• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.527-534
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• 2004

This study is a paper relating to between road traffic noise(RTN) and sound power level(PWL). At present to prediction of RTN is used to many experimental models and prediction methods. RTN is computed PWL using existing experimental models and prediction methods. Then, computed PWL is compared with it of measurement value, in them, it is selected model most similar to measurement value. And then, this results will watch for make Noise Map, as application field applied to computed results.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.48-53
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• 2006

Noise mapping is performed in the city of Cheongju which has 626,614 inhabitants on a $153.41km^2$ area. This city has 71,387 buildings and 969,274m roads. Many database sets like information of roads, topography and buildings are required for making the noise map of large area. These database sets are provided by the various departments of the regional administration in Cheongju city. Using the given database sets, 3-dimensional model of topography and buildings are made to consider the multi-reflections and diffractions. A predicted noise level is compared with measured noise level of the road traffic noise. As the tool of management and decision of urban noise policy, noise map is combined into the map of land use to make the conflict noise map. This conflict noise map is useful to assess the present urban noise and to make the better life in complicated urban life.

• 한국소음진동공학회논문집
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• 제25권4호
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• pp.275-283
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• 2015

The aim of this study is to estimate the noise exposure of roadside apartment complex according to the characteristics of apartment complex. The facade noise level of residential buildings and the ground noise level inside apartment complexes were predicted and analyzed using noise mapping based on a computerized noise model. In addition, the correlation analysis between these noise levels and the characteristics of apartment complex such as traffic volume, building coverage, the number of adjacent roads, etc. was done in a total of 21 apartment complexes. The results showed that building facade noise level and ground noise level were positively correlated with traffic volume (correlation coefficient, r=0.616~0.623) and the number of adjacent roads (r=0.340~0.496). On the other hand, they were negatively correlated with building coverage (r=-0.413~-0.477) and complex area per the number of roads (r=-0.478~-0.615).

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2009년도 추계학술대회 논문집
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• pp.714-715
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• 2009

• 한국의학물리학회지:의학물리
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• 제11권1호
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• pp.19-27
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• 2000

본 연구에서는 평탄한 지면 위의 도로 교통소음의 전파특성을 해석하고자 점음원과 쌍극자음원을 모델로 사용하였으며, 연속 지표면 음향임피던스에 대한 초과감쇠를 실험으로 조사하였다 연구결과 수음점이 지표면 가까이 있을 때, 도로 교통소음의 전파특성은 소음 전파경로 상의 지표면 음향임피던스에 크게 영향을 받는 것을 확인하였다. 또한 진행 차량으로부터 측정한 등가에너지레벨의 거리별 감쇠값을 이용하여 등음압곡선을 해석하였다 그리고 도로 교통소음이 인체에 미치는 영향을 조사하고자 초저주파 소음에 대한 응답특성, 깊은 수면율과 잠을 깨는 비율에 대한 연속적인 교통소음의 .효과, 연령과 소음폭로등 다양한 변수에 대하여 청력손실과 청감민감도 변화 등을 고찰하였다 해석결과 과도한 소음은 일시적 청력손실의 원인이며, 폭로가 길어지거나 강력하면 영구적 청력손실의 원인이 될 수 있음을 확인하였다.

• 한국음향학회지
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• 제37권6호
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• pp.469-474
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• 2018

도시 유동인구가 증가함에 따라 도시 환경 소음에 관한 연구의 중요성이 증가하고 있다. 본 연구에서는 교통상황에서 발생하는 이상 소음을 최근 환경 소음 분류 연구에서 높은 성능을 보이는 딥러닝 알고리즘을 이용하여 분류한다. 구체적으로는 타이어 제동 마찰음, 자동차 충돌음, 자동차 경적음, 정상 소음 네 개의 클래스에 대하여 합성곱 신경망을 이용하여 분류한다. 또한, 실제 교통 상황에서의 환경잡음에 강인한 분류 성능을 갖기 위해 빗소리, 바람 소리, 군중 소리의 세 가지 환경잡음을 설정하였고 이를 활용하여 분류 모델을 설계하였으며 3 dB SNR(Signal to Noise Ratio) 조건에서 88 % 이상의 분류 성능을 가진다. 제시한 교통 소음에 대하여 기존 선행연구 대비 높은 분류 성능을 보이고, 빗소리, 바람 소리, 군중 소리의 세 가지 환경잡음에 강인한 교통 소음 분류 모델을 제안한다.

