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Record W3009625920 · doi:10.22215/etd/2020-13916

Motion Compensation and Robotic Control of Maritime Cranes

2020· dissertation· en· W3009625920 on OpenAlexaff
Ryan A. McKenzie

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsDeckEngineeringCompensation (psychology)PendulumControl theory (sociology)SimulationSettling timeReduction (mathematics)Marine engineeringComputer scienceControl engineeringControl (management)Structural engineeringArtificial intelligenceMechanical engineeringStep response

Abstract

fetched live from OpenAlex

Maritime operations occur in a rapidly changing and extremely dangerous environment.To improve handling of cargo while at sea, this thesis develops a method for combining active-heave compensation and anti-pendulum control for a combined world-frame compensation system.State estimation algorithms are applied using low-cost inertial sensors attached to the deck of the ship and to the body of the load.The control system is validated with physical experiments on a test-scale motion platform, as well as hardware-in-the-loop test-scale simulations.The results show potential for 49.2-99.5% reduction in settling time, 41.1-98.4% reduction in distance travelled, and 34.6-84.0%reduction in root-mean-squared error for energy dissipation tests; as well as potential improvements in set-point tracking performance compared to uncompensated cases when base excitation is applied.i To my wife, my parents, and all of my family; I could not have done this without you.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2020
Admission routes1
Has abstractyes

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