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Stały URI dla kolekcjihttp://hdl.handle.net/11652/4775

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20232

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Tematsearch.filters.applied.operator.equals: search.filters.subject.sztuczna inteligencjaTematsearch.filters.applied.operator.equals: search.filters.subject.machine learning

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Teraz wyświetlane 1 - 2 z 2
  • Pozycja
    Performance Analysis of Machine Learning Platforms Using Cloud Native Technology on Edge Devices
    (Wydawnictwo Politechniki Łódzkiej, 2023) Cłapa, Konrad; Grudzień, Krzysztof; Sierszeń, Artur
    This article presents the results of an experiment performed on a machine learning edge computing platform composed of a virtualized environment with a K3s cluster and Kubeflow software. The study aimed to analyze the effectiveness of executing Kubeflow pipelines for simulated parallel executions. A benchmarking environment was developed for the experiment to allow system performance measurements based on parameters, including the number of pipelines and nodes. The results demonstrate the impact of the number of cluster nodes on computational time, revealing insights that could inform future decisions regarding increasing the effectiveness of running machine learning pipelines on edge devices.
  • Pozycja
    Digital Twin for Training Set Generation for Unexploded Ordnance Classification
    (Wydawnictwo Politechniki Łódzkiej, 2023) Ściegienka, Piotr; Blachnik, Marcin
    The use of machine learning methods for unexploded ordnance (UXO) detection and classification is very limited. This limitation derives from the lack of representative and enough large training data. To overcome this issue we propose a construction of a digital twin where UXO and non-UXO objects are represented using mathematical models in a simulated Earth magnetic field. The use of digital twins allows for simulating and collecting a large training set which can be used for training machine learning models. In the conducted research we discuss obtained results and point out several of the detected problems.