Quality prediction using functional linear regression with in-situ image and functional sensor data
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 article studies a general regression model for a scalar quality response with mixed types of process predictors including process images, functional sensing signals, and scalar process setup attributes. To represent a set of time-dependent process images, a third-order tensor is employed for preserving not only the spatial correlation of pixels within one image but also the temporal dependency among a sequence of images. Although there exist some papers dealing with either tensorial or functional regression, there is little research to thoroughly study a regression model consisting of both tensorial and functional predictors. For simplicity, the presented regression model is called functional linear regression with tensorial and functional predictor (FLR-TFP). The advantage of the presented FLR-TFP model, which is compared to the classical stack-up strategy, is that FLR-TFP can handle both tensorial and functional predictors without destroying the data correlation structure. To estimate an FLR-TFP model, this article presents a new alternating Elastic Net (AEN) estimation algorithm, in which the problem is reformed as three sub-problems by iteratively estimating each group of tensorial, functional, and scalar parameters. To execute the proposed AEN algorithm, a systematic approach is developed to effectively determine the initial running sequence among three sub-problems. The performance of the FLR-TFP model is evaluated using simulations and a real-world case study of friction stir blind riveting process.
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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 | 0.001 | 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 | 0.001 |
| 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