WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Enhancing Mode Transition Dynamics in Non-Inverting Buck-Boost Inverters for PV Systems(Institute of Electrical and Electronics Engineers Inc., 2026) Keskinkilic, E.; Tekgun, B.Quasi-single-stage inverters (QSSIs) are notable for their simple structure and bidirectional operation capability in applications such as photovoltaic (PV) systems. Among these QSSI, the non-inverting buck-boost inverter (NIBBI) or four-switch buck-boost inverter (FSBBI) is often preferred due to its ability to perform both step-down and step-up operations. However, when traditional control is used, achieving a smooth transition and efficient conversion becomes challenging as the output voltage approaches the input voltage. The pulse width ratio limitations and non-idealities of active and passive components are the cause of this. In this paper, a comparative analysis of the mode transition techniques in FSBBI is presented using methods available for DC/DC converters. System efficiency and output voltage signal quality are selected as performance metrics. A 2-kW FSBBI is installed and controlled using single, two, modified two, three, and four-mode techniques. Simulation and experimental studies were conducted to validate the results. Based on these studies, the four-mode control technique was observed to be the most effective in eliminating dead zone effects, reducing total harmonic distortion (THD), and achieving the highest system efficiency in a PV system where a battery powers the AC load. Experimental results indicate that the four-mode modulation attained an efficiency of 95.49% with a THD of 2.97%. © 1986-2012 IEEE.Conference Object Citation - WoS: 1Citation - Scopus: 5Sliding Mode Control of a Switched Reluctance Motor Drive With Four-Switch Bi-Directional DC-DC Converter for Torque Ripple Minimization(Institute of Electrical and Electronics Engineers Inc., 2020-09) Ates, Ertugrul; Tekgun, Burak; Ablay, GünyazIn this paper, a method to drive switched reluctance motors (SRM) with a modular four-switch bidirectional DC-DC converter and an H-bridge is proposed. The DC-DC converter operates as a buck or a boost converter with constant frequency to control each phase current while the H-bridge inverter switches only twice in a period to adjust the polarity of the phase voltage. Sliding mode control is designed to have fast and robust current control in the DC-DC converter. The sliding surface equation which is derived for all operation modes including buck and boost modes in motoring and regenerating conditions is defined with the estimated inductor current. The proposed drive system eliminates the bulk DC-capacitors and allows one to adjust the bus voltage individually for all phases. Moreover, the proposed system topology works with only one high-frequency switching device in the DC-DC conversion stage rather than two in conventional drives which provides a simpler current control and reduced switching losses. © 2020 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 3Security Through Digital Twin-Based Intrusion Detection: A SwaT Dataset Analysis(Institute of Electrical and Electronics Engineers Inc., 2023-10-18) Bozdal, MehmetDigital twin, as a virtual replica of physical entity, offer valuable insights into Industrial Control System (ICS) behavior and characteristics. Leveraging the convergence of digital twins and cybersecurity, this research explores its role in securing critical infrastructure, using the Secure Water Treatment (SWaT) system as a case study. Existing intrusion detection systems (IDS) for SWaT encounter challenges related to requiring huge amounts of a dataset for training, being unable to adopt high data dimensionality, and adaptability to emerging threats. To address these issues, a hybrid digital twin model is proposed, combining physics-based models and data-driven approaches. This model facilitates precise attack localization and explainable IDS outcomes. The method exhibits promising capabilities for enhancing critical infrastructure security and adapting to evolving cyber threats. Experimental results demonstrate the ability to detect eight out of nine attack types. © 2024 Elsevier B.V., All rights reserved.Conference Object Quasi-Static Operation of 2-Axis Microscanners With AlN Piezoelectric Quad-Actuators(Institute of Electrical and Electronics Engineers Inc., 2021-08-25) Hah, D.Aluminum nitride (AlN) started to draw attentions as a material for piezoelectric actuation owing to its CMOS process compatibility and safeness for biomedical applications. Due to its relatively low piezoelectric coefficients, AlN-based piezoelectric actuators have been mostly operated in resonance modes, especially in optical scanning. This paper presents a novel design of a 2-axis-tilt microscanner with AlN piezoelectric quad-actuators and meander-shaped hinges for reasonable quasi-static operation. Through finite-element-method simulation, it is shown that the proposed device can have about 9 degree of optical scan angle in two dimensions with the voltage amplitude of 50 V. Lissajous scanning operation of the device is demonstrated as well via simulation. © 2021 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 1Citation - Scopus: 1Peer-to-Peer Localization via On-Board Sensing for Aerial Flocking(Institute of Electrical and Electronics Engineers Inc., 2020-06) Omar Rajab, Fat Hy; Guler, Samet; Shamma, Jeff S.; Rajab, Fat-Hy OmarThe performance of mobile multi-robot systems dramatically depends on the mutual awareness of individual robots, particularly the positions of other robots. GPS and motion capture cameras are commonly used to acquire and ultimately communicate positions of robots. Such sensing schemes depend on infrastructure and restrict the capabilities of a multi-robot system, e.g., the robots cannot operate in both indoor and outdoor environments. Conversely, peer-to-peer localization algorithms can be used to free the robots from such infrastructures. In such systems, robots use on-board sensing to infer the positions of nearby robots. In this approach, it is essential to have a model of the motion of other robots. We introduce a flocking localization scheme that takes into account motion behavior exhibited by the other robots. The proposed scheme depends only on the robots' on-board sensors and computational capabilities and yields a more accurate localization solution than the peer-to-peer localization algorithms that do not take into account the flocking behavior. We verify the performance of our scheme in simulations and demonstrate experiments on two unmanned aerial vehicles. