Výsledky vyhledávání - "Merijn de Bakker"

A Federated Learning approach to use confidential hydrocarbon extraction data for investigating coastal subsidence

Autor: Madelon Molhoek, Kay Koster, Merijn de Bakker, Thibault Candela, Joana Esteves Martins, Peter Fokker

Hydrocarbon reservoirs can be situated below low-lying coastal plains. Extraction from these reservoirs are known to cause substantial amounts of subsidence. Yet, the relative contribution of hydrocarbon extraction to total subsidence is often ignore

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fe614faff813f6660848c5ab6fa14de3
https://doi.org/10.5194/egusphere-egu22-3943

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Unraveling anthropogenic causes of subsidence in the coastal plain of Friesland (NL)

Autor: Manon Verberne, Joana E. Martins, Willem Dabekaussen, Andrei Bocin-Dumitriu, Aris Lourens, Merijn De Bakker, Kay Koster, Thibault Candela, Peter A. Fokker, Madelon Molhoek

The coastal plains of the Netherlands are subject to anthropogenic and natural subsidence with rates which are an order of magnitude higher than sea-level rise. Because one-third of the Netherlands lies below mean sea level, subsidence may threaten t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8c1452eb1dcd3778badf4bd07735f4a7
https://doi.org/10.5194/egusphere-egu21-6369

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Retrieval of reflectivity images from ambient seismic noise correlations using machine learning as a noise-panel classification tool

Autor: Merijn de Bakker, Arie Verdel, B. Boullenger, Stefan Carpentier

The theory of ambient seismic noise interferometry offers techniques to retrieve estimates of inter-receiver responses from continuously recorded ambient seismic noise. This is usually achieved by correlating and stacking successive noise panels over

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6387f394f265f72e6cf553ddae0178ba
https://doi.org/10.5194/egusphere-egu2020-1680

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Multiclass semi-supervised learning for animal behavior recognition from accelerometer data

Autor: Willem Bouteny, Hamideh Afsarmanesh, Maarten van Someren, Merijn de Bakker, Jafar Tanha, Judy Shamoun-Baranesy

Publikováno v: ICTAI
24th IEEE International Conference on Tools with Artificial Intelligence, 690-697
STARTPAGE=690;ENDPAGE=697;TITLE=24th IEEE International Conference on Tools with Artificial Intelligence

In this paper we present a new Multiclass semi-supervised learning algorithm that uses a base classifier in combination with a similarity function applied to all data to find a classifier that maximizes the margin and consistency over all data. A nov

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::12c43c928f48dced40bd9caf0aefe070
https://doi.org/10.1109/ictai.2012.98

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