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Record W3214068123 · doi:10.1115/imece2001/dsc-24557

Active Noise Cancellation Using Feedforward and Hybrid Controls

2001· article· en· W3214068123 on OpenAlexaff
Abdolreza Ohadi, Ebrahim Esmailzadeh, Aria Alasty

Bibliographic record

VenueDynamic Systems and Control · 2001
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFeed forwardActive noise controlLoudspeakerControl theory (sociology)Noise (video)Transfer functionComputer scienceLeast mean squares filterFilter (signal processing)Adaptive filterAlgorithmAcousticsControl (management)EngineeringControl engineeringArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Abstract The exact closed-form solution of a one-dimensional wave equation including the viscous damping effect has been obtained from the Green’s function. Accurate models for the error sensor and secondary loudspeaker, which includes the electro-mechanical and mechano-acoustical couplings, have been used and the transfer function of the primary, secondary and acoustic feedback paths of the active noise control system have been obtained. The generalized form of the classical FXLMS algorithm, referred to G-FXLMS algorithm, has been developed. In contrast to the FXLMS algorithm, G-FXLMS algorithm does not neglect the time shift of the filter coefficients and employs a more general recursive adaptive weight update equation, which can improve the performance of FXLMS algorithm. Simulation results presented to compare the performance of the feedforward and hybrid ANC systems and to study the effect of acoustical feedbacks and boundary conditions on the overall performance of ANC systems.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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
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
Published2001
Admission routes1
Has abstractyes

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