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Record W2773370428 · doi:10.1109/joe.2017.2771747

Coupled Hydroplane and Variable Ballast Control System for Autonomous Underwater Vehicle Altitude-Keeping to Variable Seabed

2017· article· en· W2773370428 on OpenAlexafffund
Jesse David, Robert Bauer, Mae Seto

Bibliographic record

VenueIEEE Journal of Oceanic Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBallastUnderwaterEngineeringMarine engineeringControl theory (sociology)Deflection (physics)Variable (mathematics)SeabedControl systemRemotely operated underwater vehicleControl engineeringComputer scienceControl (management)Mobile robotRobotGeologyMathematics

Abstract

fetched live from OpenAlex

This paper proposes a novel proportional-integral-derivative (PID) control approach that enables variable ballast systems on an autonomous underwater vehicle (AUV) to complement the underwater vehicle's hydroplanes when altitude-keeping over a variable seabed. This control approach is tested on an AUV computer simulator that features a variable ballast system model, which includes the capacity for free flow of seawater into and out of the ballast tank. The new control approach uses hydroplane deflection angle feedback as inputs to the variable ballast system controller in combination with a lowpass filter to ensure that the variable ballast system does not react to high hydroplane deflection rates and unnecessarily deplete the underwater vehicle's onboard energy supply. For the conditions modeled in this research, the resulting simulation case studies showed that this control approach enables the variable ballast system to effectively return control authority back to the hydroplanes while altitude-keeping to a variable seabed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.211
Teacher spread0.200 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations13
Published2017
Admission routes2
Has abstractyes

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