OPTIMAL NOISE CONTROL ON PLANT USING SIMULATED ANNEALING
Bibliographic record
Abstract
Noise control is important and essential in a manufacturing factory, where the noise level is restricted by the Occupational Safety and Health Act. Several researches on new techniques of single noise control have been well addressed and developed; however, the study of noise depression on the whole plant noise by using optimum allocation planning is hardly found. An improper machine allocation will not only result in the tremendous cost on noise control task, but also cause the harmful environment for the neighborhood; therefore, the approach of optimum and economic allocation of noise sources within a constrained plant area becomes crucial and obligatory. In this paper, a novel technique of simulated annealing (SA) is applied in the numerical optimization, and the multi-noise plant with various sound monitoring systems is also introduced. Before optimization, the single noise is tested and compared with the simulated data from SoundPlan, a commercial sound simulation package, for the accuracy check of the mathematical model. The result reveals to be within good agreements. The proposed SA optimization on the allocation of multi-noise plant surely provides an economic and effective methodology in reducing the sound accumulation around the plant boundary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".