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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 OpenAlexaff
Shashikala Prakash, C. Karthikeyan, T.G. Renjith Kumar, Prabhu Radhakrishna, P. Ravikiran

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.

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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0030.000

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

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

Citations6
Published2014
Admission routes1
Has abstractyes

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Same venueJournal of low frequency noise, vibration and active controlSame topicAdvanced Adaptive Filtering TechniquesFrench-language works237,207