• Water Engineering Research
• /
• v.2 no.1
• /
• pp.1-10
• /
• 2001

The frequency analyses for the precipitation data in Korea were performed. We used daily maximum series, monthly maximum series, and annual series. For nonparametric frequency analyses, variable kernel estimators were used. Nonparametric methods do not require assumptions about the underlying populations from which the data are obtained. Therefore, they are better suited for multimodal distributions with the advantage of not requiring a distributional assumption. In order to compare their performance with parametric distributions, we considered several probability density functions. They are Gamma, Gumbel, Log-normal, Log-Pearson type III, Exponential, Generalized logistic, Generalized Pareto, and Wakeby distributions. The variable kernel estimates are comparable and are in the middle of the range of the parametric estimates. The variable kernel estimates show a very small probability in extrapolation beyond the largest observed data in the sample. However, the log-variable kernel estimates remedied these defects with the log-transformed data.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.408-413
• /
• 2005

A new methodology for discrete non-Gaussian probability density estimation is investigated in this paper based on a dynamic Bayesian network (DBN) and kernel functions. The estimator consists of a DBN in which the transition distribution is represented with kernel functions. The estimator parameters are determined through a recursive learning algorithm according to the maximum likelihood (ML) scheme. A discrete-type Poisson distribution is generated in a simulation experiment to evaluate the proposed method. In addition, an unknown probability density generated by nonlinear transformation of a Poisson random variable is simulated. Computer simulations numerically demonstrate that the method successfully estimates the unknown probability distribution function (PDF).

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.2B
• /
• pp.225-236
• /
• 2008

The techniques to calculate the probability precipitation for the design of hydrological projects can be determined by the point frequency analysis and the regional frequency analysis. Probability precipitation usually calculated by point frequency analysis using rainfall data that is observed in rainfall observatory which is situated in the basin. Therefore, Probability precipitation through point frequency analysis need observed rainfall data for enough periods. But, lacking precipitation data can be calculated to wrong parameters. Consequently, the regional frequency analysis can supplement the lacking precipitation data. Therefore, the regional frequency analysis has weaknesses compared to point frequency analysis because of suppositions about probability distributions. In this paper, rainfall observatory in Korea did grouping by cluster analysis using position of timely precipitation observatory and characteristic time rainfall. Discordancy and heterogeneity measures verified the grouping precipitation observatory by the cluster analysis. So, there divided rainfall observatory in Korea to 6 areas, 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. At the results, the regional frequency analysis of the variable kernel function can utilize for decision difficulty of suitable probability distribution in other methods.

• Journal of Korea Water Resources Association
• /
• v.41 no.3
• /
• pp.265-276
• /
• 2008

The observed data of enough period need for design of hydrological works. But, most hydrological data aren't enough. Therefore in this paper, hourly precipitation generated by nonhomogeneous Markov chain model using variable Kernel density function. First, the Kernel estimator is used to estimate the transition probabilities. Second, wet hours are decided by transition probabilities and random numbers. Third, the amount of precipitation of each hours is calculated by the Kernel density function that estimated from observed data. At the results, observed precipitation data and generated precipitation data have similar statistic. Also, rainfall mass curve is derived by calculated transition probabilities for generation of hourly precipitation.

• Journal of Korea Water Resources Association
• /
• v.39 no.10 s.171
• /
• pp.891-904
• /
• 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 the Korean Society of Visualization
• /
• v.21 no.1
• /
• pp.94-102
• /
• 2023

Three design parameters were considered in this study: outlet nozzle angle (30°, 60°, 80°), neck length (1 mm, 3 mm), and flow rate (0.5, 0.6, 0.7, 0.8 lpm). A neck diameter of 0.5 mm induced cavitation flow at a venture nozzle. A secondary transparent chamber was connected after ejection to increase bubble duration and shape visibility. The bubble size was estimated using a Gaussian kernel function to identify bubbles in the acquired images. Data on bubble size were used to obtain Sauter's mean diameter and probability density function to obtain specific bubble state conditions. The degree of bubble generation according to the bubble size was compared for each design variable. The bubble diameter increased as the flow rate increased. The frequency of bubble generation was highest around 20 ㎛. With the same neck length, the smaller the CV number, the larger the average bubble diameter. It is possible to increase the generation frequency of smaller bubbles by the cavitation method by changing the magnification angle and length of the neck. However, if the flow rate is too large, the average bubble diameter tends to increase, so an appropriate flow rate should be selected.

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