• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.173-185
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• 2015

This study aims to investigate the characteristics and types of car accidents and establish a prediction model by analyzing 456 car accidents having occurred in the 11 tunnels in Busan, through statistical analysis techniques. The results of this study can be summarized as below. As a result of analyzing the characteristics of car accidents, it was found that 64.9% of all the car accidents took place in the tunnels between 08:00 and 18:00, which was higher than 45.8 to 46.1% of the car accidents in common roads. As a result of analyzing the types of car accidents, the car-to-car accident type was the majority, and the sole-car accident type in the tunnels was relatively high, compared to that in common roads. Besides, people at the age between 21 and 40 were most involved in car accidents, and in the vehicle type of the first party to car accidents, trucks showed a high proportion, and in the cloud cover, rainy days or cloudy days showed a high proportion unlike clear days. As a result of analyzing the principal components of car accident influence factors, it was found that the first principal components were road, tunnel structure and traffic flow-related factors, the second principal components lighting facility and road structure-related factors, the third principal factors stand-by and lighting facility-related factors, the fourth principal components human and time series-related factors, the fifth principal components human-related factors, the sixth principal components vehicle and traffic flow-related factors, and the seventh principal components meteorological factors. As a result of classifying car accident spots, there were 5 optimized groups classified, and as a result of analyzing each group based on Quantification Theory Type I, it was found that the first group showed low explanation power for the prediction model, while the fourth group showed a middle explanation power and the second, third and fifth groups showed high explanation power for the prediction model. Out of all the items(principal components) over 0.2(a weak correlation) in the partial correlation coefficient absolute value of the prediction model, this study analyzed variables including road environment variables. As a result, main examination items were summarized as proper traffic flow processing, cross-section composition(the width of a road), tunnel structure(the length of a tunnel), the lineal of a road, ventilation facilities and lighting facilities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.1
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• pp.21-30
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• 1985

The tension leg platform (TLP) is a kind of compliant structures, and is also a type of moored stable platform with a buoyancy exceeding the weight because of having tensioned vertical anchor cables. In this paper, among the various kinds of tension leg structures, Deep Oil Technology (DOT) TLP was analyzed because it has large-displacement portions of the immersed surface such as vertical corner pontoons and small-diameter elongated members such as cross-bracing. It also has results of hydraulic model tests, comparable with theorectical analysis. Because of the vertical axes of symmetry in the three vertical buoyant legs and because there are no larger horizontal buoyant members between these three vertical members, it was decided to develop a numerical algorithm which would predict the dynamic response of the DOT TLP using the previously developed numerical algorithm Floating Vessel Response Simulation (FVRS) for vertically axisymmetric bodies of revolution. In addition, a linearized hydroelastic Morison equation subroutine would be developed to account for the hydrodynamic pressure forces on the small member cross bracing. Interaction between the large buoyant members or small member cross bracings is considered to be negligible and is not included in the analysis. The dynamic response of the DOT TLP in the surge mode is compared with the results of the TLP algorithm for various combinations of diffraction and Morison forces and moments. The results which include the Morison equation are better than the results for diffraction only. This is because the vertically axisymmetric buoyant members are only marginally large enough to consider diffractions effects. The prototype TLP results are expected to be more inertially dominated.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.61-67
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• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• Journal of Korean Society of Steel Construction
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• v.16 no.1 s.68
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• pp.11-20
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• 2004

This paper focused on square steel tubular column to H-beam connections (concrete filled tubular) with an outside-type diaphragm. Based on the yield line theory and the nonlinear static FEM analysis the specification equations were evaluated by comparing them with previous result of the simplified tensile experiment[please check. The yield line theory applied to the mechanical model theory revised by K. Morita, the nonlinear static FEM analysis using abaqus/standard, the ultimate strength equation in the specification equation using the factor for long-time loading, and the yield ratio according to material. The allowable strength in the specification equations applied the safety factors of 2.2 and 2.6 in the cases with and without filled concrete, respectively. Therefore, the evaluation of strength(for the previous result of the simplified tensile experiment in this study) was considered possible through the yield line theory, the nonlinear static FEM analysis, and the specification equations. Likewise, the specification equations were seen to be an underestimate of the previous result of the simplified tensile experiment. The strength and displaced mesh in the FEM analysis approximated the previous result of the simplified tensile experiment.