• 한국소음진동공학회논문집
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• 제12권3호
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• pp.236-242
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• 2002

This paper presents some advanced and supplemental methods to enhance the accuracy In case of calculating geometric divergence attenuation, attenuation by multiple screening structures, ground attenuation at unflat surfaces of sound during propagation outdoors by the methods specified in ISO 9613-2. Moreover, a calculation method for considering short-term wind effect, specified in ASJ Model-1998, is also introduced. To verity the accuracy of adopted methods, we have carried out highway traffic noise prediction and measurement at tile twelve locations appearing representative road shapes and structures, such as flat, retained cut, elevated, barrier-constructed roads. From the results, we have confirmed the predicted results show good correspondence with the measured at direct, diffracted and reflected sound fields within 30 m from the center of near side lane.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.1140-1143
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• 2007

The purpose of this study is the improvement of the prediction model of highway noise. It includes the measurement and analysis of predicted noise levels by various programs in types of road and environments. The results of the measurement are compared with the noise levels predicted by improved highway noise prediction model and domestic prediction models, (Improved highway noise prediction model was considered ASJ-2003, ISO-9613 part2 and noise power of road surface types at Korean highway road.)

• 수산해양기술연구
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• 제51권3호
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• pp.461-473
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• 2015

Jinhae Bay located in the southern of Korean Peninsular is an important spawning area in Korea. By some preliminary studies it was measured several times that adult Pacific codes (Gadus microcephalus) were passed (swimming layer: 15 to 18 m) over a submerged sea tunnel (sea bottom: about 30 m) rather than another immigration route when the Pacific codes were tagged surgically with an acoustic transmitters and released inside of the Bay. There is a possibility that the Pacific codes and the other fishes use the route on the sea tunnel as an immigration route are affected by a human-generated underwater noise around the sea tunnel due to the sea tunnel traffic. On this study the 25-hour measurements of the underwater noise level by water layer were conducted with a hydrophone attached on a portable CTD and an underwater noise level meter during four seasons, and the acoustical characteristics of the underwater noise was analyzed. The mean traffic volume for one hour at the sea tunnel on the spring was shown the largest value of 1,408 [standard deviation (SD): 855] vehicles among four seasons measurement. The next one was ordered on the autumn [1,145 (SD: 764)], winter [947 (SD: 598)] and summer [931 (SD: 558)] vehicles. Small size vehicle was formed 84.3% of the traffic volume, and ultra-small size, medium size, large size and extra-large size of the vehicle were taken possession of 8.7%, 3.2%, 2.0% and 1.8%, respectively. On the daily change of the noise level in vertical during four seasons the noise level of 5 m-layer was shown the highest value of 121.2 (SD: 3.6) dB (re $1{\mu}Pa$), the next one was 10 m-layer [120.7 (SD: 3.5)], 2 m- and 15 m-layer [120.3 (SD: 3.5 to 3.7)] and 1 m-layer [119.2 (SD: 3.6)] dB (re $1{\mu}Pa$). In relation with the seasonal change of the noise level the average noise level measured during autumn was shown the highest value of 123.9 (SD: 2.6) dB (re $1{\mu}Pa$), the next was during summer [121.4 (SD: 3.2)], spring [118.0 (SD: 3.4)] and winter [116.5 (SD: 5.1)] dB (re $1{\mu}Pa$). In results of eigenray computation when the real bathymetry data (complicate shape of sea bed) was applied the average number of eigenray was 2.68 times (eigenrays: 11.03 rays) higher than those of model bathymetry (flat and slightly sloped sea bottom). When the real bathymetric data toward inside (water depth becomes shallow according to a distance between the source of noise and hydrophone) of the Bay was applied on the eigenrays calculation the number of the eigenray was 1.31 times (eigenrays: 12.49 rays) larger than the real bathymetric data toward outside (water depth becomes deep with respect to the distance). But when the model bathymetric data toward inside of the Bay was applied the number of the eigenray was 1.05 times (eigenrays: 4.21 rays) larger than the model bathymetric data toward outside.

• 소음진동
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• 제9권6호
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• pp.1131-1136
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• 1999

NIC@E is the software developed by authors. The program provides the noise level in outdoors due to various noise source types : construction machines including blast sources, railroad vehicles and automobiles. It operates in the Windows system. In this paper, a highway traffic noise has been evaluated using various types of approach : Ray-tracing method, NIRI method, JAS method. In order to compare the noise estimation performance for various models, a measurement is conducted on a 8 lane express highway at the distance of 25 m and 50 m from the lane. The result shows that the ray tracing and JAS model predict the measured value well within 2dB deviaton. The NIRI model, however, underestimates the highway noise level, as the distance between the source and receiver increases.