• Journal of Veterinary Clinics
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• v.28 no.1
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• pp.101-107
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• 2011

This study aimed to determine dose-response (DR) curve of avian influenza (AI) virus to predict the probability of illness or adverse health effects that may result from exposure to a pathogenic microorganism in a quantitative microbial risk assessment. To determine the parametric DR relationship of several strains of AI virus, 7 feeding trial data sets challenging humans (5 sets) and chickens (2 sets) for strains of H3N2 (4 sets), H5N1 (2 sets) and H1N1 (1 set) from the published literatures. Except for one data set (study with intra-tracheal inoculation for data set no. 6), all were obtained from the studies with intranasal inoculation. The data were analyzed using three types of DR model as the basis of heterogeneity in infectivity of AI strains in humans and chickens: exponential, beta-binomial and beta-Poisson. We fitted to the data using maximum likelihood estimation to get the parameter estimates of each model. The alpha and beta values of the beta-Poisson DR model ranged 0.06-0.19 and 1.7-48.8, respectively for H3N2 strain. Corresponding values for H5N1 ranged 0.464-0.563 and 97.3-99.4, respectively. For H1N1 the parameter values were 0.103 and 12.7, respectively. Using the exponential model, r (infectivity parameter) ranged from $1.6{\times}10^{-8}$ to $1.2{\times}10^{-5}$ for H3N2 and from $7.5{\times}10^{-3}$ to $4.0{\times}10^{-2}$ for H5N1, while the value was $1.6{\times}10^{-8}$ for H1N1. The beta-Poisson DR model provided the best fit to five of 7 data sets tested, and the estimated parameter values in betabinomial model were very close to those of beta-Poisson. Our study indicated that beta-binomial or beta-Poisson model could be the choice for DR modeling of AI, even though DR relationship varied depending on the virus strains studied, as indicated in prior studies. Further DR modeling should be conducted to quantify the differences among AI virus strains.

• Journal of Korean Society of Forest Science
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• v.103 no.4
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• pp.639-645
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• 2014

This study was conducted to evaluate the management efficiency of oak mushroom farms in Korea using the Data Envelopment Analysis (DEA), which is one of the non-parametric estimation methods. The data that was analyzed in this study was from the result of 2013 survey entitled "Standard Diagnostic Table for Oak Mushroom Management", which was conducted from March 2012 to October 2012. This survey was based on the inputs and outputs of 20 oak mushroom farms. Specifically, this study analyzed the technical efficiency, pure-technical efficiency and scale efficiency using CCR and BCC model of the DEA methods. Furthermore, this study compares the management efficiency between the full time oak mushroom production farms and part time oak mushroom production farms. Results showed that mean value for the technical efficiency was 0.655 which is considered as inefficient in general. For the pure-technical efficiency and scale efficiency, the mean values were 0.830 and 0.747, respectively which showed that inefficiency in the management was observed in the mushroom farms. Results also showed that there were seven farms with a total efficiency of 1, namely Decision Making Unit(DMU)2, DMU5, DMU6, DMU8, DMU10, DMU15 and DMU20. The management efficiency of DMU7 specifically the inputs for production was analyzed and compared to DMU5 and DMU6 and results showed that the DMU7 had an excessive inoculation and site development cost. Lastly, it was also observed that the full time mushroom production farms were more efficient as compared to the part time mushroom farms because of the lower scale efficiency value or smaller area for mushroom production allotted in the part time farms.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.139-152
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• 2015

Along with climate change, it is reported that the scale and frequency of extreme climate events show unstable tendency of increase. Thus, to comprehend the change characteristics of precipitation data, it is needed to consider non-stationary. The main objectives of this study were to estimate future design floods for Wonpyeongcheon watershed based on RCP (Representative Concentration Pathways) scenario. Wonpyeongcheon located in the Keum River watershed was selected as the study area. Historical precipitation data of the past 35 years (1976~2010) were collected from the Jeonju meteorological station. Future precipitation data based on RCP4.5 were also obtained for the period of 2011~2100. Systematic bias between observed and simulated data were corrected using the quantile mapping (QM) method. The parameters for the bias-correction were estimated by non-parametric method. A non-stationary frequency analysis was conducted with moving average method which derives change characteristics of generalized extreme value (GEV) distribution parameters. Design floods for different durations and frequencies were estimated using rational formula. As the result, the GEV parameters (location and scale) showed an upward tendency indicating the increase of quantity and fluctuation of an extreme precipitation in the future. The probable rainfall and design flood based on non-stationarity showed higher values than those of stationarity assumption by 1.2%~54.9% and 3.6%~54.9%, respectively, thus empathizing the necessity of non-stationary frequency analysis. The study findings are expected to be used as a basis to analyze the impacts of climate change and to reconsider the future design criteria of Wonpyeongcheon watershed.

