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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

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)0.0000.000

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