• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.217-225
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• 2021

In this study, wave spectrum data were calculated using the water surface elevation data observed at 5Hz intervals from the HeMOSU-2 meteorological tower installed on the west coast of Korea, and wave parameters were estimated using wave spectrum data. For all significant wave height ranges, the peak enhancement parameter (γ_opt) of the JONSWAP spectrum and the scale parameter (α) and shape parameter (β) of the modify BM spectrum were estimated based on the observed spectrum, and the distribution of each parameter was confirmed. As a result of the analysis, the peak enhancement parameter (γ_opt) of the JONSWAP spectrum was calculated to be 1.27, which is very low compared to the previously proposed 3.3. And in the range of all significant wave heights, the distribution of the peak enhancement parameter (γ_opt) was shown as a combined distribution of probability mass function (PMF) and probability density function (PDF). In addition, the scale parameter (α) and shape parameter (β) of the modify BM spectrum were estimated to be [0.245, -1.278], which are lower than the existing [0.300, -1.098], and the result of the linear correlation analysis between the two parameters was β = -3.86α.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.245-255
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• 2022

Evaluating the qualitative the qualitative process of water resources by using various indicators, as one of the most prevalent methods for optimal managing of water bodies, is necessary for having one regular plan for protection of water quality. In this study, zoning maps were developed on a yearly basis by collecting and reviewing the process, validating, and performing statistical tests on qualitative parameters҆ data of the Iranian aquifers from 1995 to 2020 using Geographic Information System (GIS), and based on Inverse Distance Weighting (IDW), Radial Basic Function (RBF), and Global Polynomial Interpolation (GPI) methods and Kriging and Co-Kriging techniques in three types including simple, ordinary, and universal. Then, minimum uncertainty and zoning error in addition to proximity for ASE and RMSE amount, was selected as the optimum model. Afterwards, the selected model was zoned by using Scholar and Wilcox. General evaluation of groundwater situation of Iran, revealed that 59.70 and 39.86% of the resources are classified into the class of unsuitable for agricultural and drinking purposes, respectively indicating the crisis of groundwater quality in Iran. Finally, for validating the extracted results, spatial changes in water quality were evaluated using the Groundwater Quality Index (GWQI), indicating high sensitivity of aquifers to small quantitative changes in water level in addition to severe shortage of groundwater reserves in Iran.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.18-27
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• 2022

In this study, the time-dependent chloride ingress behavior in GGBFS concrete was evaluated considering marine exposure conditions and the properties of concrete mixtures. The concrete mixture for this study had 3 levels of water to binder ratio and the substitution rate of GGBFS, and outdoor exposure tests were performed considering submerged area, tidal area, and splash area. According to the evaluation results of diffusion coefficient considering properties of concrete mixtures, as the substitution rate of GGBFS increased, the decreasing rate of the diffusion coefficient decreased based on exposure periods of 730 days(2 years). As the evaluation result of the diffusion behavior according to the marine exposure conditions, the diffusion coefficient was evaluated in the order of submerged area, tidal area, and splash area. In tidal area, a relatively high diffusion coefficient was evaluated due to the repetition of wet and dry seawater. In this study, the effects of GGBFS substitution rate on the decreasing behavior of apparent chloride diffusion coefficient was analyzed in consideration of exposure conditions and periods. Linear regression analysis was performed with apparent chloride diffusion coefficient as output value and GGBFS substitution rate as input value. After 730 days of exposure, the effect of GGBFS on diffusion coefficient was significantly reduced. Even for OPC concrete, after 730 days, the diffusion coefficient was as low as that of GGBFS concrete, so the gradient of the regression equation decreased significantly. It is thought that improved durability performance for chloride ingress can be secured before 730 days through the use of GGBFS.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.314-320
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• 2022

The function of reinforcing fibers used in building materials is to maintain resistance to bending loads and to function for cracking caused by drying shrinkage. High-density fiber-cement composites are mainly used for linear plates and are used to increase bending resistance. Therefore, tensile properties, bonding strength with cement hydrate, alkali resistance, and the like are required. Recently, as the non-combustible performance has been strengthened, a function to minimize the occurrence of sparks during high-temperature heating has been added. Therefore, the use of organic fibers is limited. In this study, a study was conducted to replace polypropylene used as reinforcing fiber with hemp fiber with excellent heat resistance. Hemp fibers have excellent heat resistance, good affinity with cement, and excellent alkali resistance. Based on the total volume of polypropylene fibers used in the existing formulation, the non-combustible performance was compared and evaluated by using hemp fibers instead of the polypropylene fibers, and basic physical properties such as flexural strength were tested. As a result of conducting a non-combustibility and physical property test using hemp fibers with a fiber length of 7 mm using 2 % and 3 % by weight, it was found that there is no remaining time of the flame, and the flexural strength can be secured at 95 % level of the existing polypropylene fiber.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.19-31
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• 2022

