• Journal for History of Mathematics
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• v.17 no.2
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• pp.85-96
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• 2004

The Resealed range statistical analysis and Hurst exponent which are standard methods to test the chaotic model are used to examine sinusoid pattern. We notice that the Hurst exponent can be used as a measure to examine the time series data that show semi-cyclic trend with noise.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.371-381
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• 2017

There has never been research on Chaos analysis using real estate auction sale price rate in Korea. In this study, three Chaos analysis methodologies - Hurst exponent, correlation dimension, and maximum Lyapunov exponent - in order to capture the nonlinear deterministic dynamic system characteristics. High level of Hurst exponent and the extremely low maximum Lyapunov exponent provide the tendency and the persistence of the data. The empirical results give two meaningful facts. First, monthly time lags of the correlation dimension are coincident with the time period from the approval auction start day to the sale price fixing day. Second, its weekly time lags correspond to the time period from the last day of request for sale price allocation to the sale price fixing day. Then, this study potentially examines the predictability of the real estate auction price rate time series.

• Journal of Korea Water Resources Association
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• v.37 no.12
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• pp.993-1007
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• 2004

There are many different techniques for the estimation of the Hurst exponent. However, the techniques can produce different characteristics for the persistence of a time series each other. This study uses several techniques such as adjusted range, resealed range(RR) analysis, modified restated range(MRR) analysis, 1/f power spectral density analysis, Maximum Likelihood Estimation(MLE), detrended fluctuations analysis(DFA), and aggregated variance time(AVT)method for the Hurst exponent estimation. The generated time series from chaos and stochastic systems are analyzed for the comparative study of the techniques. Then this study discusses the advantages and disadvantages of the techniques and also the limitations of them.

• Journal of the Korean Geotechnical Society
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• v.31 no.4
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• pp.19-29
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• 2015

Nowadays, the importance of the information management of construction sites to achieve the goal of safety construction. This management uses the collaborated analysis of in-situ monitoring data and numerical analysis, especially of an earth retaining structures of excavation sites. In this paper, the fractal theory was applied to actually monitored data from various excavation sites to develop the alternative interpolation technique which could predict the displacement behavior of unknown location around the monitoring locations and the future behavior of the monitoring locations with the steps of excavation. Data, mainly from inclinometer, were collected from various sites where retaining structures were collapsed during construction period, as well as from normal sites with the characteristics of geology, excavation method etc. In the analyses, Hurst exponent (H) was estimated with monitored periods using the Rescaled range analysis (R/S analysis) method applying the H in simulation processes. As the results of the analyses, Hurst exponents were ranged from 0.7 to 0.9 and showed the positive correlation of H > 1/2. The simulation processes, then, with the Hurst exponent estimated by Rescaled range analysis method showed reliable results. In addition, it was also expected that the variation of Hurst exponents with the monitoring period could instruct the abnormal behavior of an earth retaining structures to directors or operators. Therefore it was concluded that fractal theory could be applied for predicting the lateral displacement of unknown location and the future behavior of an earth retaining structures to manage the safety of construction sites during excavation period.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.792-796
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• 2005

The methods of times series analysis have been recognized as important tools for assisting in solving problems related to the management of water resources. Especially, After more than 40 years the so-called Hurst effect remains an open problem in stochastic hydrology. Until now, its existence has been explained fly R/S analysis that roots in early work of the British hydrologist H.E. Hurst(1951). Today, the Hurst analysis is mostly used for the hydrological studies for memory and characteristics of time series and many methodologies have been developed for the analysis. So, there are many different techniques for the estimation of the Hurst exponent(H). However, the techniques can produce different characteristics for the persistence of a time series each other. We found that DFA is the most appropriate technique for the Hurst exponent estimation for both the shot term memory and long term memory. We analyze the SOI(Southern Oscillations Index) and 6 tree-ring series for USA sites by means of DFA and the BDS statistic is used for nonlinearity test of the series. From the results, we found that SOI series is nonlinear time series which has a long term memory of H=0.92. Contrary to earlier work of Rao(1999), all the tree- ring series are not random from our analysis. A certain tree ring series show a long term memory of H=0.97 and nonlinear property. Therefore, we can say that the SOI and tree-ring series may show long memory and nonlinearity.

