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Record W1967624595 · doi:10.1109/isie.2012.6237134

Regenerative vibration damping of a suspension system testbed

2012· article· en· W1967624595 on OpenAlexaff
Reza Sabzehgar, Mehrdad Moallem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSuspension (topology)DamperVibrationBattery (electricity)Linear congruential generatorTestbedPower (physics)Shock absorberSprung massMaximum power transfer theoremPulse-width modulationGenerator (circuit theory)EngineeringAutomotive engineeringControl theory (sociology)Computer scienceElectrical engineeringVoltagePhysicsMechanical engineeringControl engineeringAcousticsMagnet

Abstract

fetched live from OpenAlex

In this paper, an energy-regenerative suspension system is implemented consisting of a linear DC machine, power electronic circuity, and a battery. The linear DC machine operates as a generator, converting the vibration energy of a mass-spring system into battery charge. The linear DC machine generates desired forces acting between the unsprung and sprung masses of the suspension system; hence acting as a damper that transforms vibrations into electrical charge stored in a battery. A pulse-width-modulated (PWM) power electronic converter is utilized to achieve the transfer of power to the battery. The proposed method is applied to a quarter car test rig to evaluate performance of the regenerative suspension system.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.199
Teacher spread0.186 · 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 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

Citations4
Published2012
Admission routes1
Has abstractyes

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