MétaCan
Menu
Back to cohort
Record W2363758887

Development of Stress-Strain Measurement Instrument for Trencher Guide Bar

2013· article· en· W2363758887 on OpenAlexaff
Ding Zhong-ju

Bibliographic record

VenueJournal of Qingdao University of Science and Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Control Systems
Canadian institutionsCentre For Cold Ocean Resources Engineering
Fundersnot available
KeywordsStrain gaugeBar (unit)SoftwareDifferential amplifierInstrumentation (computer programming)AmplifierStress (linguistics)Instrumentation amplifierMeasuring principleMeasuring instrumentEngineeringElectronic engineeringComputer scienceElectrical engineeringOpticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

In order to ensure the safe operation of trencher along the seabed pipeline,the stress-strain measuring instrument used in the trencher guide bar was developed.According to the characteristics of the guide bar,the high precision strain measurement circuit and special measurement and control software with the function of differential input and two stage amplifier-filtering were designed,and the real-time monitoring of guide bar stress-strain was realized.In the aspect of hardware design,the detection principle of the electric resistance strain gauge measuring method was analyzed,the weak signal differential input was realized by high precision instrumentation amplifier AD620and the design method of two second-order active filter amplifier circuit was introduced.In the aspect of software design,the measurement and control software of stress-strain instrument was developed based on virtual instrument software LabVIEW.Experimental test results show that this stress-strain instrument can meet the demand of high precision design requirements.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.193
Teacher spread0.176 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Qingdao University of Science and TechnologySame topicAdvanced Sensor and Control SystemsFrench-language works237,207