• Magazine of the Korea Concrete Institute
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• v.10 no.6
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• pp.269-280
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• 1998

Recently, application of composite materials such as fiber reinforced concretes(FRCs) and fiber reinforced plastics(FRPs) in conjunction with conventional structural components has become one of the main research areas. A proper use of advanced composite materials requires understanding their resistance mechanism and failure mode when they are applied to structures or their components. Particular considerations are given in this research to develop an analytical model which can predict the nonlinear flexural responses of bonded and unbonded prestressed concrete beams possibly having layers of different cementitious composite matrices in a section and/or FRP tendons. The block concept is used, which can be regarded as an intermediate modeling method between the couple method with one block and the layered method with multiply sliced layers in a section. In order to find a particular deflection point of a beam under load, solutions to the 2N-variables are found numerically by using approximate N-force equilibrium equations and N-moment equilibirum equations. The model is shown to successfully predict the flexual behavior of variously reinforced bonded and unbonded prestressed concrete beams. The model is also successful in simulating a gradually increasing load after sudden drop inload resistance due to fracture of one or more FRP tendons. This feature is useful in tracing the overall load-deflection response of a beam prestressed with brittle FRP tendons.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.2079-2083
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• 2007

하구둑은 해수의 역류에 의한 염수침입을 방지하고 유역에 필요한 담수를 확보하기위하여 건설되며, 하구둑에 의하여 하천의 하구에 담수호가 생성된다. 하구담수호에 의해 기존에 염해로 취수할 수 없었던 하천구간에서의 취수가 가능하게 되며, 하구담수호를 일정한 수위로 관리함으로써 상당량의 농업용수를 확보할 수 있다. 이와 관련하여 하구담수호에 의하여 확보되는 수자원량에 대해 설계 당시의 분석 자료들이 있기는 하지만, 대부분 연 총량의 형태로 나타나 있으며, 수리권과 관련하여 하구둑에 의하여 추가적으로 확보되는 수리권 가용수량이 얼마인지에 대한 분석은 거의 없다. 그것은 하구둑의 방류량이 댐의 방류량과는 달라서 조석의 영향을 매우 크게 받기 때문에 단순한 물수지 분석으로 이를 모의하기 힘들기 때문이다. 게다가 1일 2회의 조석이 발생하므로 1일단위의 물수지 분석은 어려운 현실이다. 그럼에도 불구하고 하구둑에서 많은 양의 농업용수 취수가 이루어지고 있다. 과연 얼마정도의 양이 수리권으로서의 역할을 할 수 있는지 모르고 임의적인 허가가 계속 부여될 경우, 갈수 시 용수공급의 안정성을 확보할 수 없게 될 것이다. 따라서 안정적인 용수의 공급을 위한 적절한 수리권 가용수량에 대한 추정 필요하다. 이에 본 연구에서는 영산강하구둑의 실시간 운영을 위하여 고 등(2004)이 개발한 "영산강하구 둑 물관리 프로그램"의 기본적인 조위의 예측과 배수갑문의 방류량 산정기법 등과 수자원장기종 합계획(2006) 수립 시에 사용된 장기간의 일자연유량을 이용하여 하구둑의 수리권 가용수량 추정 모형을 개발하고, 이를 영산강 하구둑에 의해 생성된 영산호에 적용함으로써 영산호에 의해 증가되는 수리권 가용수량에 대해 고찰하여 보았다., 그리고 T-P $77.7{\sim}96.6%$(평균 94.3%)로 산정되었다. 이러한 결과는 대청호로 유입하는 유기물과 영양염류 연간 부하량의 80% 이상이 강우-유출과 함께 유입하는 것을 의미하며, 저수지 수질관리를 위해서는 유역차원의 비점오염원 관리가 시급함을 시사한다.하기 위해, 수렴흐름 추적자시험에 의한 종분산지수와 시험대수층의 평균선형유속을 이용하여 종분산계수를 구하였다. 현장에서 수행된 양수시험에 의한 평균선형유속 22.44 m/day와 평균 종분산지수 0.4155 m를 적용하여 산정된 종분산계수는 $9.32\;m^2/day$이었다. 따라서, 시험부지 내 충적층에서 일정한 양수율$(2,500\;m^3/day)$로 지하수를 개발할 시에 양수정 주변지역으로 유입되는 염소이온의 확산면적은 1일 $9.32\;m^2$ 정도일 것으로 나타났다.적인 $OH{\cdot}$ 의 생성은 ascorbate가 조직손상에 관여할 가능성을 시사하였다.었다. 정확한 예측치를 얻기 위하여 불균질 조직이 조사야에 포함되는 경우 보정이 요구되며, 골반의 경우 골 조직의 보정이 중요한 요인임을 알 수 있었다. 이를 위하여 불균질 조직에 대한 정확한 정보가 요구되며, 이는 CT 영상을 이용하는 것이 크게 도움이 되리라 생각된다.전시 슬러지층과 상등액의 온도차를 측정하여 대사열량의 발생량을 측정하고 슬러지의 활성을 측정할 수 있는 방법을 개발하였다.enin과 Rhaponticin의 작용(作用)에 의(依)한 것이며, 이는 한의학(韓醫學) 방제(方劑) 원리(原理)인 군신좌사(君臣佐使) 이론(理論)에서 군약(君藥)이 주증(主症)에 주(

