PCA-WA Based Approach for Concurrent Control Chart Pattern Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Process Control Chart Patterns (SPCC) is a vital task for supervising manufacturing processes. This is done for better control to produce high-quality products. The motivation of this work is to increase the recognition accuracy of concurrent patterns. In this paper, a novel approach is proposed, using neural networks (NN) with Wavelet Analysis (WA) and Principal Component Analysis (PCA) to address the (CCP) recognition problem in concurrent patterns. Eight types of concurrent patterns based on a combination of normal patterns and unnatural patterns are addressed namely; stratification, systematic, increasing trend, decreasing trend, upshift, downshift, and cyclic. Thirteen statistical and shape features are proposed as inputs to the model. The main contribution of this work is the enhancement of the performance of NN through the augmentation of the signal (control chart data) using WA and proposing better extracted statistical features through the use of PCA. Our work shows that improving the original signal and using the right features improves the accuracy of the CCP recognition significantly. The proposed approach has an overall accuracy of 96.3%. The method was compared with four other methods from the previous literature, and it outperformed these methods.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it