Polymorphic Measurement Method of FeO Content of Sinter Based on Heterogeneous Features of Infrared Thermal Images
Bibliographic record
Abstract
FeO content of sinter is an important indicator of the quality of sinter. Aiming to overcome the difficulty of detecting the FeO content of sinter in the sintering process in real-time, this paper proposes a polymorphic measurement method for sinter FeO content based on heterogeneous features of infrared thermal images. First, an infrared thermal imager is applied to capture the infrared thermal images of sinter cross section at the tail of the sintering machine, and key frame and region of interest extraction are adopted to reduce the data throughput. Then, the shallow features and deep features that are related to the FeO content are extracted based on the regions of interest. Next, a polymorphic mechanism model is established to obtain the preliminary FeO content, and the sinter quality is divided into three grades according to the preliminary FeO content. Finally, three intelligent models corresponding to the three sinter grades are established to achieve the FeO content prediction based on the extracted heterogeneous features. Results in a sintering plant show that the proposed method can measure the FeO content accurately and provide reliable FeO content data for sintering site.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".