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Record W2066289967 · doi:10.1260/0263-0923.33.2.139

Active Vibration Control of Composite Structures Using MicroBlaze™ Soft Core Processor on Virtex-4 FPGA

2014· article· en· W2066289967 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

VenueJournal of low frequency noise, vibration and active control · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsBC Research (Canada)
Fundersnot available
KeywordsReconfigurabilityField-programmable gate arrayMicroBlazeDigital signal processingComputer scienceEmbedded systemSignal processingVirtexComputer hardwareTelecommunications

Abstract

fetched live from OpenAlex

The present work investigates Multi – channel Active Vibration Control (AVC) of a composite research wing model and shell structure using modified Filtered × Least Mean Square (F×LMS) algorithm on Field Programmable Gate Arrays (FPGAs). AVC, using Digital Signal Processing (DSP) techniques are cost effective. But FPGAs, consuming small silicon area, have emerged as a dominant technology in embedded applications with potential features like high speed processing and low power consumption. Prominent features like high-speed parallel processing architecture along with hardware reconfigurability facilitates its usage to be wide spread in signal processing applications. An adaptive active vibration control system based on feed forward modified F×LMS algorithm implemented on FPGA hardware is presented here. The results from the Multi channel real time AV C studies are brought out in the paper.

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 categoriesMeta-epidemiology (narrow)
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 score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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