In Machine learning, we usually divide the entire data into training data and test data, train the model using training data, and use test data to determine the accuracy and generalization performance of the model. In the case of models with low generalization performance, the prediction accuracy of newly data is significantly reduced, and the model is said to be overfit. This study is about a method of generating training data based on central limit theorem and combining it with existed training data to increase normality and using this data to train models and increase generalization performance. To this, data were generated using sample mean and standard deviation for each feature of the data by utilizing the characteristic of central limit theorem, and new training data was constructed by combining them with existed training data. To determine the degree of increase in normality, the Kolmogorov-Smirnov normality test was conducted, and it was confirmed that the new training data showed increased normality compared to the existed data. Generalization performance was measured through differences in prediction accuracy for training data and test data. As a result of measuring the degree of increase in generalization performance by applying this to K-Nearest Neighbors (KNN), Logistic Regression, and Linear Discriminant Analysis (LDA), it was confirmed that generalization performance was improved for KNN, a non-parametric technique, and LDA, which assumes normality between model building.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.49-62
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• 2020

Musicals are plays in which songs and dances are closely aligned with the plot development of the play, and songs and dances develop specific situations and a series of events. In particular, songs perform a fuction of causing the significant changes and emotional ventilation and amplification in the play by specifically capturing the goals and actions the characters are aiming for. This paper focuses on the song that takes on the function of the plot deployment, defines songs that embody actions and stories on stage' as 'action songs' and highlights their role and importance. Action Song is not a clearly defined or prescribed term in Musical. However, 'Action Song' is a song about how a character's desire or motivation for action, given as a 'character song' inserted at the beginning of an integrated musical play, provides a conflict that is a central event of action. In other words, 'action song' means a song that organically combines music and narrative by conveying the action on the stage as a song with lyrics. In addition, it is a song that moves the plot forward and contains a series of events or actions that are the material of the plot. This paper is intended to summarize the concept of'Action song which is not well known to us, and to lay the foundation of the stud, learned the concept, function and efficiency by the analyses of script and music of the musical . As a result, action songs accumulate and amplify tension caused by conflicts and induce the audience to immerse in their emotions. At the same time, the action song multiplies fun and interest of the play while the audience's expectation increased for the next scene after the action song and the progression of the play with single action makes the contents of the drama to be understood clearly.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.9-18
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• 2023

Predicting the compressive strength of high-performance concrete (HPC) is challenging because of the use of additional cementitious materials; thus, the development of improved predictive models is essential. The purpose of this study was to develop an HPC compressive-strength prediction model using an ensemble machine-learning method of combined bagging and stacking techniques. The result is a new ensemble technique that integrates the existing ensemble methods of bagging and stacking to solve the problems of a single machine-learning model and improve the prediction performance of the model. The nonlinear regression, support vector machine, artificial neural network, and Gaussian process regression approaches were used as single machine-learning methods and bagging and stacking techniques as ensemble machine-learning methods. As a result, the model of the proposed method showed improved accuracy results compared with single machine-learning models, an individual bagging technique model, and a stacking technique model. This was confirmed through a comparison of four representative performance indicators, verifying the effectiveness of the method.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.116-116
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• 2021

