Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 2Data-Driven Methods for Optimal Setting of Legacy Control Devices in Distribution Grids(IEEE Computer Society, 2024-07-21) Savasci, Alper; Ceylan, Oǧuzhan; Paudyal, SumitThis study presents machine learning-based dispatch strategies for legacy voltage regulation devices, i.e., onload tap changers (OLTCs), step-voltage regulators (SVRs), and switched-capacitors (SCs) in modern distribution networks. The proposed approach utilizes k-nearest neighbor (KNN), random forest (RF), and neural networks (NN) to map nodal net active and reactive injections to the optimal legacy controls and resulting voltage magnitudes. To implement these strategies, first, an efficient optimal power flow (OPF) is formulated as a mixed-integer linear program that obtains optimal decisions of tap positions for OLTCs, SVRs, and on/off status of SCs. Then, training and testing datasets are generated by solving the OPF model for daily horizons with 1-hr resolution for varying loading and photovoltaic (PV) generation profile. Case studies on the 33-node feeder demonstrate high-accuracy mapping between the input feature and the output vector, which is promising for integrated Volt/VAr control schemes. © 2024 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Integrative Analyses in Omics Data: Machine Learning Perspective(Deutsche Gesellschaft fur Medizinische Informatik, Biometrie und Epidemiologie e.V., 2023) Ünlü Yazici, Miray; Bakir-Güngör, Burcu; Yousef, Malik; Yazici, Miray UnluDevelopments in the high throughput technologies have enabled the production of an immense amount of knowledge at the multi-omics level. Considering complex diseases which are affected by multi-factors, single omics datasets might not be sufficient to unveil the molecular mechanisms of heterogeneous diseases. Providing a comprehensive and systematic overview to explain disease hallmarks in significant depth is critical. Utilizing multi-omics datasets has led to the development of a variety of tools and platforms. Machine learning models are utilized in a wide variety of tools to tackle the complexity of disorders and to identify new biomolecular signatures and potential markers. Underlying aspects of these approaches are based on training the models for making predictions and classification of the given data. In this review, we describe current machine learning-based approaches and available implementations. Challenges in the enlightenment of disease mechanisms of onset and progression and future development of the field of medicine will be discussed. The prominence of biological interpretation of model output with corresponding biological knowledge will be also covered in this review. © 2023 Elsevier B.V., All rights reserved.