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Record W2083330600 · doi:10.1177/1077546305055772

Development and Application of an Active Control System for Smart Structures Using the Wave Method

2005· article· en· W2083330600 on OpenAlexaff
Aghil Yousefi‐Koma, George Vukovich

Bibliographic record

VenueJournal of Vibration and Control · 2005
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsController (irrigation)Control theory (sociology)Parametric statisticsActuatorControl engineeringVibration controlEngineeringControl systemOpen-loop controllerSimple (philosophy)VibrationComputer scienceControl (management)MathematicsAcousticsPhysics

Abstract

fetched live from OpenAlex

A flexible structure with bonded piezoelectric sensors and actuators is considered as a smart structure. In this paper we introduce a new control criterion based on the absorption of the traveling waves of the sensing area. Unlike conventional wave controllers (local wave controllers), this controller not only employs the local system parameters but also uses global system parameters, such as the total length of the flexible structure and the absolute positions of the piezotransducers. Including the global parameters of the system in the wave control design increases the control performance significantly over mainstream wave controllers. A simple parameter based design of the global wave controller is then developed, which can be easily implemented in actual practice. The controller gains depend only on the mechanical parameters of the system and can be obtained from a simple parametric formula. The performance of the controller is compared with that of the conventional controller, and it is seen that this performance is as good as that of optimal controllers with this very simply designed wave controller. Experimental results match closely with simulation and illustrate that the controller suppresses the flexural vibrations quickly and efficiently.

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 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: none
Teacher disagreement score0.855
Threshold uncertainty score0.247

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2005
Admission routes1
Has abstractyes

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