자연 하천에서의 부유사 농도 계측은 주로 재래식 채집방식을 활용한 직접계측 방식에 의존하여 비용과 시간이 많이 소요되며 점 계측 방식으로 고해상도의 시공간 자료를 측정하기엔 한계가 존재한다. 이러한 한계점을 극복하기 위해 최근 위성영상과 드론을 활용하여 촬영된 다분광 혹은 초분광 영상을 통해 고해상도의 부유사 농도 시공간분포를 측정하는 기법에 대한 연구가 활발히 진행되고 있다. 하지만, 다른 하천 물리량 계측에 비해 부유사 계측 연구는 하천에 따라 부유사가 비균질적으로 분포하여 원격탐사를 통해 정확하고 전역적인 농도 분포를 재현하기는 어려운 실정이다. 이러한 부유사의 비균질성은 부유사의 입도분포, 광물특성, 침강성 등이 하천에서 다양하게 분포하기 때문이며 이로 인해 부유사는 지역별로 다양한 분광특성을 가지게 된다. 따라서, 본 연구에서는 이러한 영향을 고려한 전역적인 부유사 농도 예측 모형을 개발하기 위해 실내 실험을 통해 부유사 특성별 고유 분광 라이브러리를 구축하고 실규모 수로에서 다양한 부유사 조건에 대한 초분광 스펙트럼과 부유사 농도를 측정하는 실험을 수행하였다. 실제 부유사 농도는 광학 기반 센서인 LISST-200X와 샘플링을 통한 실험실 분석을 통해 계측되었으며, 초분광 스펙트럼 자료는 초분광 카메라를 통해 촬영한 영상에서 부유사 계측 지점에 대한 픽셀의 스펙트럼을 추출하여 구축하였다. 이렇게 생성된 자료들의 분광 다양성을 주성분 분석(Principle Component Analysis; PCA)를 통해 분석하였으며, 부유사의 입도 분포, 부유사 종류, 수온 등과의 상관관계를 통해 분광 특성과 가장 상관관계가 높은 물리적 인자를 규명하였다. 더불어 구축된 자료를 바탕으로 기계학습 기반 주요 특징 선택 알고리즘인 재귀적 특징 제거법 (Recursive Feature Elimination)과 기계학습기반 회귀 모형인 Support Vector Regression을 결합하여 초분광 영상 기반 부유사 농도 예측 모형을 개발하였으며, 이 결과를 원격탐사 계측 연구에서 일반적으로 사용되어 오던 최적 밴드비 분석 (Optimal Band Ratio Analysis; OBRA) 방법으로 도출된 회귀식과 비교하였다. 그 결과, 기존의 OBRA 기반 방법은 비선형성을 증가시켜도 좁은 영역의 파장대만을 고려하는 한계점으로 인해 부유사의 다양한 분광 특성을 반영하지 못하였으며, 본 연구에서 제시한 기계학습 기반 예측 모형은 420 nm~1000 nm에 걸쳐 폭 넓은 파장대를 고려함과 동시에 높은 정확도를 산출하였다. 최종적으로 개발된 모형을 적용해 다양한 유사 조건에 대한 부유사 시공간 분포를 매핑한 결과, 시공간적으로 고해상도의 부유사 농도 분포를 산출하는 것으로 밝혀졌다.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.13 no.3
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• pp.252-259
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• 2008

The deep-sea mining system is generally composed of surface vessel, lifting system, buffer, flexible pipe and miner. The mining system can be regarded as a large-scale system in which each subsystem is interconnected to other ones. In order to control a large-scale system, decentralized control approaches have been proposed recently. In this paper, as a basic study on application of decentralized control, firstly, the mining system was modeled in a simplified way. Lifting system and buffer were regarded as a spherical pendulum and the flexible pipe was taken as a two-dimensional linear spring connection. Based on the simplified model dynamics, the mining system can be decentralized two subsystems, the one consisting of surface vessel, lifting system and buffer, and the other, the miner. Next, this paper proposed the design of controller for each decentralized subsystem by regarding the interacting terms as disturbances. The controllers kept the constant distance between two subsystems during the miner was moving on the specified track. Finally, the efficiency of proposed controller was proven through the numerical simulation of the derived model.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.471-479
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• 2024

Flood damage is becoming more serious due to the heavy rainfall caused by climate change. Physically based hydrological models have been utilized to predict stream water level variability and provide flood forecasting. Recently, hydrological simulations using machine learning and deep learning algorithms based on nonlinear relationships between hydrological data have been getting attention. In this study, the Long Short-Term Memory (LSTM) algorithm is used to predict the water level of the Seomjin River watershed. In addition, Climate Prediction Center morphing method (CMORPH)-based gridded precipitation data is applied as input data for the algorithm to overcome for the limitations of ground data. The water level prediction results of the LSTM algorithm coupling with the CMORPH data showed that the mean CC was 0.98, RMSE was 0.07 m, and NSE was 0.97. It is expected that deep learning and remote data can be used together to overcome for the shortcomings of ground observation data and to obtain reliable prediction results.