A Granular Computing-Based Hybrid Hierarchical Method for Construction of Long-Term Prediction Intervals for Gaseous System of Steel Industry
Bibliographic record
Abstract
Byproduct gaseous energy is crucial to the iron-steel manufacturing process, where the tendencies of its generation and consumption can be deemed as a significant reference for scheduling production and decision-making. Besides the requirements imposed on numeric prediction, practical applications also demand that the result be represented in terms of intervals expressing the reliability of prediction outcomes. Meanwhile, prediction intervals should cover a long period of time for delivering more information on future long-term trends. Bearing this in mind, in this study, a Granular Computing-based hybrid hierarchical method is proposed for constructing long-term Prediction Intervals (PIs), in which the horizontal modelling gives rise to long periods of prediction, and the vertical one extends them to the interval-valued format. Information granules are hierarchically distributed over single data and then on industrial features-based segments. Considering the criteria of coverage and specificity as sound performance indexes of the model, a suite of optimization problems is formulated and solved by involving Particle Swarm Optimization (PSO). Experimental studies demonstrate that the proposed approach exhibits better performance when compared with the performance reported for other commonly encountered methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".