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Record W2966284565 · doi:10.11159/htff19.122

Innovative Solution for Harvesting Energy in Marine Vessels

2019· article· en· W2966284565 on OpenAlexvenueno aff
George Nerubenko, Dmytriy Gurevych

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Launch and Propulsion Technology
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy harvestingComputer scienceEnergy (signal processing)Marine engineeringEnvironmental scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

The marine vessels are subjected to various aggressive dynamic loads.Mainly they cause the roll, pitch, heave and yaw of marine vessels.One of the recently invented effective tools for suppressing the negative impact of these loads is the energy harvesting devices.The proposed energy harvester consists of two main components: tuned mass damper and electricity generator.The innovative part of proposed energy harvester is the introduction of control system into structure of tuned mass damper providing the automatic adjustment of springstiffness coefficient in accordance to the current frequency of external force.Such energy harvesting devices are targeting two issues: reduction of impacts of dynamic loads acting on vessel and obtaining additional portion of electrical energy for free.The light boat is selected as an example for demonstration of all steps of energy harvesting technology implementation into marine practice.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
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 communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.004
GPT teacher head0.180
Teacher spread0.175 · 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 designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueProceedings of the World Congress on Mechanical, Chemical, and Material EngineeringSame topicElectromagnetic Launch and Propulsion TechnologyFrench-language works237,207