Process operation performance assessment of electro‐fused magnesium furnace based on deep auto‐encoder transfer generative adversarial network
Bibliographic record
Abstract
Abstract The process operation performance assessment (POPA) of the smelting process of electro‐fused magnesium furnaces (EFMFs) plays a very important role for improving production quality and pursuing the highest economic benefit. Most of the existing methods are based on the assumption of abundant labelled training samples. However, the lack of samples for the POPA is a challenging problem in the smelting process of EFMFs. Traditional methods for POPA make it difficult to obtain satisfactory results under small samples. A new deep auto‐encoder transfer generative adversarial network (DAETGAN) based on source domain data is proposed to improve the performance of POPA with small samples in EFMF smelting process. Firstly, a general transfer framework is proposed, in which the data of source domain is used as input to generate adversarial network (GAN), and a large number of images, sound, and current samples are generated with the same distribution as the data of target domain so as to improve the transfer learning effect. Secondly, a POPA model is proposed with the multi‐source heterogeneous information generated by DAETGAN and the original multi‐source heterogeneous information in target domain. Finally, it is verified by experiments that the DAETGAN model can generate data with the same distribution as the original data, and the accuracy of the assessment of operational performance is effectively improved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".