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Record W4252074028 · doi:10.32920/ryerson.14644842

Cuff Dynamics System Analysis and Modeling

2021· preprint· en· W4252074028 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

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCuffSystem dynamicsDynamics (music)Computer scienceRepresentation (politics)Nonlinear systemControl theory (sociology)LinearityPoint (geometry)Linear modelArtificial neural networkMathematicsEngineeringArtificial intelligenceElectronic engineeringPhysicsControl (management)Acoustics

Abstract

fetched live from OpenAlex

This thesis investigates the properties of the cuff dynamics system. The approach used has been to first build up a linear model for the cuff dynamics system. Analysis of the results shows that the linear model can only hold over a very small operating range, the conclusion is drawn that the cuff dynamics system exhibits strong non-linearity. An artificial neural network then is proposed to model the non-linear cuff dynamics system. Mathematical analysis of the results shows that the model structure provides a better representation of the system dynamics. Two experiments are designed to capture the non-linearity of the cuff dynamics system using a NNARX neural network model. The single operating point of cuff dynamics approximation and the multiple operating point cuff dynamics approximation. A second order with one zero model is chosen as the best representation. The result of the simulations shows that it is not appropriate to use the cuff as sensor in the blood pressure measurement without considering the behaviour of the cuff. The cuff dynamics shows strongly non-linear properties, which contribute a lot to the whole blood pressure measurement.

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 score0.849
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.036
GPT teacher head0.247
Teacher spread0.211 · 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