Work Description

Title: 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" Open Access Deposited

h
Attribute Value
Methodology
  • This CSV file contains: 1) Globel locations and 0h, 24h, 72h, 144h, and 240h sea-state forecasts for 1000 select locations in May 22 2023 captured from outputs in the European Centre for Medium-Range Weather Forecasts(ECMWF) Creative Commons Licensed Products  https://www.ecmwf.int/en/forecasts 2) Spring-mass-damper system parameters from random sampling, and various approximation of these parameters 3) Pass-fail motion threshold data
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.
Creator
Creator ORCID iD
Depositor
Depositor creator
  • true
Contact information
Discipline
Funding agency
  • Department of Defense (DOD)
ORSP grant number
  • F061726
Keyword
Citations to related material
  • 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
Resource type
Last modified
  • 12/16/2024
Published
  • 12/16/2024
Language
DOI
  • https://doi.org/10.7302/0v2f-0297
License
To Cite this Work:
Sulkowski, B., Collette, M. (2024). 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" [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/0v2f-0297

Relationships

This work is not a member of any user collections.

Files (Count: 2; Size: 215 KB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
Readme.txt 2024-12-13 2024-12-13 2.93 KB Open Access
MLPaperData.csv 2024-12-11 2024-12-11 212 KB Open Access

Date: 12 December, 2024

Dataset Title: 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"

Dataset Contact: Matthew Collette, [email protected]

Dataset Creators:
Name: Matthew Collette
Email: [email protected]
College of Engineering, University of Michigan, Ann Arbor, MI
ORCID: https://orcid.org/0000-0002-8380-675X

Name: Brendan Sulkowski
Email: [email protected]
College of Engineering, University of Michigan, Ann Arbor, MI
ORCID: https://orcid.org/0009-0000-9970-3918

Description:
This data set contains: 1) Globel locations and 0h, 24h, 72h, 144h, and 240h sea-state forecasts for 1000 select locations in May 22 2023 captured from outputs in the European Centre for Medium-Range Weather Forecasts(ECMWF) Creative Commons Licensed Products https://www.ecmwf.int/en/forecasts 2) Spring-mass-damper system parameters from random sampling, and various approximations of these parameters 3) Pass-fail motion threshold data that was used to train different ML models.

Use and Access:
This data set is made available under a Creative Commons Public Domain license (CC0 1.0).

To Cite and Understand Methods Behind Data:
Brendan Sulkowski, Matthew Collette, "A comparison of machine learning classifiers in predicting safety for a multi-component dynamic system representation of an autonomous vessel", Applied Ocean Research 2024 Open Access - https://doi.org/10.1016/j.apor.2024.104368

File Inventory:
MLPaperData.csv - dataset used to train models in the paper.

Data Description:
The data is contained in a CSV file. Each row represents one data point with one simulated spring-mass-damper and actual weather for a specific location on the globe, with the columns as attributes. Column descriptions the first row in the file, and are summarized here by column number in the file:

(1) mass value for spring-mass-damper (SMD), kg
(2) spring stiffness value for SMD, N/m
(3) damping value for SMD, N*s/m
(4-5) Significant wave height (meters) and period, Tp, (seconds), at 0000 on 22 May 2023 (hindcast)
(6-8) Category values for stiffness from simulated inspection (see paper for complete description of binning procedure 0= failed, 1=worn, 2=health)
(9-17) noise-contaminated estimates of m, k, and c (as labeled) see paper for complete details of noise parameters. Same units a (1-3)
(18-19) Lat/Long of each sample location on the globe
(20-23) 24, 72, 144, and 240 prior forecasts for Significant Wave Height at 0000 on 22 May 2023 (archived forecasts, meters)
(23-27) 24, 72, 144, and 240 prior forecasts for Tp at 0000 on 22 May 2023 (archived forecasts, seconds)
(28) Pass (0 - stayed below ) or fail (1 - stayed above) of motion with true m/c/k/ and hindcast Hs/Tp failed motion criteria. Criteria was if average of 1/1000th highest responses exceeded 5m of motion.

Funding:
Office of Naval Research Code 331 N00014-21-1-2795.

Download All Files (To download individual files, select them in the “Files” panel above)

Best for data sets < 3 GB. Downloads all files plus metadata into a zip file.

Files are ready   Download Data from Globus
Best for data sets > 3 GB. Globus is the platform Deep Blue Data uses to make large data sets available.   More about Globus

Remediation of Harmful Language

The University of Michigan Library aims to describe its collections in a way that respects the people and communities who create, use, and are represented in them. We encourage you to contact us anonymously if you encounter harmful or problematic language in catalog records or finding aids. More information about our policies and practices is available at Remediation of Harmful Language.