Automatic Material Classification via Proprioceptive Sensing and Wavelet Analysis During Excavation
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an excavation material classification methodology that uses wavelet analysis and unsupervised learning on acceleration measurements. The technique was validated by using acceleration data that were acquired from three inertial measurement units (IMUs) on an instrumented 1-tonne capacity wheel loader. One IMU was installed on the loader’s boom and two on the bucket. The acceleration signals were logged for 32 manual excavation trials in three excavation materials with different rock size distributions, and the data were processed offline. The continuous wavelet transform was applied to the acceleration signals to extract features from the acceleration signals. The results show that classifying the wavelet feature set using an unsupervised k-means algorithm provides an average material classification accuracy of 81 %, when attempting to simultaneously classify all three materials.
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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 | 0.000 | 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