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Record W1964003905 · doi:10.1080/03093640108726589

Failure diagnosis of a type of hydraulic knee joint controller

2001· article· en· W1964003905 on OpenAlexafffund
Chris K. Mechefske, Yue Yang

Bibliographic record

VenueProsthetics and Orthotics International · 2001
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKnee JointController (irrigation)Computer scienceReliability (semiconductor)Joint (building)Hydraulic machineryKnee prosthesisSimulationPhysical medicine and rehabilitationProsthesisMedicineStructural engineeringEngineeringArtificial intelligenceMechanical engineeringSurgery

Abstract

fetched live from OpenAlex

The development of the hydraulic artificial knee joint controller has significantly improved the quality of life for many amputees. Even though the controller unit is very reliable, gradual deterioration inevitably takes place because the unit consists of many moving parts and is subjected to repetitive loads. This paper describes a simple method to test the reliability of hydraulic artificial knee joint controllers. A life cycle testing machine for artificial knee controllers was designed part of this programme. This study included life cycle testing of 5 knee controllers. Performance measurements representing the different degrees of deterioration were recorded and correlated against the actual deterioration found during refurbishment of the units. The results can be used (with care), together with simple tests, to judge performance of a given unit.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.277
Teacher spread0.252 · 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 designBench or experimental
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

Citations5
Published2001
Admission routes2
Has abstractyes

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