• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.313-325
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• 2022

Recently around the world, coastal erosion is paying attention as a social issue. Various constructions using low-crested and submerged structures are being performed to deal with the problems. In addition, a prediction study was researched using machine learning techniques to determine the wave attenuation characteristics of low crested structure to develop prediction matrix for wave attenuation coefficient prediction matrix consisting of weights and biases for ease access of engineers. In this study, a deep neural network model was constructed to predict the wave height transmission rate of low crested structures using Tensor flow, an open source platform. The neural network model shows a reliable prediction performance and is expected to be applied to a wide range of practical application in the field of coastal engineering. As a result of predicting the wave height transmission coefficient of the low crested structure depends on various input variable combinations, the combination of 5 condition showed relatively high accuracy with a small number of input variables defined as 0.961. In terms of the time cost of the model, it is considered that the method using the combination 5 conditions can be a good alternative. As a result of predicting the wave transmission rate of the trained deep neural network model, MSE was 1.3×10^-3, I was 0.995, SI was 0.078, and I was 0.979, which have very good prediction accuracy. It is judged that the proposed model can be used as a design tool by engineers and scientists to predict the wave transmission coefficient behind the low crested structure.

• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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• v.3 no.1
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• pp.29-34
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• 2000

This paper proposes a long-term prediction of offshore structures in ocean waves. All short-term statistics is generated by the simulation for all the combinations of significant wave heights and spectral peak periods. The simulation has been tested first on linear system, whose analytic solution is known, to verify if the simulation works accurately. Then the scheme was applied to the nonlinear system. This paper demonstrated that the proposed scheme could be an efficient tool in estimating the response of offshore structures.

• The korean journal of orthodontics
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• v.10 no.1
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• pp.81-93
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• 1980

The interrelationships between growth rates (and size) of the selected cranifacial dimensions and body dimensions (height and weight) were investigated in the longitudinal data of primary school children from 6 to 11 years of age. The data were obtained from serial cephalometric radiographs and health record which were taken at one year interval. Regression analyses were used to analyze the data. The main concludions might be summarized as follows; 1. Size relationships between body height (and weigh) and S-Gn, posterior facial height (s-Go), total mandibular length (Ar-Gn) showed high significant correlation, but no association between body height, weight and anterior cranial base length (S-N). 2. Correlation coefficients between facial dimensions and body height (and weight) were getting lower with age increase. 3. At all age groups, significant prediction equation for some facial dimensions with body height and weight were obtained. 4. In this sample, the growth rates of facial dimensions and body height and weight showed almostly constant during this age period and the growth rate of body height and weight of girls was exceeded that of boys. 5. A relatively high degree of variation between individuals existed in the sample. 6. A positive correlation was found for the relationship between the growth rates of facial dimensions and those of body height (and weight) in boys and girls, but was not found in total samples.

• The Journal of Pediatrics of Korean Medicine
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• v.31 no.4
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• pp.31-38
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• 2017

Objectives The purpose of this study was to find out the clinically reliable relationships between the Risser sign and chronological age, bone age, menarche, and adult height prediction (AHP) and to evidence the reliability of the Risser sign. Methods This study had been carried out with 50 children who had their growth checked in an oriental medical hospital from January 2015 to February 2017. We investigated Risser sign in AP X-rays with iliac crest, bone age, AHP for all 50 children and the timing of menarche from the 22 girls in the study subjects. We also investigated a correlation between the Risser stage and the other indicators to analyze statistical data. Results The mean chronological ages of Risser 1, 2, 3 and 4 were 11.2, 12.6, 14.4, and 15.5 years respectively for the boys and 10.8, 12.2, 13.8 and 14.8 years respectively for the girls. The mean bone ages of Risser 1, 2, 3 and 4 were 12.3, 13.6, 15.7 and 16.5 years respectively for the boys and 11.7, 13.8, 14.3 and 14.9 years respectively for the girls. We analyzed 22 girls' Risser stages in accordance with the duration from menarche. The result showed that in the first six months after menarche, all girls were in Risser 1 and 2; in the next six months, the girls were in Risser 2 on average; in the next 12 months, all girls were in Risser 3 and 4; after more than two years from menarche, all girls were in Risser 4. The mean remaining growth height of Risser 1, 2, 3 and 4 were 27.8, 17.3, 4.4 and 1.0 cm respectively for the boys and 14.5, 5.1, 3.1 and 1.1 cm respectively for the girls. The Risser stage was correlated strongly with chronological age (Spearman's rho=0.707 (boy), 0.841 (girl)), bone age (Spearman's rho=0.869 (boy), 0.875 (girl)), duration from menarche (Spearman's rho=0.909) and remaining growth height (Spearman's rho=-0.784 (boy), -0.878 (girl)). Conclusions This study showed that the Risser sign can be useful in assessing skeletal maturity and predicting remaining growth height based on the Risser stage and the other growth indicators.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.892-903
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• 2020

