• Journal of KIISE:Computer Systems and Theory
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• v.37 no.6
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• pp.354-367
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• 2010

There are many methods for analyzing patterns in time series data. Although stock data represents a time series, there are few studies on stock pattern analysis and prediction. Since people believe that stock price changes randomly we cannot predict stock prices using a scientific method. In this paper, we measured the degree of the randomness of stock prices using Kolmogorov complexity, and we showed that there is a strong correlation between the degree and the accuracy of stock price prediction using our semi-global alignment method. We transformed the stock price data to quantized string sequences. Then we measured randomness of stock prices using Kolmogorov complexity of the string sequences. We use KOSPI 690 stock data during 28 years for our experiments and to evaluate our methodology. When a high Kolmogorov complexity, the stock price cannot be predicted, when a low complexity, the stock price can be predicted, but the prediction ratio of stock price changes of interest to investors, is 12% prediction ratio for short-term predictions and a 54% prediction ratio for long-term predictions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.852-860
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• 2014

This paper proposes an efficient algorithm based on Kolmogorov-Smirnov test to determine the presence of impulsive noise in the given environment. Kolmogorov-Smirnov and Chi-Square tests are known in the literature to serve as a goodness-of-fit test especially for a testing for normality of the distribution. But these algorithms are difficult to implement in practice due to high complexity. The proposed algorithm gives a significant reduction of the computational complexity while decreasing the error probability of hypothesis test, which is shown in the simulation results. Also, it is worth noting that the proposed algorithm is not dependent on the noise environment.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.136-136
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• 2019

원형 실린더를 주변 흐름에 관한 연구는 오랜 기간 유체역학 전 영역에서 모형실험이나 수치모형으로 광범위하게 연구되었다. 이 흐름은 하천의 교각이나, 바다의 시추선과 같은 수공구조물 주변에서 관측된다. 난류와 와류가 공존하는 복잡한 특성 때문에, 이 흐름은 수공학에서 유사이송, 세굴, 오염물 확산 등에 영향을 준다. 본 연구는 실험실 수로에 설치된 원형 실린더(D=9cm) 후방의 근접 와류 구간에서(x/D<5) 유속을 ADV로 측정한 후, 난류 특성을 Power Spectral Estimation(PSE)와 Singular Spectral Analysis(SSA) 방법으로 연구하였다. PSE는 샘플 스펙트럼의 한계를 보완하고자 자료를 분할하고, window 함수를 적용하여 ensemble 평균을 구하는 경험적 방법이다. PSE를 이용하여 스펙트럼을 계산한 결과, 주 흐름 및 횡방향 흐름은 Inertial subrange에서 Kolmogorov의 가정과 일치하는 추세를 보였다. 그러나 수심방향 흐름의 스펙트럼은 -5/3보다 빠르게 감소하는 추세를 보였다. Inertial subrange 스펙트럼에서 난류 에너지 소산율은 원형 실린더에서 멀어짐에 따라 감소하는 추세를 보였고, 주 흐름방향과 횡방향 흐름은 비슷한 크기를 보였다. 난류 에너지 소산율과 동점성계수를 이용하여 Kolmogorov 길이, 유속, 시간 스케일을 계산했다. 난류의 운동에너지를 계산하기 위해 Triple decomposition 방법 중 하나인 SSA를 적용하였다. SSA는 유속행렬을 이용하여 고윳값과 고유벡터를 계산하고, 유속에서 기여도가 큰 부분을 추출하는 방법이다. SSA를 통해 실린더 후방 흐름에서 와류 성분과 난류 성분을 나누었다. 횡방향 흐름은 강한 와류로 큰 기여도를 갖는 고유벡터가 나타났지만, 주 흐름과 수심방향 흐름은 상대적으로 낮은 기여도를 갖는 고유벡터가 나타났다. 와류를 제외한 흐름에서 난류 운동에너지는 실린더와 멀어짐에 따라 감소하고, 흐름 중앙에서(y/D=0) 가장 큰 값을 보였다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.797-810
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• 2017

The Korean peninsula is exposed to typhoons every year. Typhoons cause huge socioeconomic damage because tropical cyclones tend to occur with strong winds and heavy precipitation. In order to understand the complex dependence structure between strong winds and heavy precipitation, the copula links a set of univariate distributions to a multivariate distribution and has been actively studied in the field of hydrology. In this study, we carried out analysis using data of wind speed and precipitation collected from the weather stations in Busan and Jeju. Log-Normal, Gamma, and Weibull distributions were considered to explain marginal distributions of the copula. Kolmogorov-Smirnov, Cramer-von-Mises, and Anderson-Darling test statistics were employed for testing the goodness-of-fit of marginal distribution. Observed pseudo data were calculated through inverse transformation method for establishing the copula. Elliptical, archimedean, and extreme copula were considered to explain the dependence structure between strong winds and heavy precipitation. In selecting the best copula, we employed the Cramer-von-Mises test and cross-validation. In Busan, precipitation according to average wind speed followed t copula and precipitation just as maximum wind speed adopted Clayton copula. In Jeju, precipitation according to maximum wind speed complied Normal copula and average wind speed as stated in precipitation followed Frank copula and maximum wind speed according to precipitation observed Husler-Reiss copula.