Identifying Localized Noise Sources During Industrial Vehicle Passbys
Bibliographic record
Abstract
Commercial truck pass-by noise is comprised of multiple noise sources that all contribute the whole of the vehicle pass-by noise, including engine, exhaust, tires and trailer-related sources. In order to determine appropriate noise mitigation strategies, it is important to understand how each localized noise source contributes to the whole of the vehicle pass-by noise emission. It is also important to know where these noise sources are located on the vehicle in question should noise mitigation be investigated. Identifying and quantifying the discrete noise sources on a vehicle pass-by can be difficult due to the transient nature of some sources. To locate and quantify these noise sources the Norsonic Nor848, an acoustic camera capable of mapping the spatial distribution of sound, was used to measure several different truck pass-by events. Using the acoustic camera, we were able to observe several loaded and un-loaded commercial trucks passing over flat and uneven road surfaces to determine the location and contribution of each localized noise source. This paper will discuss our measurement processes, findings, and conclusions of our study. C ommercial truck pass-by noise is comprised of multiple noise sources that all contribute the whole of the vehicle pass–by noise , including engine, exhaust, tires and trailer-related sources . In order to determine appropriate noise mitigation strategies, it is important to understand how each localized noise source contributes to the whole of the vehicle passby noise emission. It is also important to know where these noise sources are located on the vehicle in question should noise mitigation be investigated. I dentifying and quantifying the discrete noise sources on a vehicle pass-by can be difficult due to the transient nature of some sources . To locate and quantify these noise sources the Norsonic Nor848, an acoustic camera capable of mapping the spatial distribution of sound, was used to measure several different truck passby events. Using the acoustic camera, we were able to observe several loaded and un-loaded commercial truck s passing over flat and uneven road surfaces to determine the location and contribution of each localized noise source. This paper will discuss our measurement processes, findings, and conclusions of our study.
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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".