Stress wave evaluation for predicting the properties of thermally modified wood using neuro-fuzzy and neural network modeling
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
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.
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.
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 | 0.000 | 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 | 0.000 |
| 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