• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1037-1041
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• 2006

본 연구에서는 수질자료에 내재되어 있는 주기성 및 경향성 등을 파악하기 위해 웨이블렛 변환을 적용하였으며 비선형 시계열자료에 대한 예측력이 우수한 인공신경망을 적용하여 예측모형을 개발하였다. 대상자료는 섬진강 유역의 주암호 수질자동측정망 지점에서 측정되고 있는 수질자료 중 2002년 1월 1일 ${\sim}$ 2004년 12월 31일까지의 일 TOC 수질자료를 이용하였다. 웨이블렛 변환을 위해 사용한 기저함수로는 Daubechies의 10번 웨이블렛 함수('db10')를 사용하였으며, 각 스케일링 및 웨이블렛 함수를 이용하여 5단계까지 변환하였다. 최종 변환된 근사성분과 D5, D4, D3, D2의 상세성분 자료를 이용하여 1시간후 TOC 예측 모형을 구성하였으며 그 결과 은닉층의 노드의 수가 17개인 모형인 Model_5_17 모형이 가장 우수한 예측력을 보였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1905-1909
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• 2007

최근 도시발달과 인구증가로 인하여 수자원의 중요성이 더욱 커지고 있으며 이를 효율적으로 이용하고자하는 노력은 계속되고 있다. 또한 전 세계가 가뭄과 홍수 등 물과 관련된 재해를 예방하기 위하여 지속적인 수자원계획관리가 이루어지고 있으며, 특히 댐은 수자원의 효율적인 관리와 안정적인 용수공급을 위하여 건설된 것으로서 유역의 수문특성에 따른 변동성이 고려되어야 한다. 따라서 댐의 최적운영을 위해서는 정확한 강우 예측과 이에 따른 유입량 예측이 선행되어야 하며, 유입량 예측을 위한 강우-유출과정을 모형화 하여야 한다. 그러나 모형화에 따르는 복잡한 과정과 수문자료의 비선형성과 비정규성으로 인하여 많은 오차가 발생할 수 있다. 본 연구에서는 이러한 문제점을 개선하기 위하여 임하댐 유역에 신경망 이론을 강우-유출모형에 수학적으로 모형화 하였으며, 이를 통하여 효율적인 댐 운영을 위한 유입량 예측기법에 관한 기초연구를 수행하였다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.3
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• pp.111-119
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• 2005

The objective of this study is to predict fracture movements accurately and reliably by nonlinear analysis of the response of RC shear wall or RC flange sections. Hognestad's and Vallenas's theories are used for concrete model and Ramberg-Osgood's theory is used for steel model. Non-linear analysis considering confined concrete and unconfined concrete is performed. Mander's Fiber Approach Section analysis, new strain profile considering the Gamma factor are used to this section analysis. The section analysis considering cases of precracked, uncracked, boundary warping and shear warping is performed.

• Journal of the Korean Geotechnical Society
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• v.19 no.1
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• pp.181-189
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• 2003

The active earth pressure against a rigid retaining wall has been generally calculated using either Rankine's or Coulomb's formulation. Both assume that the distribution of active earth pressure exerted against the wall is triangular. However, many experimental results show that the distribution of the active earth pressure on a rigid rough wall is nonlinear. These results do not agree with the assumption used in both Rankine's and Coulomb's theories. The nonlinearity of the active earth pressure distribution results from arching effects in the backfill. Several researchers have attempted to estimate the active earth pressure on a rigid retaining wall, considering arching effect in the backfill. Their equations, however, have some limitations. In this paper, a new formulation for calculating the active earth pressure on a rough rigid retaining wall undergoing horizontal translation is proposed. It takes into account the arching effects that occur in the backfill.

• Journal of Astronomy and Space Sciences
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• v.14 no.1
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• pp.142-149
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• 1997

This paper applied one of the well-known optimal control theory, namely, linear quadratic regulator(LQR), to the station-keeping maneuvers(SKM) for a geostationary satellite. The boundary conditions to transfer the system with a good accuracy at a terminal time were based upon the predicted orbital data which are created due to the Earth's non-uniform mass distribution's effect during 14 days and due to luni-solar effect during 28 days. Through the linearization of the nonlinear system equation with respect to reference orbit and the numerical integration of Riccati equation, the optimal trajectories and the corresponding control law have been obtained by using LQR. From the comparison of ${\Delta}V$ obtained by LQR with the ${\Delta}V$ obtained anatically by geometric method, Station Keeping Maneuvers(SKM) via LQR may provide comparable results to a real system. Furthermore it will demonstrate the possibility in fuel optimization and life extension of geostationary satellite.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.163-171
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• 2009

