In this study, Target strength for multi-frequency (38 kHz, 70 kHz, 120 kHz and 200 kHz) of chub mackerel (Scomber japonicus) was estimated using by the KRM model. The body shape of the Chub mackerel was described by an X-ray system and the body length of 20 individuals ranged from 16 cm to 28 cm. The swimbladder tilt angle ranged between – 8 and – 14°, the maximum TS value according to the swimming angle of chub mackerel was – 33.0 dB at – 11°. The averaged TScm according to fork length was – 66.02 dB at 38 kHz, – 66.50 dB at 70 kHz, – 66.00 dB at 120 kHz and – 67.35 dB at 200kHz, respectively.

In this study, the well-known non-destructive acoustic emission (AE) and electrical resistivity methods were employed to predict quantitative damage in the silo structure of the Wolsong Low and Intermediate Level Radioactive Waste Disposal Center (WLDC), Gyeongju, South Korea. Brazilian tensile test was conducted with a fully saturated specimen with a composition identical to that of the WLDC silo concrete. Bi-axial strain gauges, AE sensors, and electrodes were attached to the surface of the specimen to monitor changes. Both the AE hit and electrical resistance values helped in the anticipation of imminent specimen failure, which was further confirmed using a strain gauge. The quantitative damage (or damage variable) was defined according to the AE hits and electrical resistance and analyzed with stress ratio variations. Approximately 75% of the damage occurred when the stress ratio exceeded 0.5. Quantitative damage from AE hits and electrical resistance showed a good correlation (R = 0.988, RMSE = 0.044). This implies that AE and electrical resistivity can be complementarily used for damage assessment of the structure. In future, damage to dry and heated specimens will be examined using AE hits and electrical resistance, and the results will be compared with those from this study.

A Catholic school is a school that spreads religious ideology to students by combining religious education with regular school curriculum. But, most Catholic schools do not have a separate liturgical space, mass is performed in multipurpose spaces such as auditoriums, and are exposed to severe acoustic defects due to the very aging facilities and lack of sound performance suitable for the liturgical space. So, in this study, an improvement plan was proposed using sound simulation after identifying acoustic defects through field measurements in the liturgical space of Haeseong High School in Jeonju. Also, hearing experiment using Acoustic Psychological Analysis was conducted to identify changes in subjective responses felt by students as they improved their acoustic performance. The results of study, before and after sound performance improvement sound pressure level (SPL500Hz) was 64.8dB at 66.7dB, the weighted sound level (SPLdB(A)) was 67.9dB(A) at 75.8dB(A), reverberation time (RT500Hz) was 2.04sec at 2.52sec, definition (D50,500Hz) was 40.2% at 33.5%, clarity (C80.500Hz) was –0.8dB at –1.18dB, and RASTI was 57.7% at 49.2% was evaluated. In addition, the amount of improvement in subjective responses before and after sound performance through psycho-acoustic experiment decreased by “Reverberation” -17.0%, “Loudness” -11.3% and increased by “Clear” 21.3%, “Vivid” 21.3%, “Intimacy” 12.4%, “Warmth” 14.3%, “Stability” 19.3%, “Godliness” 18.8%. Therefore, after improving sound performance, Haesung High School's liturgical space improved satisfaction with subjective sound performance felt by students and teachers to ensure sound performance suitable for the liturgical space.

Evaluating the quantitative damage to rocks through acoustic emission (AE) has become a research focus. Most studies mainly used one or two AE parameters to evaluate the degree of damage, but several AE parameters have been rarely used. In this study, several data-driven models were employed to reflect the combined features of AE parameters. Through uniaxial compression tests, we obtained mechanical and AE-signal data for five granite specimens. The maximum amplitude, hits, counts, rise time, absolute energy, and initiation frequency expressed as the cumulative value were selected as input parameters. The result showed that gradient boosting (GB) was the best model among the support vector regression methods. When GB was applied to the testing data, the root-mean-square error and R between the predicted and actual values were 0.96 and 0.077, respectively. A parameter analysis was performed to capture the parameter significance. The result showed that cumulative absolute energy was the main parameter for damage prediction. Thus, AE has practical applicability in predicting rock damage without conducting mechanical tests. Based on the results, this study will be useful for monitoring the near-field rock mass of nuclear waste repository.

