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Record W1582602271 · doi:10.4271/2000-01-2688

Study of Occupant-Seat Models for Vibration Comfort Analysis of Automotive Seats

2000· article· en· W1582602271 on OpenAlexaff
Vladimir Tchernychouk, Subhash Rakheja, Ion Stiharu, P.-É. Boileau

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2000
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
Fundersnot available
KeywordsAutomotive industryAutomotive engineeringVibrationCar seatComputer scienceAeronauticsEngineeringAerospace engineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">The comfort assessments of automotive seats are attempted through development of seat-occupant models in order to minimize the participation of human subjects in such studies. A nonlinear model of a polyurethane foam (PUF) cushion and its support mechanism is developed through measurement of static and dynamic properties as functions of the seated load, and excitation frequencies and amplitudes. Nonlinear analytical models of the seat-occupant system are developed by integrating three different occupant models of different complexities with the cushion model. The analytical response characteristics of these models are derived under sinusoidal and random excitations considered representative of the automotive vibration environment. The vibration transmission properties of the seat are measured in the laboratory under harmonic and random excitations using 6 human subjects. The response characteristics of the models are compared with the mean measured response characteristics to examine the validity of occupant models for automotive applications. From the comparison, it is concluded that the reported occupant models yield poor estimates of vibration transmission performance of automotive seats.</div>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.334
Teacher spread0.304 · 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 designObservational
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

Citations15
Published2000
Admission routes1
Has abstractyes

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