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Record W2089408607 · doi:10.1142/s2301385014400123

Depth Control with Moving Weight for a Mini Underwater Vehicle

2014· article· en· W2089408607 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUnmanned Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsDalhousie University
FundersBeijing Municipal Science and Technology CommissionNational Natural Science Foundation of ChinaScience and Technology Commission of Shanghai MunicipalityNatural Science Foundation of Shanghai
KeywordsUnderwaterThrustControl theory (sociology)Controller (irrigation)Mechanism (biology)Displacement (psychology)Computer scienceTorqueMarine engineeringControl (management)EngineeringGeologyPhysicsAerospace engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

A new kind of depth control mechanism (DCM), which applies a moving weight (MW) to adjust its output torque, is designed for a mini underwater vehicle to overcome its problem of insufficient vertical thrust. Motion of the mechanism and underwater vehicle are analyzed. Dynamic terminal sliding mode controller is used to control the displacement of the MW and the depth. Simulations show that the proposed DCM and its controller are effective and the system can reach the predefined depth.

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.

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)0.000
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 score0.925
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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