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Record W2046659232 · doi:10.1504/ijvd.2012.045926

Vibration control for active seat suspension system based on projective chaos synchronisation

2012· article· en· W2046659232 on OpenAlexaff
Guilin Wen, Shengji Yao, Zhiyong Zhang, Hanfeng Yin, Zhong Chen, Huidong Xu, Chuanshuai Ma

Bibliographic record

VenueInternational Journal of Vehicle Design · 2012
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of New Brunswick
FundersBijzonder Onderzoeksfonds UGentNational Natural Science Foundation of China
KeywordsVibrationVibration isolationEngineeringRobustness (evolution)ChaoticAccelerationControl theory (sociology)Vibration controlSuspension (topology)Computer scienceAcousticsControl (management)PhysicsMathematics

Abstract

fetched live from OpenAlex

A control method based on Projective Chaos Synchronisation (PCS) is developed for design of active seat suspension system. The chaotic vibration signals with broadband frequency are used to reduce the Power Spectrum Density (PSD) of the driver’s acceleration in the human-body sensitive range. The scaling factor of PCS enables us to proportionally diminish the vibration amplitudes of the driver seat to the degree as we desire. Detailed analyses of this method show that the control method could improve the vibration-isolation performance effectively, and the control force applied to the seat suspension could be constrained to a relatively low level. In addition, the robustness of the control method is addressed.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.020
GPT teacher head0.245
Teacher spread0.225 · 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
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

Citations9
Published2012
Admission routes1
Has abstractyes

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