Orca-RBFNN: A New Machine Learning Method for Control Chart Pattern Recognition
Bibliographic record
Abstract
Supervising the production process in different factories and industries is one of the important and basic measures for the production of high quality goods and is of special importance. This is accomplished by monitoring the behavior of a system. Control chart is one of the most widely used and accurate statistical quality control tools that has been used in recent years in various industries to monitor the production process. In this study, a new method for detecting control chart patterns (CCPs) with the aim of online monitoring of the production process is proposed. In the proposed method, the radial basis function neural network (RBFNN) is used as a classifier of CCPs and a combination of shape and statistical features is used as input. In the proposed method, unlike the conventional methods in the literature, which use a set of shape or statistical features as input, the features are used intelligently and at different steps. In the RBFNN, center of clusters, number of clusters and their spread has a high impact on the network performance. Therefore, their optimal value must be determined correctly. In the proposed method, Orca optimization algorithm (OOA) is used to determine the value of these parameters. The proposed method was tested on a data set containing 800 samples and the simulation results showed that the proposed method is able to identify eight CCPs with 99.41% accuracy.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".