Robust and constrained tracking of PSV interface using convolutional neural networks and optimistic moving horizon estimation
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
This manuscript proposes a novel video-based robust and constrained estimation framework using the convolutional neural network and optimistic moving horizon estimation, with applications in interface estimation of oil sand primary separation vessels (PSV). Although convolutional neural networks have achieved notable success across various computer vision and image analysis tasks, image outliers (such as blocking, blurriness, and lighting variations) would inevitably affect recognition/tracking performance. To address this issue, this manuscript proposes a robust estimation approach by leveraging a convolutional neural network and moving horizon estimation. Along this line, the interface recognition results by the convolutional neural network can be modeled as the measurements corrupted by disturbances and outliers, and the internal states can be modeled through a discrete-time finite-dimensional state space model. More importantly, the ubiquitously present constraints in the estimation task can be explicitly and readily handled by the moving horizon estimation. The stability analysis of the proposed method is provided in the presence of disturbances and model-plant mismatch. The effectiveness of the proposed method is validated through a pilot-scale laboratory study and an industrial primary separation vessel case study. • A combined CNN and MHE approach for interface estimation is proposed. • The proposed method can explicitly handle physical constraints in the estimation. • The proposed method is robust to camera blocking, blurriness and lighting variations. • The stability is proven in the presence of disturbances and model-plant mismatch. • Laboratory and industrial videos are used to verify the proposed method.
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 | 0.001 |
| 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