• Proceedings of the Korea Water Resources Association Conference
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• 2003.05b
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• pp.599-602
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• 2003

• Journal of Korea Water Resources Association
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• v.44 no.10
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• pp.831-841
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• 2011

In this study, with the assumption that the geographical characteristics of the river basin have selfsimilarity, fractal dimensions are used to quantify the complexity of the terrain. For this, Area exponent and hurst exponent was applied to estimate the fractal dimension by using spatial analysis. The result shows that the value of area exponent and hurst exponent calculated by the fractal dimension are 2.008~2.074 and 2.132~2.268 respectively. Also the $R^2$ of area exponent and hurst exponent are 94.9% and 87.1% respectively too. It shows that the $R^2$ is relatively high. After analyzing the spatial self-similarity parameter, it is shown that traditional urban area's moderate slope geographical characteristic closed to 2D fractal in Ara water way. In addition, the relation between fractal dimension and geographical elements are identified. With these results, fractal dimension is the representative value of basin characteristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.463-463
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• 2012

본 연구에서는 유역의 공간적 자기상사성 평가를 통하여 하천유역의 특성을 파악하고자 하였다. 이를 위해 자기상사성 분석의 지표인 허스트지수 및 프랙탈차원을 산정하였다. 허스트지수(h)의 산정은 모형에 있어서 상당히 중요한 부분을 차지한다. 이 지수에 따라 지형의 모양은 서로 상이하게 다루어질 수 있기 때문이다. 허스트지수의 산정은 Hurst가 제시한 방법(허스트지수), Peters의 수정식, Mandebrot와 Wallis의 Pox 도표, 투영면적 및 표면적 비율 방법(면적지수)이 있으며, 본 연구에서는 유역의 공간 자기상성 분석을 위해 면적지수에 의한 방법과 허스트지수에 의한 방법을 적용하였다. 지형자료는 LiDAR 측량 및 하천 횡단측량에 의해 생성된 정밀 DEM을 활용하여 허스트지수 및 프랙탈차원을 산정하였다. 면적지수 및 허스트지수에 의한 프랙탈차원과 평균경사도와의 관계에서 아라천유역은 결정계수 R2값이 94.9 %, 99.5 %로 비교적 결정계수값이 크게 나타났으며, 경사도와 표면적과의 관계에서 결정계수 R2값은 81.8 %로 분석되었다. 이는 면적지수와 허스트지수에 의해 산정된 프랙탈 차원은 유역의 지형특성 인자로 타당성을 갖는 것으로 판단된다.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.221-226
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• 2005

우리나라의 애국가(愛國歌), 일본(Kimigayo) 그리고 미국국가(The star-spangled Banner) 등에 대해서 악보가 갖는 고유정보를 카오스적 접근 방법인 근사엔트로피(approximate entropy)와 허스트(Hurst) 지수를 이용하여 각각 음계(scale)의 복잡도(複雜度)와 장기기억속성(長期 記憶 屬性)을 계산하여 비교하였던 바, 애국가가 상대적으로 복잡도에서 가장 높았으며, 세 국가 모두 장기 기억효과가 있는 것으로 나타났는데, 지속적인(persistent) 성향은 일본국가가 가장 컸다.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.63-89
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• 2007

This study aims at empirically verifying whether long memory properties exist in returns and volatility of the financial time series and then, empirically observing influential factors of long-memory properties. The presence of long memory properties in the financial time series is examined with the Hurst exponent. The Hurst exponent is measured by DFA(detrended fluctuation analysis). The empirical results are summarized as follows. First, the presence of significant long memory properties is not identified in return time series. But, in volatility time series, as the Hurst exponent has the high value on average, a strong presence of long memory properties is observed. Then, according to the results empirically confirming influential factors of long memory properties, as the Hurst exponent measured with volatility of residual returns filtered by GARCH(1, 1) model reflecting properties of volatility clustering has the level of $H{\approx}0.5$ on average, long memory properties presented in the data before filtering are no longer observed. That is, we positively find out that the observed long memory properties are considerably due to volatility clustering effect.