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Record W3084431180 · doi:10.32393/csme.2020.76

Analysis of an Optical Force Sensor for Haptic Applications

2020· article· en· W3084431180 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

VenueProgress in Canadian Mechanical Engineering. Volume 3 · 2020
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHaptic technologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this project is to evaluate a midpriced 3-axis optical force sensor for low force applications, such as the validation of haptic devices. By applying a range of static loads, the sensor was calibrated, and the hysteresis, repeatability, and non-linearity of the sensor was analysed. The results of the sensor testing were compared to the manufacturer specifications and to the requirements for using the sensor to measure the force output by a haptic device. Custom components for testing the sensor were designed and 3D printed. The sensitivity of the sensor was found to have deteriorated over time. The sensor exhibited significant hysteresis and non-linearity for low forces, though the results with respect to the nominal capacity agreed with the manufacturer specifications. The sensor was determined to have acceptable results in resolution, accuracy, and repeatability for use in the validation of haptic systems. The sensor was shown to retain a reading of 10 % of the loaded force after it was unloaded, which may invalidate the sensor for use in some applications.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.747

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 science0.0000.000
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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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