Stress wave evaluation for predicting the properties of thermally modified wood using neuro-fuzzy and neural network modeling
Bibliographic record
Abstract
Abstract This study investigated using the stress wave method to predict the properties of thermally modified wood by means of an adaptive neuro-fuzzy inference system (ANFIS) and neural network (NN) modeling. The stress wave was detected using a pair of accelerometers and an acoustic emission (AE) sensor, and the effect of heat treatment (HT) on the physical and mechanical properties of wood as well as wave velocity and AE signal is discussed. The AE signal was processed in the time and time-frequency domains using wavelet analysis and different features were extracted for network training. The auto-associative NN is used as a dimensional reduction method to decrease the dimension of the extracted AE features and enhance the ANFIS performance. It was shown that while the stress wave velocity using the accelerometer did not result in an accurate model, the network performance significantly increased when trained with the AE features. The AE signal exhibited a significant correlation with wood treatment and porosity. The best ANFIS performance corresponded to predicting the wood swelling coefficient, equilibrium moisture content (EMC) and water absorption (WA), respectively. However, the AE signal did not seem suitable for predicting the wood density and hardness. The performance of ANFIS was compared with the “group method of data handling” (GMDH) NN. Both the ANFIS and GMDH networks showed higher accuracy than the multivariate linear regression (MVLR) model.
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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 | 0.000 | 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) | 0.001 | 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 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".