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Record W2057603970 · doi:10.1121/1.4785720

Mass loading effect of shock accelerometers

2005· article· en· W2057603970 on OpenAlexaff
Lixue Wu, George S. K. Wong, Peter Hanes, Won‐Suk Ohm

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Measurement and Uncertainty Evaluation
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAccelerometerOscilloscopeAccelerationSensitivity (control systems)Shock (circulatory)SIGNAL (programming language)CalibrationAcousticsPhysicsMaterials scienceOpticsComputer scienceElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

Mass loading affects the sensitivity of an accelerometer. The mass loading effect can be corrected using mass loading correction curves published by manufacturers. These curves, however, are only applicable to the sinusoidal acceleration below 500 m/s2. The mass loading effect on the sensitivity of an Endevco 2270 accelerometer in shock calibration, from 500 m/s2 and up, was investigated using a laser vibrometer. A new method for conversion of a velocity signal to an acceleration signal was developed. With this method, the sensitivities of the above accelerometer for different mass loads at different shock levels were measured. The mass loading effect in shock calibration for this accelerometer was then obtained. The limitations of the sensitivity measurements were also studied. The variance of the measured sensitivity was mainly due to the resolution limitation (8-bit) of the A/D converter in the digital oscilloscope. A correlation matching algorithm was then developed that utilizes the similarity between the measured acceleration signal and that converted from the velocity signal to further improve the resolution of the digital oscilloscope.

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)0.014
metaresearch head score (Gemma)0.005
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: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.097
GPT teacher head0.371
Teacher spread0.274 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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