Kalman Filter-Based Convolutional Neural Network for Robust Tracking of Froth-Middling Interface in a Primary Separation Vessel in Presence of Occlusions
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.
Bibliographic record
Abstract
Bitumen in the oil sands industry is separated from sand using a water-based gravity separation process in a primary separation vessel (PSV). The interface between the froth and the middlings layer is an important parameter to control for optimal operation of the PSV unit. In this paper, a method using computer vision based on convolutional neural networks (CNNs) and Kalman filter (KF) is designed for the detection of the interface in PSV, with the CNN estimating both the interface level and the image quality. The proposed method consists of two parts: an offline and an online stage, wherein the parameters of CNN and KF are trained in the offline stage using the available data. The algorithm is made robust to any new type of occlusions, not present in the training dataset, in the online stage. Experimental results demonstrate that the proposed method is accurate and robust to different abnormalities in the process.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it