• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.565-576
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• 2002

We propose a new sampling design for the 2001 Health Index Survey at Seoul. In this stratified two-stage sampling design, the ED(enumeration district) of 2000 Population and Housing Census is used as primary sampling unit and the Gu is used as stratification variable in order to obtain the sub-domain estimate for 25 Gu's as well as population estimate for Seoul. The sample ED's are systematically selected after the Ed's are ordered by location and property to obtain a representative sample. And also, the imputation methods for item nonresponses are suggested.

• The Korean Journal of Applied Statistics
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• v.1 no.1
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• pp.12-20
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• 1987

This paper aims at studing on a multi-purpose sampling design involving both a farm population survey and a livestock survey. Some features of this design are firstly to equalize the sampling errorsamong Shi-Gun (county) estimates, secondly, to make an efforts to increase the precision by introducing a purpose sample into the probability sample. There are two types of purpose samples according to sampling units; one is farm, the otheris enumeration district. A criterion of demarcation for selecting the farm purposesamle was investigated.

• Survey Research
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• v.8 no.2
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• pp.67-89
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• 2007

The Korea National Health and Nutrition Examination Survey(KNHANES) consists of Health Interview Survey, Health Behaviour Survey, Nutrition Survey, and Health Examination, and is designed to produce a broad range of descriptive health and nutritional statistics for sex and age subdomains of the population. These data can be used to measure and monitor the health and nutritional status of the population of Korea. The survey has been conducted three times from 1998. The Korea Centers for Disease Control and Prevention(KCDC) is preparing for the 4th survey which is to be conducted from 2007 through 2009. This study is to design a sample for the 4th survey. The main new feature of the sampling design is using a rolling sampling design method. Since KCDC has imposed some operational requirements, e,g., the needs of producing the annual national statistics and of year-round data collection by some regular staffs, a rolling sampling design method is introduced. This is the first time in history of applying a rolling sampling design for a national-wide large scale survey in Korea. Bringing in the rolling sampling, measurement variation due to different data collectors may be minimized.

• Advances in aircraft and spacecraft science
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• v.6 no.6
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• pp.529-550
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• 2019

The effect of multiple process parameters on a set of continuous response variables is, especially in experimental designs, difficult and intricate to determine. Due to the complexity in aeroacoustic and vibroacoustic studies, the often-performed simple one-factor-at-a-time method turns out to be the least effective approach. In contrast, the statistical Design of Experiments is a technique used with the objective to maximize the obtained information while keeping the experimental effort at a minimum. The presented work aims at giving insights on Design of Experiments applied to aeroacoustic and vibroacoustic problems while comparing different experimental designs and approximation models. For this purpose, an experimental rig of a ducted low-pressure fan is developed that allows gathering data of both, aerodynamic and aeroacoustic nature while analysing three independent process parameters. The experimental designs used to sample the design space are a Central Composite design and a Box-Behnken design, both used to model a response surface regression, and Latin Hypercube sampling to model an Artificial Neural network. The results indicate that Latin Hypercube sampling extracts information that is more diverse and, in combination with an Artificial Neural network, outperforms the quadratic response surface regressions. It is shown that the Latin Hypercube sampling, initially developed for computer-aided experiments, can also be used as an experimental design. To further increase the benefit of the presented approach, spectral information of every experimental test point is extracted and Artificial Neural networks are chosen for modelling the spectral information since they show to be the most universal approximators.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.8 s.251
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• pp.941-948
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• 2006

In this study, the new method for reliability estimation is proposed using kriging metamodel. Kriging metamodel can be determined by appropriate sampling range and sampling numbers because there are no random errors in the Design and Analysis of Computer Experiments(DACE) model. The first kriging metamodel is made based on widely ranged sampling points. The Advanced First Order Reliability Method(AFORM) is applied to the first kriging metamodel to estimate the reliability approximately. Then, the second kriging metamodel is constructed using additional sampling points with updated sampling range. The Monte-Carlo Simulation(MCS) is applied to the second kriging metamodel to evaluate the reliability. The proposed method is applied to numerical examples and the results are almost equal to the reference reliability.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.85-93
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• 1998

In general, the solvability of linear robust output regulation problem are not preserved under time-sampling. Thus, it is found that the digital regulator implemented by time-sampling of analog output regulator designed based on the continuous-time linear system model is nothing but a 1st order approximation with respect to time-sampling. However, one can design an improved sampled-data regulator with respect to sampling time by utilizing teh intrinsic structure of the system. In this paper, we study the system structures for which it is possible to design an improved sampled-data regulator with respect to sampling time.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.428-433
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• 1993

First, we propose a transparent and efficient design of discrete-time integral controllers accounting sampling skew. Based on the proposed controller, we derive a state-space representation of doubly coprime factorization including integral action. The representation is then used to obtain a convenient state-space parametrization of discrete-time two-degree-of-freedom integral controllers acconting sampling skew.

• Journal of Korean Society for Quality Management
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• v.23 no.4
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• pp.1-12
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• 1995

Sample skewness has not received much attention from researchers to design a control chart. In this paper, control charts based on two skewness measures are studied to control a manufacturing process. One skewness measure is the third central moment about mean, the other is the third L-moment which is a linear combination of order statistics. Since the exact sampling distributions of two skewness measures are unknown, empirical sampling distributions are studied using simulation. The sampling distributions are used to design control charts for skewness and performance of two skewness measures is compared.

• International Journal of Reliability and Applications
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• v.6 no.1
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• pp.31-39
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• 2005

An economic life test sampling plan for products with exponential lifetime distribution is developed. To reduce test time, a test plan with curtailed Type II censoring is considered. A cost model is constructed which involves three cost components; test cost, accept cost, and reject cost. Determination of optimal plan minimizing the expected average cost per lot is discussed with a constraint related to consumer's risk. Some numerical examples are provided.

• Journal of Korean Society of Forest Science
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• v.80 no.4
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• pp.379-382
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• 1991

Tree height at age 10 was used to estimate the statistical efficiencies of sampling size in the progeny test of Pinus koraiensis S. et Z. Experimental design was RCB design which consists of 25 half-sib families in each of three blocks. The number of families and blocks were fixed, therefore, the number of trees sampled per plot was the only factor that influences the environmental portion of the family mean height. Coefficient of variation, the estimate of the standard error of the family mean height, decreased with increase of sampling plot size, and became stable from 4-tree plot sampling (6.97%). The experimental error was significant from 7-tree sampling plot size. Nonlinear relationship (${\hat{Y}}=10.425e-^{0.073x}$ ; $R^2$=0.840) was found between the sampling plot size and the standard error of family mean height.