Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments
Bibliographic record
Abstract
Determining the height of plume clouds is crucial for various applications, including global climate models. Smokestack plume rise refers to the altitude at which the plume cloud travels downwind until its momentum dissipates and the temperatures of the plume cloud and its surroundings become equal. While most air-quality models employ different parameterizations to forecast plume rise, they have not been effective in accurately estimating it. This paper introduces a novel framework that utilizes Deep Convolutional Neural Networks (DCNNs) to monitor smokestack plume clouds and make real-time, long-term measurements of plume rise. The framework comprises three stages. In the first stage, the plume cloud is identified using an enhanced Mask R-CNN, known as the Deep Plume Rise Network (DPRNet). Next, image processing analysis and least squares theory are applied to determine the plume cloud’s boundaries and fit an asymptotic model to its centerlines. The z-coordinate of the critical point of this model represents the plume rise. Finally, a geometric transformation phase converts image measurements into real-world values. This study’s findings indicate that the DPRNet outperforms conventional smoke border detection and recognition networks. In quantitative terms, the proposed approach yielded a 22% enhancement in the F1 score, compared to its closest competitor, DeepLabv3.
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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) | 0.001 | 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 | 0.001 | 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".