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Record W1501598340 · doi:10.1109/icce-tw.2015.7216922

Application of blast furnace falling materials trajectories measurement using laser scan technology

2015· article· en· W1501598340 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBlast furnaceFalling (accident)ScannerMechanical engineeringComputer scienceMaterials scienceEngineeringArtificial intelligenceMetallurgy

Abstract

fetched live from OpenAlex

An innovative method for blast furnace falling materials trajectories measurement using a laser scanner is presented in this paper. The surface profiles of the falling materials delivered from the rotary chute at various titled angle in the filling operation of the blast furnace in China Steel Corporation (CSC) were digitized during the course of burden discharge. Based on the rigid body transformation and the invariant features, 3D image registration algorithm was applied to find out the transformation between the measurement and the blast furnace. Afterwards, the developed data processing method was employed to determine the quadratic curve representing the surface profiles of the falling materials. Consequently, the trajectories standing for the moving paths of the raw materials were evaluated to provide valuable information for preventing blast furnace wall from serious worn-out as well as establishing burden distribution strategies for better productivity and stability.

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: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.328

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 head0.056
GPT teacher head0.248
Teacher spread0.191 · 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