• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.384-395
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• 2020

Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions, such as sediment transport, longshore currents, and shock wave pressure. Therefore, it is crucial to accurately predict breaker index such as breaking wave height and breaking depth, when designing coastal structures. Numerous scientific efforts have been made in the past by many researchers to identify and predict the breaking phenomenon. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. In this paper, we applied a representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a model for prediction of the breaking index is developed from previously published experimental data on the breaking wave, and a new linear equation for prediction of breaker index is presented from the trained model. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.540-550
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• 2020

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.345-358
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• 2019

The purpose of this study is to develop the Internet vulnerability index of adolescents. To do this, we used the original data of long - term follow - up survey for the internet overdependency cause analysis conducted by NIA in 2018, and analyzed the correlation between alternatives of internet vulnerability index and personal psychology by using linear regression analysis. Factor analysis showed that the relationship with the surroundings was indexed by adding 9 items to positive factors such as family acceptance, peer attachment, and teacher favorability. The relationship between the surroundings and self - stigmatization is confirmed, and the relationship between the surroundings and the Internet fragility is predicted to be negatively related, and the digital capacity is also assumed to be negatively correlated with the Internet vulnerability. In order to develop the specific form of the Internet vulnerability index, personal psychology and linear regression analysis were conducted. As a result, positive factors and R value of personal psychology were increased when considering the relationship with the environment and the digital capacity rather than the Internet overdependency model. Based on these implications, we discussed the implications and limitations of this study.

• Proceedings of the International Union of Geodesy And Geophysics Korea Journal of Geophysical Research Conference
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• 2003.05a
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• pp.17-17
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• 2003

한반도에 있어서의 지진의 영향을 분석하기 위해서는 강지진동 연구가 필수적이다. 강지진동 자료가 부족한 한반도의 특성상 모사를 통해 연구하고 있다. 강지진동 분석을 하기 위해서는 되도록 노이즈가 포함되어 있지 않은 지진파자료를 선택하여 그 지진자료의 스펙트럼 분석을 통해 감쇠상수 k, Q 등을 구한다. 이러한 감쇠상수 값을 통해 한반도의 진동 특성을 이해할 수 있다. 그러나 감쇠상수를 구하는 과정에서 감쇠상수 분석에 사용된 지진자료에 노이즈가 더해졌을 경우, 어떤 형태로 스펙트럼 영역에 영향을 미치고, 감쇠상수에는 어떤 영향을 미치는 지를 연구하여 노이즈효과를 제거할 수 있는 최적화된 분석에 관한 연구가 선행되어야 한다고 본다. 따라서 이번 연구에서는 강지진동 모사프로그램을 가지고 노이즈효과를 적용하면서 감쇠상수에 노이즈가 어떤 영향을 미치는 지에 대한 수치 해석적 연구를 실시하였다. 합성지진파에 이 합성지진파와 전혀 다른 주파수 형태를 보이는 노이즈를 강도를 달리하면서 합성해 본 결과, 노이즈효과를 고려할 수 있는 몇 가지 요소가 있음을 알 수 있었다. 감쇠상수 k값을 강지진동 모사프로그램으로부터 값을 달리하며 합성해 본 결과 노이즈효과를 보이는 것을 알 수 있었으며, 감쇠상수 k를 선형회귀를 통해 $k_{s}$ 와 $k_{q}$를 구할 때의 적용 주파수 범위를 변화시켰을 때도 일정한 양상의 노이즈 효과를 보였다. 또 지진자료와 노이즈를 중첩시킨 지진파 시계열 자료의 정부분만을 감쇠상수 k를 구하는 선형회귀에 이용했을 경우에도 노이즈 효과를 보였다. 또한 계산되어 나온 감쇠상수 값으로부터 특정지역의 지반운동의 특성을 이해할 수 있는 스펙트럼 가속도, 최대 가속도, 및 최대속도 값에 따른 감쇠식을 구하였다. 이것을 한반도와 같은 판 내부 환경인 ENA 값과 비교하였으며 기존의 연구와도 비교하였다.심으로부터 지오이드까지의 거리, 지오이드로부터 지표까지의 거리를 정의해주었으며, 각 격자점의 수직구조를 정의하기 위해 깊이에 따른 각 매질의 밀도, P파의 속도, S파의 속도, P파에 대한 Q값, S파에 대한 Q값을 정의 해주었다. S파의 속도를 구하기 위해서 지구 내부 물질을 포아송 매질이라는 가정 하에, 관계식을 $Vp{\;}={\;}SQRT(3){\;}{\times}{\;}Vs$ 이용하였다. 획득한 모델치들을 이용해 동해와 동해 인근 지역에 대한 초기모델을 구축하였다. 약 1 × 10/sup 6/ e/sup -//sec·n㎡ 의 전자선량에 해당되며 이를 기준으로 각각의 illumination angle에 대한 임계전자선량을 평가할 수 있었다. 실질적으로 Cibbsite와 같은 무기수화물의 직접가열실험 시 전자빔 조사에 의해 야기되는 상전이 영향을 배제하고 실험을 수행하려면 illumination angle 0.2mrad (Dose rate : 8000 e/sup -//sec·n㎡)이하로 관찰하고 기록되어야 함을 본 자료로부터 알 수 있었다.운동횟수에 의한 영향으로써 운동시간을 1일 6시간으로 설정하여, 운동횟수를 결정하기 위하여 오전, 오후에 각 3시간씩 운동시키는 방법과 오전부터 6시간동안 운동시키는 두 방법을 이용하여 품질을 비교하였다. 각 조건에 따라 운동시킨 참돔의 수분함량을 나타낸 것으로, 2회(오전 3시간, 오후 3시간)에 나누어서 운동시키기 위한 육의 수분함량은 73.37±2.02%를 나타냈으며, 1회(6시간 운동)운동시키기 위한 육은 71.74±1.66%을 나타내었다. 각각의 운동조건에서 양식된 참돔은 사육초기에는 큰 변화가 없었으나, 사육 5일 이후에는 수분함량이 증가하여 15일에는 76.40±0.14, 75.62±0.98%의 수분함량을 2회와 1회 운동시킨 참돔의 육에서 각각 나타났다. 운동횟수에 따른 지

