Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 1Modeling and Simulation of Advanced Nuclear Reactors(IEEE Computer Society help@computer.org, 2013-11) Ablay, GünyazAdvanced nuclear power plants are currently being proposed with a number of various designs. However, there is a lack of modeling and control strategies to deal with load following operations. This research investigates a possible modeling approach for advanced nuclear power plants in order to provide an assessment way to the concept designs. The modeling approach covers linear and nonlinear reactor modeling and linear modeling of heat exchanger-turbine- generator systems. Numerical results are presented on an example molten-salt type nuclear power plant system to demonstrate the validity and effectiveness of the modeling approach. © 2013 IEEE. © 2014 Elsevier B.V., All rights reserved.Article Citation - Scopus: 5Modeling and Real Time Digital Simulation of Microgrids for Campuses Malta and Jordan Based on Multiple Distributed Energy Resources(Institute of Advanced Engineering and Science, 2021-02-01) Khiat, Sidahmed; Chaker, Abdelkader El Kader; Zacharia, Lazaros; Onen, AhmetThis paper presents the modeling and real-time digital simulation of two microgrids: The malta college of arts, science and technology (MCAST) and the German jordan university (GJU). The aim is to provide an overview of future microgrid situation and capabilities with the benefits of integrating renewable energy sources (RES), such as photovoltaic panels, diesel generators and energy storage systems for projects on both campuses. The significance of this work starts with the fact that real measurements were used from the two pilots, obtained by measuring the real physical systems. These measures were used to plan different solutions regarding RES and energy storage system (ESS) topologies and sizes. Also, the demand curves for the real microgrids of MCAST and GJU have been parameterized, which may serve as a test bed for other studies in this area. Based on actual data collected from the two pilots, a real-time digital simulation is performed using an RT-LAB platform. The results obtained by this tool allow the microgrid manager to have a very accurate vision of the facility operation, in terms of power flow and default responses. Several scenarios are studied, extracting valuable insight for implementing both projects in the future. Eventually, the proposed models would be a blueprint for training and research purposes in the microgrid field. © 2020 Elsevier B.V., All rights reserved.Conference Object Metabolomics Data Analysis to Discover Chronic Granulomatous Disease-Associated Biomarkers Utilizing G-S-M Machine Learning Model via Grouping Metabolites According to Ion Type(Institute of Electrical and Electronics Engineers Inc., 2024-10-16) Ersöz, Nur Sebnem; Bakir-Güngör, Burcu; Yousef, MalikChronic Granulomatous Disease (CGD) is a rare, inherited immunodeficiency disorder characterized by white blood cells unable to effectively kill certain bacteria and fungi. This defect results in the formation of clusters of immune cells called granulomas that form at sites of infection or inflammation. Therefore, identification of disease-related biomarkers is a critical step in advancing precision medicine and improving diagnostic accuracy. In this study, we applied a G-S-M machine learning approach to metabolomics data to uncover CGD-Associated biomarkers. We obtained a metabolomics dataset from Gene Expression Omnibus with GSE220260 accession number. Data includes 85 samples (16 healthy controls and 69 CGD samples) with comprehensive metabolic profiles obtained using liquid chromatography-mass spectrometry analysis. Dataset includes metabolite names with their ion type and formula. In order to identify CGD related metabolites and their ion types, G-S-M was used as a grouping function when performing machine learning oriented metabolomics data analysis. We have performed the G-S-M approach by grouping metabolites according to their ion type. In the training part of the G-S-M approach, metabolites annotated with selected ion types have been utilized to perform a two-class classification task which generates an important set of ion type output. We also compared the performance results of the G-S-M machine learning model with traditional feature selection methods; XGB, SKB, IG, FCBF, MRMR, CMIM with random forest classifier. 100 times Monte-Carlo Cross Validation was used in our experiments. It was observed that G-S-M, XGB, SKB and FCBF methods similarly provided the best performances. In this study, besides its performance, G-S-M method used groups based on ion types unlike TFS, and then identified relevant Chronic Granulomatous Disease-associated metabolites. © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 54EdgeAISim: A Toolkit for Simulation and Modelling of AI Models in Edge Computing Environments(Elsevier Ltd, 2024-02) Nandhakumar, Aadharsh Roshan; Baranwal, Ayush; Choudhary, Priyanshukumar; Golec, Muhammed; Gill, Sukhpal SinghTo meet next-generation Internet of Things (IoT) application demands, edge computing moves processing power and storage closer to the network edge to minimize latency and bandwidth utilization. Edge computing is becoming increasingly popular as a result of these benefits, but it comes with challenges such as managing resources efficiently. Researchers are utilising Artificial Intelligence (AI) models to solve the challenge of resource management in edge computing systems. However, existing simulation tools are only concerned with typical resource management policies, not the adoption and implementation of AI models for resource management, especially. Consequently, researchers continue to face significant challenges, making it hard and time-consuming to use AI models when designing novel resource management policies for edge computing with existing simulation tools. To overcome these issues, we propose a lightweight Python-based toolkit called EdgeAISim for the simulation and modelling of AI models for designing resource management policies in edge computing environments. In EdgeAISim, we extended the basic components of the EdgeSimPy framework and developed new AI-based simulation models for task scheduling, energy management, service migration, network flow scheduling, and mobility support for edge computing environments. In EdgeAISim, we have utilized advanced AI models such as Multi-Armed Bandit with Upper Confidence Bound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, and Actor-Critic Network to optimize power usage while efficiently managing task migration within the edge computing environment. The performance of these proposed models of EdgeAISim is compared with the baseline, which uses a worst-fit algorithm-based resource management policy in different settings. Experimental results indicate that EdgeAISim exhibits a substantial reduction in power consumption, highlighting the compelling success of power optimization strategies in EdgeAISim. The development of EdgeAISim represents a promising step towards sustainable edge computing, providing eco-friendly and energy-efficient solutions that facilitate efficient task management in edge environments for different large-scale scenarios. © 2023 Elsevier B.V., All rights reserved.Conference Object A Horizontal Magnetic Tweezer for Single Molecule Micromanipulations(Institute of Electrical and Electronics Engineers Inc., 2018-10) Ablay, Günyaz; Böyük, Mustafa; Eroǧlu, Yakup; İçöz, KutayMagnetic tweezers are able to manipulate cells or biomolecules for various applications and measurements. In this work, a horizontal magnetic tweezer is designed, modeled and controlled for single molecule manipulations. A method is presented for dynamic modeling of horizontal magnetic tweezers. A linear control method is designed to ensure a wide range of operation conditions with zero steady-state error. The horizontal magnetic tweezer is able to generate a wide range of piconewton scale forces on a superparamagnetic microparticle for single molecule separation, and biosensor developments. © 2019 Elsevier B.V., All rights reserved.