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Record W1967963155 · doi:10.1115/1.4023058

Minimal Spatial Accelerometer Configurations

2013· article· en· W1967963155 on OpenAlexaff
Thomas R. Williams, Donald W. Raboud, K.R. Fyfe

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2013
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAccelerometerAngular accelerationAccelerationAngular velocityNonlinear systemPoint (geometry)MathematicsClass (philosophy)Motion (physics)Rigid bodyMathematical analysisControl theory (sociology)Computer scienceGeometryPhysicsClassical mechanicsComputer visionArtificial intelligence

Abstract

fetched live from OpenAlex

It is well established that it is necessary to use a minimum of six accelerometers to determine the general motion of a rigid body. Using this minimum number of accelerometers generally requires that a nonlinear differential equation be solved for the angular velocity and that the estimate of angular velocity that is obtained from the solution of this equation be used in the calculation of the specific force at a point. This paper serves two main purposes. First it discusses, for the first time, the geometric conditions that must be satisfied by an arrangement of six accelerometers so that it is possible, in principle, to determine the motion of the body to which they are attached. Second, a special class of minimal accelerometer configurations that yields angular acceleration as a linear combination of accelerometer measurements is identified, and a design methodology for this special class is presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.010
GPT teacher head0.180
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), 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
Published2013
Admission routes1
Has abstractyes

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