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.4
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• pp.148-159
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• 2021

This study estimates the region of freshwater influence (ROFI) by Han River discharge in the Yeomha channel, Gyeonggi Bay. A 3-D numerical model, which is validated for reproducibility of variation in current velocity and salinity, is applied in Gyeonggi Bay. Distance of freshwater influence (DOFI) is defined as the distance from the entrance of Yeomha channel to the point where surface salinity is 28 psu. Model scenarios were constructed by dividing the Han River discharge into 10 categories (200~10,000 m³/s). The relation equation between freshwater discharge and DOFI was calculated based on performing a non-linear regression analysis. ROFI in Yeomha channel expands from the southern sea area of Ganghwa-do to the northern sea area of Yeongheung-do as the intensity of Han River discharge increases. The discharge and DOFI are a proportional relationship, and the increase rate of DOFI gradually decreases as discharge increases. Based on the relation equation calculated in this study, DOFI in the Yeomha channel can be estimated through the monthly mean Han River discharge. Accordingly, it will be possible to respond and predict problems related to damage to water quality and ecology due to rapid freshwater runoff.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.632-640
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• 2017

Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.

• Journal of the Korea Concrete Institute
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• v.18 no.5 s.95
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• pp.611-620
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• 2006

In the shear failure mechanism of a beam, beam and arch actions always exist simultaneously. According to the shear span to depth ratio, the proportion between these two actions is varied and the contribution of these actions to shear capacity is changed. Moreover, the current codes provide recommendations based on experimental results of normal strength concrete, so the application range of concrete strength must be extended. Based on this mechanism and new requirement, a simplified analytical equation for shear capacity prediction of reinforced high strength concrete beams without stirrups is proposed. To reflect the change in the contribution between these actions, stress variation in the longitudinal reinforcement along the span is considered by use of the Jenq and Shah Model. Dowel action with horizontal splitting failure and shear friction between cracks are also taken into account. ize effect is included to derive a more precise equation. Regression analysis is performed to determine each variable and simplify the equation. And, the formula derived from theoretical approaches is evaluated by comparison with numerous experimental data, which are in broad range of concrete strength(especially in high strength concrete), shear span to depth ratio, geometrical size and longitudinal steel ratio. It is shown that the proposed equation is more accurate and simpler than other empirical equations, so a wide range of a/d can be considered in one equation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1C
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• pp.11-17
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• 2011

This study was performed a series of the isotropic compression-expansion tests and the drained triaxial tests with various the relative densities 25%, 50%, 80% and 100% for Baekma river sand. Using the tests results the characteristic of the parameters of Lade's single hardening constitutive model were investigated. The soil parameters Kur and n representing elastic behavior are not much affected by the change of the relative density. The other parameters such as failure criterion (m, ${\eta}_1$), hardening function (C, p) and plastic potential (${\Psi}_2$, ${\mu}$) are in a positive linear relationship with the relative density. Since the soil parameters h and $\alpha$ representing yield function do not change much to the change of relative density and also closely related to failure criterion, they can be replaced by failure criterion ${\eta}_1$. We also observed that predicted values from the Lade's single hardening constitutive model were well consistent with the observed data.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.61-73
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• 2014

This study reviewed an application of water quality estimation using an Airborne Hyperspectral Imagery (A-HSI) and tested a part of Han River water quality (especially suspended solid) estimation with available in-situ data. The estimation of water quality was processed two methods. One is using observation data as downwelling radiance to water surface and as scattering and reflectance into water body. Other is linear regression analysis with water quality in-situ measurement and upwelling data as at-sensor radiance (or reflectance). Both methods drive meaningful results of RS estimation. However it has more effects on the auxiliary dataset as water quality in-situ measurement and water body scattering measurement. The test processed a part of Han River located Paldang-dam downstream. We applied linear regression analysis with AISA eagle hyperspectral sensor data and water quality measurement in-situ data. The result of linear regression for a meaningful band combination shows $-24.847+0.013L_{560}$ as 560 nm in radiance (L) with 0.985 R-square. To comparison with Multispectral Imagery (MSI) case, we make simulated Landsat TM by spectral resampling. The regression using MSI shows -55.932 + 33.881 (TM1/TM3) as radiance with 0.968 R-square. Suspended Solid (SS) concentration was about 3.75 mg/l at in-situ data and estimated SS concentration by A-HIS was about 3.65 mg/l, and about 5.85mg/l with MSI with same location. It shows overestimation trends case of estimating using MSI. In order to upgrade value for practical use and to estimate more precisely, it needs that minimizing sun glint effect into whole image, constructing elaborate flight plan considering solar altitude angle, and making good pre-processing and calibration system. We found some limitations and restrictions such as precise atmospheric correction, sample count of water quality measurement, retrieve spectral bands into A-HSI, adequate linear regression model selection, and quantitative calibration/validation method through the literature review and test adopted general methods.