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Record W2782304773 · doi:10.2514/6.2018-2215

Development of a Multi-axis Active Seat Mount to Mitigate Vibration Transmission to Helicopter Aircrew

2018· article· en· W2782304773 on OpenAlexaff
Firdous Hadj-Moussa, Amin Fereidooni, Yong Chen, Viresh Wickramasinghe

Bibliographic record

Venue2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference · 2018
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsNational Research Council Canada
FundersFonds National de la Recherche Luxembourg
KeywordsAircrewMountVibrationAerospace engineeringAutomotive engineeringAeronauticsTransmission (telecommunications)Computer scienceEngineeringElectrical engineeringAcousticsPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents the performance evaluation of a multi-axis active helicopter seat mount. By simultaneously counteracting vibrations in three orthogonal directions, the mount mitigates the transmission of helicopter vibrations from the floor to the seat before reaching to the human body. This design is tested independently in each direction and simultaneously in all three directions using shaker tables. The input vibration prole used in these tests are helicopter flight measurements collected on a Bell-412 research helicopter. To cancel the input vibration, the feedforward Filtered-x Least Mean Square (FxLMS) algorithm is employed as the adaptive control law. Signicant suppression of the harmonic components of the N/rev harmonics is achieved for both independent and simultaneous testing. In each case, the overall g-rms reduction level is more than 40 % along each axis. The experimental testing veried the effectiveness of the active seat mount as a means to attenuate multi-axis vibrations experienced by helicopter aircrew.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.221
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
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)0.0010.000

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 head0.024
GPT teacher head0.311
Teacher spread0.287 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venue2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials ConferenceSame topicEffects of Vibration on HealthFrench-language works237,207