• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.245-255
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• 2011

There are two nonparametric methods that use empirical distribution functions and probability density estimators for the test of the distribution change of data. In this paper we investigate the two methods precisely and summarize the results of previous research. We assume several probability models to make a simulation study of the change point analysis and to examine the finite sample behavior of the two methods. Empirical powers are compared to verify which is better for each model.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.279-289
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• 2010

In this study, we propose a simple statistical model for determining the admission or discharge of 668 patients with a chief complaint of dyspnea. For this, we use 11 explanatory variables which are chosen to be important by clinical experts among 55 variables. As a modification process, we determine the discharge interval of each variable by the kernel density functions of the admitted and discharged patients. We then choose the optimal model for determining the discharge of patients based on the number of explanatory variables belonging to the corresponding discharge intervals. Since the numbers of the admitted and discharged patients are not balanced, we use, as the criteria for selecting the optimal model, the arithmetic mean of sensitivity and specificity and the harmonic mean of sensitivity and precision. The selected optimal model predicts the discharge if 7 or more explanatory variables belong to the corresponding discharge intervals.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.1-7
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• 2009

An approach for the classification of music genres using a Fuzzy c-Means(FcM) with divergence-based kernel is proposed and presented in this paper. The proposed model utilizes the mean and covariance information of feature vectors extracted from music data and modelled by Gaussian Probability Density Function (GPDF). Furthermore, since the classifier utilizes a kernel method that can convert a complicated nonlinear classification boundary to a simpler linear one, he classifier can improve its classification accuracy over conventional algorithms. Experiments and results on collected music data sets demonstrate hat the proposed classification scheme outperforms conventional algorithms including FcM and SOM 17.73%-21.84% on average in terms of classification accuracy.

• Journal of the Korean Chemical Society
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• v.25 no.5
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• pp.291-299
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• 1981

A test particle kinetic theory for reaction dynamics in liquids is presented at the repeated ring collision level for the hard sphere model. A kinetic equation for the equilibrium time correlation function of the reactive test particle phase space density is derived and the rate kernel expression for the reversible chemical reaction of the type A +B ${\rightleftharpoons$ C + D in the presence of inert solvent S is obtained by the projection operator method.

• Journal of Ship and Ocean Technology
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• v.11 no.4
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• pp.29-54
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• 2007

Systematic sensitivity analysis of smoothed particle hydrodynamics method (SPH), a gridless Lagrangian particle method, was carried out in this study. Unlike traditional grid-based numerical schemes, systematic sensitivity study for computational parameters is very limited for SPH. In this study, the effect of computational parameters in SPH simulation is explored through two-dimensional dam-breaking and sloshing problem. The parameters to be considered are the speed of sound, the type of kernel function, the frequency of density re-initialization, particle number, smoothing length and pressure extraction method. Through a series of numerical test, detailed information was obtained about how SPH solution can be more stabilized and improved by adjusting computational parameters.

• ETRI Journal
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• v.44 no.5
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• pp.769-779
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• 2022

Spectral clustering has become a typical and efficient clustering method used in a variety of applications. The critical step of spectral clustering is the similarity measurement, which largely determines the performance of the spectral clustering method. In this paper, we propose a novel spectral clustering algorithm based on the local similarity measure of shared neighbors. This similarity measurement exploits the local density information between data points based on the weight of the shared neighbors in a directed k-nearest neighbor graph with only one parameter k, that is, the number of nearest neighbors. Numerical experiments on synthetic and real-world datasets demonstrate that our proposed algorithm outperforms other existing spectral clustering algorithms in terms of the clustering performance measured via the normalized mutual information, clustering accuracy, and F-measure. As an example, the proposed method can provide an improvement of 15.82% in the clustering performance for the Soybean dataset.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.381-381
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• 2017

