Exploring Synthetic Noise Algorithms for Real-World Similar Data Generation: A Case Study on Digitally Twining Hybrid Turbo-Shaft Engines in UAV/UAS Applications

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dc.authorscopusid57216462967
dc.authorscopusid57209449951
dc.authorscopusid55962680100
dc.authorscopusid58547084300
dc.authorscopusid58714662400
dc.contributor.authorAghazadeh Ardebili, Ali
dc.contributor.authorLongo, Antonella
dc.contributor.authorFicarella, Antonio
dc.contributor.authorKhalil, Adem
dc.contributor.authorKhalil, Sabri
dc.date.accessioned2024-09-11T19:57:29Z
dc.date.available2024-09-11T19:57:29Z
dc.date.issued2024
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description12th International Conference on Model and Data Engineering, MEDI 2023 -- 2 November 2023 through 4 November 2023 -- Sousse -- 305949en_US
dc.description.abstractAn emerging technology for automating Unmanned aircraft is digitally twining the system, and employing AI-based data-driven solutions. Digital Twin (DT) enables real-time information flow between physical assets and a virtual model, creating a fully autonomous and resilient transport system. A key challenge in DT as a Service (DTaaS) is the lack of Real-world data for training algorithms and verifying DT functionality. This article focuses on data augmentation using Real-world Similar Synthetic Data Generation (RSSDG) to facilitate DT development in the absence of training data for Machine Learning (ML) algorithms. The main focus is on the noise generation step of the RSSDG for a common Hybrid turbo-shaft engine because there is a significant gap in transforming synthetic data to Real-world similar data. Therefore we generate noise through 6 different noise generation algorithms before Rolling Linear Regression and Filtering the noisy predictions through Kalman Filter. The primary objective is to investigate the sensitivity of the RSSDG process concerning the algorithm that is used for noise generation. The study’s results support the potential capacity of RSSDG for digitally twining the engine in a Real-world operational lifecycle. However, noise generation through Weibull and Von Mises distribution showed low efficiency in general. In the case of Normal Distribution, for both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters, except the engine torque; however, Students-T, Laplace, and log-normal show better performance for engine torque RSSDG. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.description.sponsorshipEU-NextGenerationEU, (C83C22000560007); Italian Research Center on High Performance Computing, Big Data and Quantum Computing; International Council of Shopping Centers, ICSCen_US
dc.description.sponsorshipThe research was partially supported by the Ph.D. school of the University of Salento, Dep. of the complex systems engineering-XXXVI cycle, the RIPARTI regional project-dataEnrichment for Resilient UAS (assegni di RIcerca per riPARTire con le Imprese)-POC PUGLIA FESRTFSE 2014/2020, CUP F87G22000270002 and the Italian Research Center on High Performance Computing, Big Data and Quantum Computing (ICSC) funded by EU-NextGenerationEU (PNRR-HPC, CUP:C83C22000560007).en_US
dc.identifier.doi10.1007/978-3-031-49333-1_7
dc.identifier.endpage101en_US
dc.identifier.isbn978-303149332-4en_US
dc.identifier.issn0302-9743en_US
dc.identifier.scopus2-s2.0-85180796926en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage87en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-49333-1_7
dc.identifier.urihttps://hdl.handle.net/11363/8285
dc.identifier.volume14396 LNCSen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectData for Resilience; Digital Twins; Noise Generation; Realistic Synthetic Data; Synthetic data Generation; Unmanned Aircraft Systemsen_US
dc.titleExploring Synthetic Noise Algorithms for Real-World Similar Data Generation: A Case Study on Digitally Twining Hybrid Turbo-Shaft Engines in UAV/UAS Applicationsen_US
dc.typeConference Objecten_US

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