• Journal of the Korean Society of Food Science and Nutrition
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• v.21 no.2
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• pp.213-218
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• 1992

The reaching time to the freezing point was to be fast in the order of 2% agar gel, 5% agar gel, 20% gelatin gel, pork, respectively. The freezing time and the passing time through the zone of the maximum ice crystal formation had linear relationship with the coolant temperature. The average diameter d$_{p}$ of ice crystal in a soybean protein gel and the moving of freezing front were represented an inverse proportion, and the moving velocity of freezing front was shown as 3.4$\times$10$^{-6}$ $\textrm{cm}^2$/sec from predicted theoretical formula. This value was very close to experimental results. The storage temperature did not give any influences for the growth of ice crystal in inside soybean protein gels during freezing conservation. The relationship between freezing condition and structure of freezing front was as follows : (moving velocity of freezing front) : (mass transfer rate of water at freezing point)$\times$(surface area of freezing front).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3176-3183
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• 2010

A multi-scale fatigue life prediction methodology of composite pressure vessels subjected to multi-axial loading has been proposed in this paper. The multi-scale approach starts from the constituents, fiber, matrix and interface, leading to predict behavior of ply, laminates and eventually the composite structures. The multi-scale fatigue life prediction methodology is composed of two steps: macro stress analysis and micro mechanics of failure based on fatigue analysis. In the macro stress analysis, multi-axial fatigue loading acting at laminate is determined from finite element analysis of composite pressure vessel, and ply stresses are computed using a classical laminate theory. The micro stresses are calculated in each constituent from ply stresses using a micromechanical model. Three methods are employed in predicting fatigue life of each constituent, i.e. a maximum stress method for fiber, an equivalent stress method for multi-axially loaded matrix, and a critical plane method for the interface. A modified Goodman diagram is used to take into account the generic mean stresses. Damages from each loading cycle are accumulated using Miner's rule. Monte Carlo simulation has been performed to predict the overall fatigue life of a composite pressure vessel considering statistical distribution of material properties of each constituent, fiber volume fraction and manufacturing winding angle.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.19-24
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• 2020

The interest in renewable energy is increasing due to the depletion of fossil fuels. In particular, active research on wave power, which is highly predictable and abundant, is being conducted. The coaxial accelerator-type wave power generator used in this study was designed to improve the power generation efficiency by converting bidirectional linear motion into a rotational force. In an offshore engineering basin, waves were generated, and case tests were performed according to the wave period and wave height. The experimental results were verified by the theoretical method related to the frequency response, and the overall trend was confirmed to be consistent. These results are expected to be useful in estimating the power of wave generators and designing parameters to improve the efficiency of wave energy in the design stage before manufacturing. In addition, the manufacturer can predict the wave energy efficiency of wave generators, which can reduce the development time and cost by preventing trial and error processes.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.