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Record W2993778525 · doi:10.3390/proceedings2019032006

Developing a Vibration-Sensing Yarn for Monitoring Hand-Transmitted Vibrations

2019· article· en· W2993778525 on OpenAlexfundno aff
Zahra Rahemtulla, Theodore Hughes‐Riley, Tilak Dias

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilTrent UniversityNottingham Trent University
KeywordsVibrationYarnLimitingEngineeringAcousticsStructural engineeringComputer scienceMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Overexposure to hand-transmitted vibrations (HTVs) is a serious concern within industries that use vibrating power tools as HTVs can lead to severe and chronic injuries to the hand-arm system, which are preventable by limiting vibration exposure. This study presents a novel vibration-sensing electronic yarn (E-yarn) that can be used to monitor HTVs at the point-of-entry of the vibrations into the hand. The construction of the vibration-sensing E-yarn is described, with the vibration-sensing E-yarns being fully characterised at each stage of the manufacturing process to understand how the manufacturing stages affect the behaviour of the embedded sensor. The results show that there is no significant difference in the sensor response at each of the three stages in the E-yarn production over a range of frequencies and amplitudes relevant to HTVs.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.028
GPT teacher head0.293
Teacher spread0.265 · 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 designBench or experimental
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

Citations3
Published2019
Admission routes1
Has abstractyes

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