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Record W2725401892 · doi:10.4050/f-0073-2017-12280

Evaluation of Aircrew Noise Exposure and Hearing Protection Solutions in CH-147F Chinook Helicopter Cabin

2017· article· en· W2725401892 on OpenAlexaffabout
Yong Chen, Sebastian Ghinet, Andrew Price, Viresh Wickramasinghe, Anant Grewal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAircrewAeronauticsNoise (video)Chinook windEnvironmental scienceEngineeringAudiologyAcousticsMarine engineeringAutomotive engineeringComputer scienceMedicinePhysicsFisheryBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

High noise levels in the helicopter cabin affect aircrew communication and reduce aircrew comfort, and may lead to hearing loss in the long-term if flight helmets cannot provide sufficient protection to the aircrew, or are improperly fitted. A cabin noise exposure survey has been performed on a Royal Canadian Air Force (RCAF) CH-147F Chinook heavy lift helicopter to evaluate the noise environment. The insertion loss performance of the flight helmet was characterized in a reverberant acoustic chamber facility. Investigation results showed that the low frequency noise attenuation provided by the RCAF flight helmet was marginal at high speed flight conditions in which significant cabin noise levels existed in the helicopter cabin. To enhance the low frequency hearing protection performance and clarity in voice communication, in-canal Communication Ear Plugs (CEPs) integrated with active noise cancellation (ANC) capability was investigated. Simulation and proof-of-concept test results demonstrated that the ANC in-canal CEPs can serve as a feasible technical solution to provide enhanced noise attenuation to mitigate the low frequency N/rev tonal noise generated by the aerodynamic pressure from the helicopter rotor blades.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.056
GPT teacher head0.269
Teacher spread0.212 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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