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Record W3104283120 · doi:10.1115/detc2001/vib-21482

Adaptive Feedforward Active Noise Control in an Acoustic Duct

2001· article· en· W3104283120 on OpenAlexaff
Abdolreza Ohadi, Ebrahim Esmailzadeh, Aria Alasty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFeed forwardActive noise controlControl theory (sociology)Computer scienceTransfer functionMinimaxNoise (video)Channel (broadcasting)EngineeringControl engineeringControl (management)MathematicsMathematical optimizationTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The single-reference/multi-output active noise control (ANC) of an accurate physical model of an acoustic duct system has been investigated. Computer model of a multi-channel ANC system with tonal and sweep sine input signals, and an adaptive feedforward algorithm that minimizes the generic cost function are developed. Results obtained for various single-input/single-output (SISO) configurations of ANC systems were compared. The dynamic response of a single-reference/multi-output ANC system, using Minimax and MEFXLMS algorithms, is studied and the effect of acoustical feedback neutralization in a multichannel ANC system is studied. Simulation results demonstrate that the multi-channel adaptive feedforward ANC system, using the Minimax algorithm, has a superior performance in comparison to the same system with MEFXLMS.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 science0.0000.000
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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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

Citations0
Published2001
Admission routes1
Has abstractyes

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