• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2006.11a
• /
• pp.187-192
• /
• 2006

The effect where the multiple sound arrest ing goes mad to the human being does the zone. From like that cotton, this dissertation the both sides flag executed the research regarding a sound arresting reduction in the object in one example. It compared the piping structure which generally is space-time and a specific piping structure and it tested and research and the modeling regarding a sound arresting reduction the simulation which leads and it executed result and comparison of existing it analyzed. The duplication where the reduction effect is bigger the result general VG2 piping structure than escape it did with the fact that it appears the large effect the piping structure which it connects. Also, the straight pipe effect of multiple sound arresting could not go mad with the fact that.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2006.11a
• /
• pp.11-16
• /
• 2006

It appraises that use an indoor noise standard, a NC value which is a noise appraisal, a dB(A) value, a N value in foreign country because it doesn't yet ready an appraisal standard in domestic. Also, It appraises that the supply and drainage noise which could change water supply pressure, $4kg/cm^2,\;3kg/cm^2,\;2kg/cm^2,\;1.5kg/cm^2,\;1kg/cm^2$, bring about a noise and inquires how does noise level indicates according to each instruments. In case of a water supply pressure standard, $3kg/cm^2$, a C-605is $3{\sim}5dB(A)$ lower than another instruments in directly overhead stories. It appears that all of them is similar to level in directly under level except c-407(2)Analyzed the NC value, c-605is the lowest level, NC-50, of a water supply pressure, $4.0kg/cm^2$, c-407 is the highest level, NC-55.(3) In case of N value, which is one of water supply methods in Japan, it is the lowest level, N-55, of a water supply pressure, $4.0kg/cm^2$ same as NC value and C-407is the highest level, N-60.(4) In case of water supply that is likely to noise level, It appears 6dB(A) level gap each instruments, and C-605 is the lowest level, 63.9dB(A).

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.6
• /
• pp.603-616
• /
• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.