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Record W2561584218 · doi:10.1109/auv.2016.7778686

Risk analysis of an Autonomous Surface Craft for operation in harsh ocean environments

2016· article· en· W2561584218 on OpenAlexafffund
Zhi Li, Ralf Bachmayer, Andrew Vardy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of NewfoundlandAtlantic Canada Opportunities AgencyResearch and Development Corporation of Newfoundland and LabradorSuncor Energy Incorporated
KeywordsCraftMarine engineeringComputer scienceEnvironmental scienceEngineeringGeographyArchaeology

Abstract

fetched live from OpenAlex

Risk analysis of an Autonomous Surface Craft (ASC) is a very important subject since it is closely related to the safety of an ASC operating in harsh ocean environments. In this study, we provide a detailed analysis of the primary disturbance of ocean waves and its influence on an ASC's roll motion. A conventional decoupled nonlinear roll motion model has been chosen and through experiments the roll motion model parameters are successfully identified. Using this model, we perform extensive simulations under different assumed wave conditions. Our analysis is based on the well-known erosion basin technique in phase plane. The safe region proportion has been defined to serve as a safety criterion. Through analysis, we find out that the safety of an ASC operating in the ocean is related to the wave amplitude and wave encounter frequency. This relationship provides a useful reference for risk analysis of an ASC. The results provided can be regarded as guidelines for an ASC's safety determination, and thus they are planned to be integrated into an ASC for its self safety awareness. The presented method can also be extended to other medium-size or large marine vessels for their operational safety analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.350
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2016
Admission routes2
Has abstractyes

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