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Record W2338830536 · doi:10.1177/1077546315616522

Time-delay control of a switchable stiffness system

2015· article· en· W2338830536 on OpenAlexafffund
Kefu Liu

Bibliographic record

VenueJournal of Vibration and Control · 2015
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStiffnessControl theory (sociology)Oscillation (cell signaling)VibrationControl (management)System dynamicsComputer scienceVibration controlEngineeringPhysicsStructural engineeringAcoustics

Abstract

fetched live from OpenAlex

This paper focuses on suppression of free vibration of single degree-of-freedom systems that possess time delay. The switchable stiffness (SS) control strategy is reviewed. The implication of time delay is examined. It shows that the system delay can cause malfunction of the direct SS control. To overcome this problem, the two time-delay control strategies are proposed. The first strategy named as half period delay SS control introduces an intentional delay such that the switch action takes place in a half of oscillation period later. The second strategy named as quarter period delay SS control is to add an intentional delay such that the switch action occurs in a quarter of oscillation period later. In this case, the SS control law is inverted. An apparatus consisting of an electromagnetic (EM) spring is developed to validate the proposed strategies. The stiffness models of the system are established. In computer simulation, three cases have been examined. In case A, the system is simplified as linear and the dynamics of the EMs is neglected. In case B, the stiffness models are used and the dynamics of the EMs is neglected. In case C, the stiffness models are used and the dynamics of the EMs is considered. An experimental study is conducted in real time. The results have validated the observations obtained from the computer simulations.

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 score0.898
Threshold uncertainty score0.312

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 head0.189
Teacher spread0.182 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

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