard localization framework for robot swarms, we present a distance and vision-based localization algorithm integrated into a distributed formation control framework for three-drone systems. The proposed approach is established upon the bearing angles and the relative distances between the pairs of drones in a cyclic formation where each drone follows its coleader. We equip each drone with a monocular camera sensor and derive the bearing angle between a drone and its coleader with the recently developed deep learning algorithms. The onboard measurements are then relayed back to the formation control algorithm in which every drone computes its control action in its own frame based on its neighbors only, forming a completely distributed architecture. The proposed approach enables three-drone systems to perform in coordination indepen- dent of any external infrastructure. We validate the performance of our approach in a realistic simulation environment. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ROSE: A Novel Approach for Protein Secondary Structure Prediction
(Springer Science and Business Media Deutschland GmbH, 2021) Görmez, Yasin; Aydın, Zafer; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Görmez, Yasin; Aydın, Zafer
Three-dimensional structure of protein gives important information about protein’s function. Since it is time-consuming and costly to find the structure of protein by experimental methods, estimation of three-dimensional structures of proteins through computational methods has been an efficient alternative. One of the most important steps for the 3-D protein structure prediction is protein secondary structure prediction. Proteins which contain different number and sequences of amino acids may have similar structures. Thus, extracting appropriate input features has crucial importance for secondary structure prediction. In this study, a novel model, ROSE, is proposed for secondary structure prediction that obtains probability distributions as a feature vector by using two position specific scoring matrices obtained by PSIBLAST and HHblits. ROSE is a two-stage hybrid classifier that uses a one-dimensional bi-directional recurrent neural network at the first stage and a support vector machine at the second stage. It is also combined with DSPRED method, which employs dynamic Bayesian networks and a support vector machine. ROSE obtained comparable results to DSPRED in cross-validation experiments performed on a difficult benchmark and can be used as an alternative to protein secondary structure prediction. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Synchrotron-Based techniques for analysis of perovskite solar cells
(Wiley-Blackwell, 2021) Farooq, Umar; Phul, Ruby; Shabbir, Mohd; Arif, Rizwan; Akrema; 0000-0003-3269-4951; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Phul, Ruby
Perovskite solar cells (PSCs) have been proven as promising material, owing to their remarkable performance attained in the field of photovoltaic (PV) and optoelectronics. These materials possess direct band gaps, high absorption coefficient, low exciton binding energy, and long carrier diffusion lengths. However, the primary electronic structure, size and spatial distribution, and other significant properties of perovskite materials are not fully understood due to a lack of precise and high-spatiotemporal resolution characterization techniques. A synchrotron provides high-brilliance X-ray beams that can easily penetrate deeper into the matter to explore the material's properties at the atomic or molecular level within a short time. Herein, we discuss synchrotron techniques for determining the structure and properties of perovskite materials within the bulk, at the surfaces and the interfaces. This chapter is dedicated to providing the latest findings on developing synchrotron-based techniques tailored for PSC. The method of characterization is also discussed with sample preparation. Finally, we focused on the state-of-the-art strategies for gaining in-depth knowledge of mechanism enhancing the photovoltaic performance and providing decisive answers to the outstanding science problems in the perovskite field, pushing forward technological development.

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