Perceiving Excitation Characteristics from Interactions between Field Road and Vehicle via Vibration Sensing
Bibliographic record
Abstract
When agricultural vehicles operate in the field, the soft road excitation makes it difficult to measure the vehicle vibration. A camera-accelerator system can solve this issue by utilizing computer vision information; however, the relationship between the field road surface and the vehicle vibration response remains an unsolved problem. This study aims to investigate the correlation of the soft road excitation of different long-wave surfaces with the vehicle vibration response. Vibration equation between the vehicle and soft road surface system was established to produce an effective roughness model of the field soft road surface. In order to simulate the vehicle vibration state under different long-wave road surfaces, the soil rectangular pits with 21 kinds of different spans and depths were applied to the road surfaces, and a tractor vibration test system was built for vibration test. The frequency spectrum analysis was performed for the vibration response and the roughness signals of the road surfaces. The results showed that coefficient (R2) of frequency correlation between the roughness excitation and the original unevenness at the excitation point at the rear end of the rectangular soil pit fell within 0.9641∼0.9969. The main frequency band of the vibration response fell within 0∼3 Hz, and the phenomenon of quadruple frequency existed. The correlation of roughness excitation with quadruple frequency fell within 0.992165∼1. The primary excitation points were located at the rear end of the rectangular soil pit. In addition, it also indicated that when the vehicle was driven without autonomous power, the vehicle vibration frequency mainly depended on the excitation frequency of the field road surface and the frequency at the maximum vehicle vibration intensity was 2 or 3 times of that at the maximum field soft road excitation. These findings may provide a reference for optimal design of vibration reduction and control for agricultural vehicles.
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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 | 0.000 | 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 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".