The rapid and accurate prediction of storm-surge height during typhoon attacks is essential in responding to coastal disasters. Most methods used for predicting typhoon data are based on numerical modeling, but numerical modeling takes significant computing resources and time. Recently, various studies on the expeditious production of predictive data based on artificial intelligence have been conducted, and in this study, artificial intelligence-based storm-surge height prediction was performed. Several learning data were needed for artificial intelligence training. Because the number of previous typhoons was limited, many synthesized typhoons were created using the tropical cyclone risk model, and the storm-surge height was also generated using the storm surge model. The comparison of the storm-surge height predicted using artificial intelligence with the actual typhoon, showed that the root-mean-square error was 0.09 ~ 0.30 m, the correlation coefficient was 0.65 ~ 0.94, and the absolute relative error of the maximum height was 1.0 ~ 52.5%. Although errors appeared to be somewhat large at certain typhoons and points, future studies are expected to improve accuracy through learning-data optimization.

• Journal of Environmental Impact Assessment
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• v.15 no.2
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• pp.93-100
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• 2006

In general, the verification to prediction formula in a national road and the main street of a town has been used recklessly in Korea. Therefore we investigated the validity of an existing prediction formula (NIER(87, 99), TR-Noise, KLC(2002)) with correction relationship which was based on both the prediction formular from apartment complex in the field and height 1.5m from the surface level. On the results of measuring the noise level form an isolated distance, the noise level showed that it was 4.5~5.5dB(A) by reason of becoming 2 folder far from a source. From the distribution of noise level measured by the apartment floors, the measurement point (1st floor) was 58.7~71.4dB(A) at its lowest level and the middle floors (3, 5, 7 and 10) were the highest distribution of noise level. From the analysis results on the application validity to an existing prediction formular (NIER(87, 99), TR-Noise, KLC(2002)) in the height 1.5m, the correction coefficients were 0.95~0.96 and the measured values were reasonably close to the predicted values, indicating the validity and adequacy of the predicted models. KLC(2002) model was found accurate within 3dB(A) with 36 data out of the total 42 data, showing the most accuracy among the predict models. However, the developed models have to improve the accuracy with a various of factors.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.1
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• pp.156-163
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• 2006

While sludge is settling in batch column, sludge concentration becomes high. Because the characteristic of sludge settling changes in function of time due to the sludge concentration change, the sludge settling velocity changes too. Also, because the sludge settling characteristic is influenced by a physical characteristic of sludge and a column height etc, it is difficult to exactly measure the sludge settling characteristic. Although the sludge volume indexes, SVI, SSVI and $SSVI_{3.5}$, are used to predict sludge settling characteristic, these indexes are not reliable values. Because the previously established models for sludge settling velocity predict the sludge settling velocity only, it is difficult to predict sluge-water interface height by using those models. The purpose of this experiment is to establish the empirical model which predicts the sludge interface height change with respect to the sludge physical characteristic and the settling condition.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.225-230
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• 1998

The purpose of this study is to contribute the development of an El Nino prediction model. The objectives of the study are to (1) extract sea surface height data from the TOPEI/Poseidon altimeter, and (2) compare the relations among the sea surface height, sea surface temperature and wind field. NOAA AVHRR Multi-channel data is used for sea surface temperature and wind data is derived from ERS 1, 2 AMI wind scatterometer. The results showed that sea surface height has increased significantly during the El Nino season. The sea surface height is positively related to sea surface temperature and negatively related to zonal wind.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.2
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• pp.211-218
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• 1993

Landslides, failure of slope stability by natural or artificial factors, occur loss of life and properties. Recently, statistical methods and field measurements are used to a study for prediction of landslide harzard area, but there are so many difficulties to find the occurence system because of its complexity. In this study, authors choose the model area where occured landslides to predict the landslide hazard. Authors made a database of ground height to compare the each topography by scale of 1 : 25,000, 1 : 10,000, 1 : 5,000 and 1 : 1,200. Authors predict to landslide hazard area by the weight of ground height data and slope angle data. Finally, authors could know the possibility of prediction to find the landslide hazard partly.