• Journal of the Korean Institute of Gas
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• v.21 no.3
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• pp.61-69
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• 2017

The commercial development of unconventional gas is pursued in North America because it is more feasible owing to the technology required to improve productivity. Shale reservoir have low permeability and gas production can be carried out through cracks generated by hydraulic fracturing. The decline rate during the initial production period is high, but very low latter on, there are significant variations from the initial production behavior. Therefore, in the prediction of the production rate using deterministic decline curve analysis(DCA), it is not possible to consider the uncertainty in the production behavior. In this study, production rate of the Eagle Ford shale is predicted by Arps Hyperbolic and Modified SEPD. To minimize the uncertainty in predicting the Estimated Ultimate Recovery(EUR), Monte Carlo simulation is used to multi-wells analysis. Also, kernel density function is applied to determine probability distribution of decline curve factors without any assumption.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.55-63
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• 2019

In engineering problems, many random variables have correlation, and the correlation of input random variables has a great influence on reliability analysis results of the mechanical systems. However, correlated variables are often treated as independent variables or modeled by specific parametric joint distributions due to difficulty in modeling joint distributions. Especially, when there are insufficient correlated data, it becomes more difficult to correctly model the joint distribution. In this study, multivariate kernel density estimation with bounded data is proposed to estimate various types of joint distributions with highly nonlinearity. Since it combines given data with bounded data, which are generated from confidence intervals of uniform distribution parameters for given data, it is less sensitive to data quality and number of data. Thus, it yields conservative statistical modeling and reliability analysis results, and its performance is verified through statistical simulation and engineering examples.

• Structural Engineering and Mechanics
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• v.75 no.6
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• pp.771-784
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• 2020

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.

• Journal of Korea Water Resources Association
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• v.39 no.10 s.171
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• pp.891-904
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• 2006

The estimation of the probability precipitation is essential for the design of hydrologic projects. The techniques to calculate the probability precipitation can be determined by the point frequency analysis and the regional frequency analysis. The regional frequency analysis includes index-flood technique and L-moment technique. In the regional frequency analysis, even if the rainfall data passed homogeneity, suitable distributions can be different at each point. However, the regional frequency analysis can supplement the lacking precipitation data. Therefore, the regional frequency analysis has weaknesses compared to parametric point frequency analysis because of suppositions about probability distributions. Therefore, this paper applies kernel density function to precipitation data so that homogeneity is defined. In this paper, The data from 16 rainfall observatories were collected and managed by the Korea Meteorological Administration to achieve the point frequency analysis and the regional frequency analysis. The point frequency analysis applies parametric technique and nonparametric technique, and the regional frequency analysis applies index-flood techniques and L-moment techniques. Also, the probability precipitation was calculated by the regional frequency analysis using variable kernel density function.

• Journal of Korean Neurosurgical Society
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• v.45 no.5
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• pp.284-288
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• 2009

Objective : Our retrospective study aimed to determine whether 16-slice computerized tomography (CT) angiography optimized sharp kernel is suitable for the evaluation of visibility, luminal patency and re-stenosis of intracranial stents in comparison with conventional angiography. Methods : Fifteen patients with symptomatic intracranial stenotic lesions underwent balloon expandable stent deployment of these lesions (10 middle cerebral arteries, 2 intracranial vertebral arteries, and 3 intracranial internal carotid arteries). CT angiography follow-up ranged from 6 to 15 months (mean follow-up, 8 months) after implantation of intracranial stents and conventional angiography was confirmed within 2 days. Curved multiplanar reformations with maximal intensity projection (MIP) with optimal window settings for assessment of lumen of intracranial stents were evaluated for visible lumen diameter, stent patency (contrast distal to the stent as an indirect sign), and re-stenosis by two experienced radiologists who blinded to the reports from the conventional angiography. Results : All of stents deployed into symptomatic stenotic lesions. All stents were classified as patent and no re-stenosis, which was correlated with results of conventional angiography. Parts of the stent lumen could be visualized in all cases. On average, 57% of the stent lumen diameter was visible using optimized sharp kernel. Significant improvement of lumen visualization (22%, p<0.01) was observed using the optimized sharp kernel compared with the standard sharp kernel. Inter-observer agreements on the measurement of lumen diameter and density were judged as good, respectively (p<0.05). Conclusion : Sixteen-slice CT using the optimized sharp kernel may provide a useful information for evaluation of lumen diameter patency, and re-stenosis of intracranial stents.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.341-350
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• 2021

Purpose: The purpose of this study is to present an analysis method to select priorities for areas where the traffic safety system is applied to reduce pedestrian accidents. Method: Using Kernel density analysis using the coordinate information of the accident point, we performed density analysis of elderly walking accidents and elderly jaywalking accidents, and analysis of the weight of two types of walking accidents. Result: As a result of density analysis of the weight considering elderly jaywalking accidents, it was analyzed that the density of pedestrian traffic accidents for th elderly was higher in Gunsan-si, Jeongeup-si, and Gimje -si compared to Jeonju-si, where the number of elderly pedestrian accidents were high. Conclusion: The analysis results of this study are judged to be possible to use objective indicators for the selection of target sites for the introduction of the traffic safety system.

• Land and Housing Review
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• v.14 no.2
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• pp.35-50
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• 2023

The purpose of this study is to identify areas in need of urban renewal by utilizing spatial data and analyzing their types and characteristics. For this, this research employed a kernel density function and K-means cluster analysis with spatial data, through which it sought ways to identify high-demand areas for urban renewal projects. The key findings and implications of the research are summarized as follows. Firstly, this research classified 587 target sites in Seoul based on development density (ratios) and an indicator for aged buildings. Approximately half of these areas were consistent with leading pilot project sites and Accelerated Integration Sites. Secondly, it was observed that residential environments in the designated leading pilot project sites, as decided by public sectors, were relatively poor compared to other areas. Lastly, the target areas for urban renewal were not clearly categorized through statistical analysis. Instead, it was found that categorization should be made depending on the requirements of each project.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.457-465
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• 2011

Sample entropy (Vasicek, 1976) has poor performance, and several nonparametric entropy estimators have been proposed as alternatives. In this paper, we consider a piecewise uniform density function based on quantiles, which enables us to evaluate entropy in each interval, and study the poor performance of the sample entropy in terms of the poor estimation of lower and upper quantiles. Then we propose some improved entropy estimators by simply modifying the quantile estimators, and compare their performances with some existing estimators.

• Journal of the Korean Statistical Society
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• v.18 no.2
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• pp.107-117
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• 1989

A stronger result than that of Park and Marron (1994) is proved here on the asymptotic distribution of the plug-in bandwidth selector. The new result is that the plug-in bandwidth selector may have the rate of convergence ($n^{-4/13}$ with less smoothness conditions on the unknown density functions than as described in Park and Marron's paper. Together with this, a class of various plug-in bandwidth selectors are considered and their asymptotic distributions are given. Finally, some ideas of possible improvements on those plug-in bandwidth selectors are provided.

• The Pure and Applied Mathematics
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• v.1 no.1
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• pp.19-24
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• 1994

Let P be a probability measure on the real line with Lebesque-density f. The usual estimator of the distribution function (≡df) of P for the sample $\chi$$_1$,…, $\chi$$\_$n/ is the empirical df: F$\_$n/(t)=(equation omitted). But this estimator does not take into account the smoothness of F, that is, the existence of a density f. Therefore, one should expect that an estimator which is better adapted to this situation beats the empirical df with respect to a reasonable measure of performance.(omitted)