• Journal of the Korean Wood Science and Technology
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• v.43 no.4
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• pp.456-462
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• 2015

365 pieces of domestic $38{\times}140{\times}3600mm$ Red pine structural lumber were machine graded conforming to a softwood structural lumber standard (KS F 3020). The allowable bending stresses calculated for each grade were compared with the values currently tabulated in the standard. Four calculation methods for lower $5^{th}$ percentile bending stress were non-parametric estimation with 75% confidence level, 2-parameter and 3-parameter Weibull distribution fit, and bending modulus of rupture (MOR)-modulus of elasticity (MOE) regression based method. Only the data set of Grades E8, E9, and E10 were statistically eligible for the $5^{th}$ percentile calculation. The MOR-MOE regression based method only was able to estimate the lower $5^{th}$ percentile values theoretically for the full range of grades. The results showed that all allowable bending stresses calculated were lower than the design values tabulated in the standard. This implies that the current machine grading system has the pitfall of structural safety. Improvement in current machine grading system could be achieved by introducing the bending strength and stiffness combination grade system.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.4
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• pp.204-211
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• 2011

A dynamic analysis procedure is developed to provide a better estimation of the dynamic responses of bridge during the passage of high speed railway vehicles. Particularly, a three dimensional numerical model including the structural interaction between high speed vehicles, bridges and railway endures to analyse accurately and evaluate with in-depth parametric studies for dynamic responses of various bridge span lengths running KTX railway locomotive up to increasing maximum speed(450km/h). Three dimensional frame element is used to model the simply supported pre-stressed concrete (PSC) box bridges for four span lengths(40~25m). Track irregularity employed as a stationary random process from the given spectral density functions and irregularities of both sides of the track are assumed to have high correlation. The high-speed railway vehicle (KTX) is used as 38-degree of freedom system. Three displacements (Vertical, lateral, and longitudinal) as well as three rotational components (Pitching, rolling, and yawing) are considered in the 38-degree of freedom model. The dynamic amplification factors are evaluated by the developed procedure under various traveling conditions, such as track irregularity camber, train speed and ballast. The dynamic analysis such as Newmark-${\beta}$ and Runge-Kutta methods which are able to analyse considering the dynamic impact factors are compared and contrasted.

• Journal of the Korean Geotechnical Society
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• v.30 no.7
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• pp.17-26
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• 2014

Tunneling-induced displacement in a jointed rock mass is an important factor to control tunnel stability and to secure a demanded space and construction quality. The magnitude of the inducible displacements is significantly affected by an elastic modulus and therefore, in a rock mass where a joint controls tunnel behavior, it is very important to estimate an elastic modulus of jointed rock mass reliably. Elastic modulus of jointed rock mass is affected by many factors such as rock type, joint condition, and loading condition. Nevertheless, most existing studies were focused on rough empirical relationships based on compressive loading conditions, which are different from tunnel excavation loading conditions, without a systematic approach of rock, joint, and loading conditions together. Therefore, this study considered rock and joint conditions systematically to estimate an elastic modulus of jointed rock mass under tunnel excavation loading. The controlled factors considered in this study are rock types and joint conditions (joint shear strength, joint inclination angle, number of joint sets, and joint spacing). Numerical parametric studies have been carried out with a consideration of different rock and joint conditions; the results have been compared with existing empirical relationships; and charts of elastic modulus change of different rock and joint conditions have been provided. The results are expected to have a great practical use for estimating the convergence induced by tunnel excavation in jointed rockmass.

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.753-766
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• 2014

The future mean sea level rise (MSLR) due to climate change in major harbors of Korean Peninsula has been estimated by some statistical methods in this article. Firstly, Mann-Kendall non-parametric trend test to find some trend in the observed long-term tidal data has been performed and also Bayesian change point analysis has been used also to detect the location of change points and their magnitude quantitatively. Especially, in this study, the results from Bayesian change point analysis have been applied to combine 4 future MSLR scenario projections with local MSLR data at 5 tidal gauges. This proposed procedure including Bayesian change point analysis results can improve the step for the determination of starting years of future MLSR scenario projections with 18.6-year lunar node tidal cycle and effectively consider local characteristics at each gauge. The final results by the proposed procedure in this study have shown that the future MSLR in Jeju region (Jeju tidal gauge) is in the largest increment and also the future MSLRs in Western region (Boryeong tidal gauge) and Southern region (Busan tidal gauge) are in the second largest one. Finally, it has been shown that the future MSLRs in Southern region (Yeosu tidal gauge) and Eastern region (Sokcho tidal gauge) seem to be in the relatively smallest growth among 5 gauges.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.921-932
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• 2015

This study analyzed military personnel survivability in regards to offensive operations according to the scientific military training data of a reinforced infantry battalion. Scientific battle training was conducted at the Korea Combat Training Center (KCTC) training facility and utilized scientific military training equipment that included MILES and the main exercise control system. The training audience freely engaged an OPFOR who is an expert at tactics and weapon systems. It provides a statistical analysis of data in regards to state-of-the-art military training because the scientific battle training system saves and utilizes all training zone data for analysis and after action review as well as offers training control during the training period. The methodologies used the Cox PH modeling (which does not require parametric distribution assumptions) and decision tree modeling for survival data such as CART, GUIDE, and CTREE for richer and easier interpretation. The variables that violate the PH assumption were stratified and analyzed. Since the Cox PH model result was not easy to interpret the period of service, additional interpretation was attempted through univariate local regression. CART, GUIDE, and CTREE formed different tree models which allow for various interpretations.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.543-559
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• 2019

Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.

• Informatization Policy
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• v.28 no.4
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• pp.76-92
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• 2021

The value of personal information is increasing with the digital transformation of the 4th Industrial Revolution. The purpose of this study is to analyze the efficiency of personal information protection efforts of 2,000 private companies. It uses a stochastic frontier approach (SFA), a parametric estimation method that measures the absolute efficiency of protective activities. In particular, the personal information activity index is used as an output variable for efficiency analysis, with the personal information protection budget and number of personnel utilized as input variables. As a result of the analysis, efficiency is found to range from a minimum of 0.466 to a maximum of 0.949, and overall average efficiency is 0.818 (81.8%). The main causes of inefficiency include non-fulfillment of personal information management measures, lack of system for promoting personal information protection education, and non-fulfillment of obligations related to CCTV. Policy support is needed to implement safety measures and perform personal information encryption, especially customized support for small and medium-sized enterprises.