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Record W1993529996 · doi:10.1109/icinfa.2014.6932808

Active and passive measures to reduce the noise pollution of combustion engines

2014· article· en· W1993529996 on OpenAlexaff
Ulrich Gabbert, Fabian Duvigneau, Jinjun Shan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsYork University
Fundersnot available
KeywordsAutomotive engineeringNoise (video)Noise controlActive noise controlAttenuationActuatorCombustionComputer scienceNoise pollutionEnvironmental scienceAcousticsEngineeringNoise reductionElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

In recent years comfort has become an important factor when evaluating the performance of modern automobiles. One important aspect that has negative ramifications on the perception of the quality is the generated noise, which mainly contributes to the disturbing noise level of urban regions. Therefore, an important goal in current research is the attenuation of the noise level of car engines. The paper at hand presents two main approaches, an active and a passive one, to reduce the noise radiation of combustion engines, which is the main noise source of automobiles. In the active approach thin piezoelectric wafers are attached to the structure as sensors and actuators. With an appropriate controller the structural vibrations are reduced, which result in an attenuation of the sound pressure in the environment. The passive approach utilizes a full engine encapsulation, which is also designed to reduce the sound radiation of the engine. By encapsulating the motor the oil temperature can be increased such resulting in decreased fuel consumptions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.209
Teacher spread0.201 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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