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Record W2337787762 · doi:10.1177/1077546315621207

An integrated multivariate empirical mode decomposition method towards modal identification of structures

2015· article· en· W2337787762 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 Vibration and Control · 2015
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsLakehead University
Fundersnot available
KeywordsHilbert–Huang transformModalUnivariateComputer scienceNoise (video)VibrationMultivariate statisticsModal testingMode (computer interface)Modal analysisModal analysis using FEMIdentification (biology)Pattern recognition (psychology)AlgorithmArtificial intelligenceEnergy (signal processing)AcousticsMathematicsMachine learningStatisticsPhysicsMaterials science

Abstract

fetched live from OpenAlex

In this paper, a hybrid empirical mode decomposition (EMD) method is proposed to undertake ambient modal identification of civil structures. Unlike univariate EMD that uses single channel measurement independently, multivariate EMD (MEMD) is employed to estimate the joint information of multichannel vibration measurements of structural systems. The mode mixing in the resulting modal responses from MEMD is then circumvented using ensemble EMD (EEMD). The proposed hybrid MEMD method is validated using a suite of numerical models and experimental studies including the presence of low energy modes, closely-spaced frequencies, measurement noise and reduced sensor densities. The results show the improved performance of the proposed method over the traditional EMD method and reveal the potential of MEMD as a possible candidate for the ambient modal identification method.

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: none
Teacher disagreement score0.704
Threshold uncertainty score0.256

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 head0.033
GPT teacher head0.426
Teacher spread0.394 · 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