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Design of a Traffic Detection System Based on Laser and Piezoelectric Technologies

2012· article· en· W1983438985 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

VenueApplied Mechanics and Materials · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Detection Methods
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsAxleSoftwarePiezoelectric sensorLaser scanningLaserDetectorEngineeringVolume (thermodynamics)Data acquisitionPiezoelectricitySystem of measurementData processingAutomotive engineeringReal-time computingElectronic engineeringComputer scienceElectrical engineeringMechanical engineeringOptics

Abstract

fetched live from OpenAlex

This paper aimed to solve the problems that traffic detector which was used in china, can't satisfies the multifunction and high-accuracy needs of the domestic traffic survey and traffic volume measurement.We designed a traffic detection system based on laser and piezoelectric technologies,which is made up of laser speed sensor, laser scanning sensor, piezoelectric axle shaft sensor, data acquisition unit, software system and server, and their application was discussed in detail. The working principles of laser speed sensor, laser scanning sensor, piezoelectric axle shaft sensor were presented, and the detection parameters of the system were put forward. The whole structure and data processing flow of software system were also pointed out. Through test verification, the multifunction and high-accuracy characteristics of the system were verified. This traffic detection system has great significance for enhancing the research level of the domestic traffic survey and traffic volume measurement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.210
Teacher spread0.192 · 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