수자원 분야에 대한 기후변화의 영향은 홍수, 가뭄 등 극치 수문사상의 증가와 변동성 확대를 초래하는 것으로 알려져 있으며, 이에 따라 예년에 비해 발생빈도 및 심도가 증가한 가뭄에 대한 모니터링 및 피해경감을 위해 정부에서는 국민안전처를 비롯한 관계기관 합동으로 생활 공업 농업용수 등 분야별 가뭄정보를 제공하고 있다. 국토교통부와 환경부는 생활 및 공업용수 분야의 가뭄정보 제공을 위해 광역 지방 상수도를 이용하는 급수 지역과 마을상수도, 소규모급수시설 등 미급수지역의 용수수급 정보를 분석하여 가뭄 분석정보를 제공 중에 있다. 하지만, 미급수지역에 대한 가뭄 예?경보는 기준이 되는 수원정보의 부재로 기상 가뭄지수인 SPI6를 이용하여 정보를 생산하고 있다. 기상학적 가뭄 상황과 물부족에 의한 체감 가뭄은 차이가 있으며, 미급수 지역의 경우 지하수를 주 수원으로 사용하는 지역이 대부분으로 기상학적 가뭄지수인 SPI6를 이용한 가뭄정보로 실제 물수급 상황을 반영하기는 부족한 실정이다. 따라서 본 연구에서는 미급수지역의 주요 수원인 지하수의 수위 상황을 반영한 가뭄모니터링 기법을 개발하고자 하였으며, 가용량 분석이 현실적으로 어려운 지하수의 특성을 고려하여 수위 거동의 통계적 분석을 통해 가뭄을 모니터링 할 수 있는 방법으로 접근하였다. 국가지하수관측소 중 관측기간이 10년 이상이고 강우와의 상관성이 높은 관측소들을 선정한 후, 일수위 관측자료를 월별로 분리하여 1월~12월 각 월에 대해 핵밀도 함수 추정기법(kernel densitiy estimation)을 적용하여 월별 지하수위 분포 특성을 도출하였다. 각 관측소별 관측수위 분포에 대해 백분위수(percentile)를 이용하여, 25%~100% 사이는 정상, 10%~25% 사이는 주의단계, 5%~10% 사이는 심한가뭄, 5% 이하는 매우심함으로 가뭄의 단계를 구분하였다. 각 백분위수에 해당하는 수위 값은 추정된 Kernel Density와 Quantile Function을 이용하여 산정하였고, 최근 10일 평균수위를 현재의 수위로 설정하여 가뭄의 정도를 분류하였다. 분석된 결과는 관측소를 기점으로 역거리가중법(inverse distance weighting)을 통해 공간 분포를 시켰으며, 수문학적, 지질학적 동질성을 반영하기 위하여 유역도 및 수문지질도를 중첩한 공간연산을 통해 전국 지하수 가뭄상태를 나타내는 지하수위 등급분포도를 작성하였다. 실제 가뭄상황과의 상관성을 분석하기 위해 언론기사를 통해 확인된 가뭄시기와 백문위수 25%이하로 분석된 지하수 가뭄시기를 ROC(receiver operation characteristics) 분석을 통해 비교 검증하였다.

• Journal of Korea Water Resources Association
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• v.45 no.3
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• pp.275-290
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• 2012

In this study, we have developed an optimal time distribution model through extraction of peaks over threshold (POT) series. The median values for annual maximum rainfall dataset, which are obtained from the magnetic recording (MMR) and the automatic weather system(AWS) data at Seoul meteorological observatory, were used as the POT criteria. We also suggested the improved methodology for the time distribution of extreme rainfall compared to Huff method, which is widely used for time distributions of design rainfall. The Huff method did not consider changing in the shape of time distribution for each rainfall durations and rainfall criteria as total amount of rainfall for each rainfall events. This study have suggested an extracting methodology for rainfall events in each quartile based on interquartile range (IQR) matrix and selection for the mode quartile storm to determine the ranking cosidering weighting factors on minutely observation data. Finally, the optimal time distribution model in each rainfall duration was derived considering both data size and characteristics of distribution using kernel density function in extracted dimensionless unit rainfall hyetograph.

• Journal of Internet Computing and Services
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• v.6 no.3
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• pp.159-170
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• 2005

OC-SVM(One Class Support Vector Machine) avoids solving a full density estimation problem, and instead focuses on a simpler task, estimating quantiles of a data distribution, i.e. its support. OC-SVM seeks to estimate regions where most of data resides and represents the regions as a function of the support vectors, Although OC-SVM is powerful method for data description, it is difficult to incorporate human subjective importance into its estimation process, In order to integrate the importance of each point into the OC-SVM process, we propose a fuzzy version of OC-SVM. In FOC-SVM (Fuzzy One-Class Support Vector Machine), we do not equally treat data points and instead weight data points according to the importance measure of the corresponding objects. That is, we scale the kernel feature vector according to the importance measure of the object so that a kernel feature vector of a less important object should contribute less to the detection process of OC-SVM. We demonstrate the performance of our algorithm on several synthesized data sets, Experimental results showed the promising results.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.