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Record W3211784323 · doi:10.1115/imece2000-1212

An Optimum Design Approach Using Active and Semi-Active Suspensions for 3-D Vehicle Models

2000· article· en· W3211784323 on OpenAlexaff
Mohamed Bouazara, Marc J. Richard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsUniversité LavalUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsActive suspensionSuspension (topology)Position (finance)Stability (learning theory)VibrationComputer scienceControl theory (sociology)Vehicle dynamicsCar modelAutomotive engineeringEngineeringSimulationMathematicsArtificial intelligencePhysicsMachine learning

Abstract

fetched live from OpenAlex

Abstract The primary purpose of this paper is to analyze the effects of vibrations on the comfort and stability of road vehicles as observed in the variation of different parameters such as suspension coefficients, road disturbances and the seat position. The conducting of this study required the development of a mathematical model to simulate the dynamic behavior of a 3-D vehicle. This model makes it possible to efficiently use various types of non-linear suspensions such as active and semi-active suspensions. The results obtained from the simulation of the 3-D vehicle demonstrated that the use of active and semi-active suspension models on road vehicles proved to be beneficial for comfort without unduly compromising stability.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.106
GPT teacher head0.367
Teacher spread0.261 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2000
Admission routes1
Has abstractyes

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