In order to collect basic information of response behavior of red seabream (Pagrus major) during pilling, works for constructing wind power station in Byeonsan Peninsular, Korea were investigated. Four cultured red seabream CRB1 to CRB4 [total length (TL): 27.1 ± 1.0 cm; body weight: 359 ± 30 g] were tagged with an acoustic tag and used in experiment. CRB1 and CRB2 to CBR4 were released on the sea surface at same time around the constructing site of the wind power plant on September 22, 2017 and July 18, 2018, respectively. The tracking of the CRB1 to CRB2 and CRB3 to CRB4 were conducted for two hours, approximately, using VR100 receiver including a directional hydrophone and VR2W receivers array consisted of 19 presence/absence receivers (VR2W receivers), respectively. The underwater noise level before (no pile driving works) and during pile driving works was measured 116.0-118.0 dB (re 1Pa) and a maximum of 160 dB (re 1Pa), respectively. CRB1 moved about 6.0 km with average swimming speed of 80.2 ± 20.5 cm/s for 2.1 hours without pile driving work. The average water depth of the sea bed on the route of CRB1 was 9.1 ± 0.4 m. CRB2 moved about 7.3 km with the average swimming speed of 96.8 ± 27.1 cm/s for 2.1 hours with pile driving work. The water depth of the sea bed on the route of CRB2 was 11.9 ± 0.6 m. At results of the Rayleigh’s z-test two fishes CRB1 and CRB2 showed significant directionality in the movement (p < 0.01). Movement mean angles of CRB1 and CRB2 were 92.7 and 251.8°, respectively. CRB2, CRB3 and CRB4 exhibited the escaping behavioral response from the noise of source during the pile driving work. The swimming speed of the CRB2 exposed on the heavy underwater noise stimuli due to the pile driving work was 1.21 times faster than that of the CRB1 exposed on the ambient underwater

연소실 내 공조현상으로 인해 발생되는 열음향 불안정성은 안정적인 연소시스템을 구현하기 위해 해결해야 하는 고질적인 문제로 제기되어 왔다. 열음향 불안정성은 크게 1차 2차 열음향 불안정성으로 나뉘며, 본 연구에서는 열음향 불안정성 중 2차 열음향 불안 정성의 천이에 관해 열손실이 미치는 영향에 대한 실험적 연구를 진행하였다. 2차 열음향 불안정성을 발생시키기 위해 한쪽 끝이 열린 1/4 파장 공명기를 채택하여 수직으로 설치하였고, 공명기 내부에는 예혼합 가스를 주입하였다. 또한 공명기 상단으로 발생하는 열손실 효과를 비교하기 위해 추가적으로 외부 동축류 관을 설치하였다. 연료 농후조건의 예혼합 가스만을 채택하여 주입하였기 때문에 동축관 에 주입되는 기체에 따라 공명기 상부에 추가적인 확산화염이 형성될 수 있다. 그 결과 확산화염이 발생되었을 경우 공명기 상단으로의 열손실이 감소하며 2차 열음향 불안정성이 발현되었으며, 확산화염이 억제되어 공명기 상단으로의 열손실이 증가하였을 경우 2차 열음향 불안정성의 발현이 억제되는 결과를 도출하였다.

In this study, acoustic and viscosity data are collected in real time during the ball milling process and analyzed for correlation. After fast Fourier transformation (FFT) of the acoustic data, changes in the signals are observed as a function of the milling time. To analyze this quantitatively, the frequency band is divided into 1 kHz ranges to obtain an integral value. The integrated values in the 2–3 kHz range of the frequency band decrease linearly, confirming that they have a high correlation with changes in viscosity. The experiment is repeated four times to ensure the reproducibility of the data. The results of this study show that it is possible to estimate changes in slurry properties, such as viscosity and particle size, during the ball milling process using an acoustic signal.

PURPOSES : The purpose of this study is to enhance the reliability of artificial intelligence for a noise-based pavement condition rating system (to a target performance of 95 %). METHODS : By comparing four types of pattern recognition artificial intelligence, this work acquires high-quality learning data and optimizes data learning through analysis of error characteristics. RESULTS : The system reliability improved up to 97 % (82 % in a prior study). In addition, 100 % was achieved for the E(F) condition grade, which has a direct impact on maintenance decision making. CONCLUSIONS : KNN-DTW (K-nearest neighbor dynamic time warping) is judged to be the most suitable type of artificial intelligence for a noise-based pavement condition rating system; a 4-grade system is the most suitable for classifying pavement condition.