Enhancing Ship Coating Quality Detection via Machine Learning-Optimized Visible Near-Infrared Spectroscopy
Bibliographic record
Abstract
This study proposes a portable Visible/Near-Infrared (Vis/NIR) spectroscopy-based approach to detect and evaluate the quality of ship coatings.Vis/NIR spectroscopy offers an accurate, non-destructive method for identifying coating conditions through spectral data acquisition, combined with machine learning analysis to improve detection performance.In this study, using a device with a wavelength of 410-940 nm, spectral transformations such as scatter correction, baseline correction, smoothing, and derivative were applied to improve data quality, followed by feature selection using PCA and IFS.SVM, Random Forest (RF), and LDA classification algorithms were then used to model spectral data.The coating quality consists of four classes, with 40 samples for each.The initial results of modeling without treatment were improved with an average accuracy of 83.90%.Then, applying the combination of Nippy and IFS significantly increases average accuracy results by 96.86%.Incorporating spectral transformation and feature selection methods can optimally utilize spectral information and improve the model's overall performance with an increase in accuracy of 12.96%.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".