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Record W3172749061

PROJETO DE UM SENSOR DE TORQUE SEM FIO PARA EIXOS GIRANTES

2019· article· pt· W3172749061 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

VenueCongresso Brasileiro de Automática - CBA · 2019
Typearticle
Languagept
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPhysicsHumanitiesArduinoStrain gaugeTorqueElectrical engineeringOperating systemComputer scienceArtEngineering
DOInot available

Abstract

fetched live from OpenAlex

Este artigo descreve a implementacao de um sensor de torque sem fio de baixo custo para aplicacoes em eixos giran-tes. O instrumento e capaz de medir o torque em maquinas rotativas, enviando, sem fio, o sinal relacionado a grandeza para um dispositivo Android, que funciona como sistema de aquisicao de dados. O conceito do projeto e baseado em medicoes de tensao utilizando extensometros resistivos (strain gauges). O projeto inclui um conjunto de straing gauges 350 Ω em ponte completa, um kit Arduino UNO, um adaptador SD para Arduino, um modulo Bluetooth e um conversor analogico-digital (ADC) de 24 bits. A implementacao consiste em instalar os strain gauges, programar o Arduino e fabricar o circuito ADC. Apos essas etapas, o sensor e testado em um braco de alavanca. Os resultados mostram que o sistema pode realizar medicoes de torque com uma resolucao de 0,0138 N·m, exibindo, em tempo real, a curva de torque em uma tela de smartphone/tablet. Assim, o sensor de torque apresentado representa um instrumento versatil, preciso e de baixo custo para auxiliar na avaliacao de desempenho e em projetos de maquinas girantes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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