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 1Citation - Scopus: 1Microgrid Environmental Impact(Institute of Electrical and Electronics Engineers Inc., 2020-09-28) Al-Agtash, Salem Y.; al-Hashem, Mohammad; Batarseh, Mohanad; Bintoudi, Angelina D.; Tsolakis, Apostolos Charalampos; Tzovaras, Dimitrios K.; Hadjidemetriou, Lenos; Khiat, MounirPower plants have bad impacts on the environment. One of these impacts is Carbon Dioxide (CO2) emission resulted from power plants that depend on fossil fuel, oil and natural gas. Renewable energy is considered as an important solution for this problem since it is classified as clean and environmentally friendly source of energy and helps reducing the dependency on conventional power plants. High renewable energy penetration into power systems is a big challenge that can be solved by deploying the concept of smart Micro-Grids. This paper presents a study on how much reduction of CO2 emission can be resulted from deploying smart micro-grid concept on a university campus, German Jordanian University (GJU) campus was taken as a pilot. The micro-grid is meant to operate according to an optimum resource scheduling framework that guarantee a minimum operational cost while achieving high local power availability. © 2020 Elsevier B.V., All rights reserved.Article Citation - Scopus: 1A Laser-Assisted Cellular Electrophysiology Measurement System(Institute of Electrical and Electronics Engineers Inc., 2021-02-01) Seymen, A.A.; Özgür, E.; Soran-Erdem, Z.; Ortaç, B.Patch-clamp technique is the gold standard for cellular electrophysiological measurements, which is capable of measuring single ion transport events across the cell membrane. However, the measurement possesses significant complexities, and it requires a high level of expertise, while its experimental throughput is nevertheless considerably low. Here, we suggest and experimentally demonstrate a laser-Assisted method for performing cellular electrophysiological measurements. Femtosecond laser pulses, coupled to an optical microscope, are used to form a sub-micrometer hole on a thin polymer membrane separating two electrodes, where a nearby cell is subsequently placed onto the hole by negative pressure. Afterwards, the cell is punctured using subsequent laser exposure, revealing the cell membrane over the hole for electrophysiological recording. This system could be used to increase the output amount of the electrophysiological measurements substantially. © 2021 Elsevier B.V., All rights reserved.Conference Object Hepatoselüler Karsinom Oluşumunda Etkili Moleküler Mekanizmaların İn Siliko Yöntemlerle Araştırılması(Institute of Electrical and Electronics Engineers Inc., 2020-09) Doǧan, Refika Sultan; Saka, Samed; Bakir-Güngör, Burcu; Gungor, Burcu BakirHepatocellular carcinoma (HCC) is the most common cause of cancer-related death in the world. The molecular changes in the organism during the development of HCC are not fully understood. The aim of the present study is to contribute to the identification of critical genes and pathways associated with HCC via integrating various bioinformatics methods. In this study, experiments were conducted on gene expression data of 14 HCC tissues and noncancerous control tissues. A total of 1229 genes, which show a statistically significant change between the groups, were identified. Among these, 681 genes were upregulated and 548 genes were downregulated. Via mapping the detected genes into protein protein interaction networks, active subnetwork search, subnetwork topological analysis and functional enrichment analyses were carried out. The interactions between the molecular pathways affected by HCC were also presented. © 2020 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 4Citation - Scopus: 10Sağlıkta Blokzincir Tabanlı Sistem Bilişimi Uygulamaları(Institute of Electrical and Electronics Engineers Inc., 2020-10-05) Dedeturk, Beyhan Adanur; Bakir-Güngör, Burcu; Soran, Ahmet; Adanur, BeyhanRecently, the use of blockchain technology in the field of healthcare has increased. Although blockchain technology brought several innovations to healthcare, still there are problems waiting to be resolved. In order to provide alternative solutions to these problems, the use of fog computing together with blockchain technology has been proposed. In this study, the applications of blockchain based fog computing technology in healthcare are investigated. The aim of this study is to provide the readers an idea about the interactive use of blockchain and fog computing in the field of healthcare. For this purpose, firstly, fog computing and blockchain technologies are introduced. Afterwards, the integration of these areas, the advantages and disadvantages of using these technologies in the field of healthcare is discussed and a new system architecture is proposed. © 2021 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Koroner Arter Hastalığı Tanısı İçin Alan Bilgisi İçeren Topluluk Öznitelik Seçim Yöntemi(Institute of Electrical and Electronics Engineers Inc., 2020-10-05) Kolukisa, Burak; Güngör, Vehbi Çağrı; Bakir-Güngör, Burcu; Gungor, Burcu BakirCoronary Artery Disease (CAD) is the condition where, the heart is not fed enough as a result of the accumulation of fatty matter called atheroma in the walls of the arteries. In 2016, CAD accounts for 31% (17.9 million) of the world's total deaths and its diagnosis is difficult. It is estimated that approximately 23.6 million people will die from this disease in 2030. With the development of machine learning and data mining techniques, it might be possible to diagnose CAD inexpensively and easily via examining some physical and biochemical values. In this study, for the CAD classification problem, a novel ensemble feature selection methodology that incorporates domain knowledge is proposed. Via applying the proposed methodology on the UCI Cleveland CAD dataset and using different classification algorithms, performance metrics are compared. It is shown that in our experiments, when Multilayer Perceptron classifier is used with 9 selected features, our proposed solution reached 85.47% accuracy, 82.96% accuracy and 0.839 F-Measure. As a future work, we aim to generate a machine learning model that can quickly diagnose CAD on real-time data in hospitals. © 2021 Elsevier B.V., All rights reserved.