Classical linear models have been generally used to analyze and forecast hydrologic time series. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. In recent, the BDS (Brock-Dechert-Scheinkman) statistic instead of conventional techniques has been used for detecting nonlinearity of time series. The BDS statistic was derived from the statistical properties of the correlation integral which is used to analyze chaotic system and has been effectively used for distinguishing nonlinear structure in dynamic system from random structures. DVS (Deterministic Versus Stochastic) algorithm has been used for detecting chaos and stochastic systems and for forecasting of chaotic system. This study showed the DVS algorithm can be also used for detecting nonlinearity of the time series. In this study, the stochastic and hydrologic time series are analyzed to detect their nonlinearity. The linear and nonlinear stochastic time series generated from ARMA and TAR (Threshold Auto Regressive) models, a daily streamflow at St. Johns river near Cocoa, Florida, USA and Great Salt Lake Volume (GSL) data, Utah, USA are analyzed, daily inflow series of Soyang dam and the results are compared. The results showed the BDS statistic is a powerful tool for distinguishing between linearity and nonlinearity of the time series and DVS plot can be also effectively used for distinguishing the nonlinearity of the time series.

• Knowledge Management Research
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• v.22 no.4
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• pp.119-133
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• 2021

As a large amount of data is produced in each industry, a number of time series pattern prediction studies are being conducted to make quick business decisions. However, there is a limit to predicting specific patterns in nonlinear time series data due to the uncertainty inherent in the data, and there are difficulties in making strategic decisions in corporate management. In addition, in recent decades, various studies have been conducted on data such as demand/supply and financial markets that are suitable for industrial purposes to predict time series data of irregular random walk models, but predict specific rules and achieve sustainable corporate objectives There are difficulties. In this study, the prediction results were compared and analyzed using the Chaos analysis method for roulette data and financial market data, and meaningful results were derived. And, this study confirmed that chaos analysis is useful for finding a new method in analyzing time series data. By comparing and analyzing the characteristics of roulette games with the time series of Korean stock index future, it was derived that predictive power can be improved if the trend is confirmed, and it is meaningful in determining whether nonlinear time series data with high uncertainty have a specific pattern.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.1
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• pp.53-61
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• 2015

This paper presented the nonlinear behaviors of the single-layered lattice dome, which is widely used for the long-span structure system. The behaviors were analysed through the classical shell buckling theory as the single-layered lattice dome behaves like continum thin shell due to its geometric characteristics, and finite element analysis method using the software program Nastran. Shell buckling theory provides two types of buckling loads, the global- and member buckling, and finite element analysis provides the ultimate load of geometric nonlinear analysis as well as the buckling load of Eigen value solution. Two types of models for the lattice dome were analysed, that is rigid- and pin-jointed structure. Buckling load using the shell buckling theory for each type of lattice dome, governed by the minimum value of global buckling or member buckling load, resulted better estimation than the buckling load with Eigen value analysis. And it is useful to predict the buckling pattern, that is global buckling or member buckling.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.6
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• pp.1465-1474
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• 1994

For a vertical wall with standing waves on its front face, the unsteady flow in a flow conduit installed through the wall is analyzed. A nonlinear standing wave theory making use of Fourier expansion is applied, and the results are verified by a hydraulic experiment. It is found that the nonlinear theory better predicts the behavior of the flow compared to its linear counterpart. The investigation of the water transmissibility through the conduit shows that the variation of the flow rate becomes larger as the standing wave height and period increase and as the length of conduit decreases. The relationship is presented by a nondimensional equation. The net flow gain per one wave period, which is directly related to water exchanging capability of the conduit, appears to be negative in both theory and experiment when the conduit is located near the bottom. The maximal flow gain occurs in the conduit whose mouth is located at the still water level. In addition, it is shown that the longer wave period and the shorter conduit length are more effective in the water exchanging performance.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.731-740
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• 1999

Autocorrelation function is widely used as a tool measuring linear dependence of hydrologic time series. However, it may not be appropriate for choosing decorrelation time or delay time ${\tau}_d$ which is essential in nonlinear dynamics domain and the mutual information have recommended for measuring nonlinear dependence of time series. Furthermore, some researchers have suggested that one should not choose a fixed delay time ${\tau}_d$ but, rather, one should choose an appropriate value for the delay time window ${\tau}_d={\tau}(m-1)$, which is the total time spanned by the components of each embedded point for the analysis of chaotic dynamics. Unfortunately, the delay time window cannot be estimated using the autocorrelation function or the mutual information. Basically, the delay time window is the optimal time for independence of time series and the delay time is the first locally optimal time. In this study, we estimate general dependence of hydrologic time series using the C-C method which can estimate both the delay time and the delay time window and the results may give us whether hydrologic time series depends on its linear or nonlinear characteristics which are very important for modeling and forecasting of underlying system.