Improving Measurement Accuracy of Acoustic Intensity in Vibrating Machines Using a Doosan Robot
Bibliographic record
Abstract
Measuring sound intensity is a complex and tedious task in industrial environments. Traditional manual methods can be costly, inaccurate, and potentially dangerous for workers. Integrating robotics and computer vision into acoustic measurement processes offers an innovative solution to these problems. This article provides an overview of the innovative solutions offered by Doosan Robotics, in collaboration with OnRobot, an integrated solution for automatic acoustic intensity measurement. The Doosan robot equipped with a scanning camera captures acoustic data and produces a map of the machine’s acoustic radiation using computer vision algorithms. This measurement method is accurate, efficient, and safe for workers. It can also be used for continuous monitoring of the industrial acoustic environment. With these innovations, the Doosan robot offers a promising way to automate acoustic intensity measurement, which can be performed faster and with better accuracy to help industries improve sound quality and reduce noise levels to improve worker health and well-being.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".