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Machine Learning Dataset to Support Paper "A comparison of machine learning classifiers in predicting safety for a multi-component dynamic system representation of an autonomous vessel"

Creator:

Sulkowski, Brendan and Collette, Matthew

Description:

This data set supports the published four-component integration problem using real-world weather forecasts from the European Centre for Medium-Range Weather Forecast and a simulated linear spring--mass--damper system excited by wave elevation. Each component in the spring--mass--damper system is monitored with techniques of differing accuracies representative of marine-type health uncertainties. Weather forecast uncertainty is included using weather predictions of significant wave height and peak period up to 10 days out. As well as their exact values, different test cases include the spring, mass, and damper being modeled as noisy sensors representative of sensors onboard a vessel, as well as the spring being modeled as a visually-inspected system component reflective of human impact onboard a vessel. Complete details are given in the referenced paper; this data set represents the inputs to the machine learning classifiers discussed.

Keyword:

Machine Learning, Inspection, Marine system, and Weather forecast

Citation to related publication:

Sulkowski, B and M. Collette. (2025) A comparison of machine learning classifiers in predicting safety for a multi-component dynamic system representation of an autonomous vessel. Applied Ocean Research, 154 (104368), https://doi.org/10.1016/j.apor.2024.104368

Discipline:

Engineering

Machine Learning Dataset to Support Paper "A comparison of machine learning classifiers in predicting safety for a multi-component dynamic system representation of an autonomous vessel"

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Machine Learning Dataset to Support Paper "A comparison of machine learning classifiers in predicting safety for a multi-component dynamic system representation of an autonomous vessel"

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