• Proceedings of the KSR Conference
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• 2002.10b
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• pp.943-947
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• 2002

Volatile Organic Compounds(VOCs) contribute to the formation of ozone and PAN which are injurious to health through complex photochemical reactions. Growing consumption of fossil fuels results in significant emission of VOCs and other air pollutants into the atmosphere. This study was carried out to evaluate of Volatile Organic Compounds(VOCs) concentrations in Kumi industrial complex. Ambient air sampling was determined at five sites from August to September in 2002. The Volatile Organic Compounds samples were collected using the silicocan canisters, and were determined by GC/MS which connected preconcentrator system. The Kumi industrial complex of VOCs concentrations were generally similar with Yeochun's but some of VOCs concentrations of Kumi were higher than Yeochun industrial complex's. Also the industrial area was higher than downtown and residential district.

• Survey Research
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• v.5 no.2
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• pp.49-70
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• 2004

This paper is a broad review about general regression estimators, which are very useful when auxiliary variables are available in survey sampling. We investigate the process of development of general regression estimators from birth to suggestion of variance estimation method and examine some properties of general regression estimators by comparing with calibration and QR estimators. We also present some forms of general regression estimators available under complex sampling designs such as stratified sampling and cluster sampling. Finally, we comment some advantages as well as disadvantages of general regression estimators and theoretical and practical development in the future.

• Journal of Distribution Science
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• v.19 no.9
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• pp.41-52
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• 2021

Purpose: Virtual reality is a unique technology to distribute data and demonstrates user's understanding towards complex products. The objective of this research is to investigate the impact of virtual reality on real world purchase intention of automotive cutting tools in Thailand's exhibitions. Hence, the research framework was constructed by telepresence, perception narrative, authenticity, trustworthiness, functional value, aesthetics, and purchase intention. Research design, data and methodology: Samples were collected from 500 visitors who participated in the selected top two metalworking exhibitions. Mix sampling approach is applied by using non-probability sampling methods of purposive or judgmental sampling, quota sampling, and convenience sampling method, respectively to reach target samples. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used to analyze and confirm goodness-of-fit of the model and hypothesis testing. Results: The results indicate that authenticity, functional value, and trustworthiness induced higher experiential value towards purchase intention. Those variables are stimulated by telepresence and perception narrative towards VR experience. Conclusions: Consumer's purchase intention towards VR experience on engineering cutting tools rely on consumer's sense of authenticity, trustworthiness, and functional value. Hence, marketing practitioners in automotive companies are encouraged to develop VR which focusing on significant factors to enhance consumers purchase intention.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1217-1225
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• 2015

Survey research, data is commonly collected through a sample design with complex design features that allow the relative efficiency on the precision of an estimator to be measured using the concept of the design effect compared to simple random sampling as a reference design. This concept is most useful when the design effect can be expressed as a function of various design features. We propose a design effect formula suitable under a stratified multistage sampling by generalizing Gabler et al. (1999, 2006)'s approaches for multistage sampling. Its use can either guide improvement in the design efficiency when in design stage or enable the evaluation of the adopted design features afterwards.

• ETRI Journal
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• v.14 no.4
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• pp.19-27
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• 1992

Since. as is well known, direct computation of the reliability for a large-scaled and complex net work generally requires exponential time, a variety of alternative methods to estimate the network reliability using simulation have been proposed. Monte Carlo sampling is the major approach to estimate the network reliability using simulation. In the paper, a dynamic Monte Carlo sampling method, called conditional minimal cut set (CMCS) algorithm, is suggested. The CMCS algorithm simulates a minimal cut set composed of arcs originated from the (conditional) source node until s-t connectedness is confirmed, then reduces the network on the basis of the states of simulated arcs. We develop the importance sampling estimator and the total hazard estimator and compare the performance of these simulation estimators. It is found that the CMCS algorithm is useful in reducing variance of network reliability estimator.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.93-99
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• 2013

Fractal theory has been adopted as an effective tool for modelling complex and irregular natural phenomena facing in the fields of Computer Science, Engineering, Medical, Climatology and so on. In this paper, we presents an algorithm which enhances the performance of the triangular prism method(TPM) which has been widely used for fractal dimension extraction of natural terrains and images. For this, existing sampling methods are analyzed and a new sampling method which takes their merits is proposed. The effectiveness of the proposed algorithm is tested on fractal terrain maps and its performance is compared with that of other methods.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.179-188
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• 1996

복합조사표본추출법(complex survey sampling)에 근거한 분할표분석에 카이제곱검정법을 사용할 때의 문제점들과 해결방법들을 살펴보았다. 나아가, 군집표본추출의 경우에 붓스트랩방법의 타당성을 보였으며, 실제자료분석을 통하여 실제 활용가능성과 잇점을 제시하였다.

• Journal of the Korean housing association
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• v.25 no.6
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• pp.39-47
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• 2014

The background of this study is to examine the structure of housing price of which characteristics are not equal but hierarchical in the apartment complexes. So, the purpose of this study is to analyze the influences of apartment complex characteristics on the housing price within the same regional boundary by HLM. The data used as dependent variables were the market prices of 938 units from 29 apartment complexes by stratified sampling. The 2nd level independent variables is the Housing complex characteristics which are composed of the housing complex & locational variables and the 1st level independent variables are the unit characteristics. The results are as follows. First, the first model shows that the 2nd level variables explains 68% of the housing prices. Second, the influential variables of the 1st level unit variable are 'dwelling exclusive area', 'floor of dwelling' and 'direction of dwelling'. Third, the influential variables of the housing complex variables in the 2nd level are 'lot area', 'the building-to-land ratio', 'the number of unit', 'the number of parking lots per unit', 'Green space area' and 'open space area per unit'. The last, the influential variables of the housing locational variables in the 2nd level are 'distance to subway and park' and the number of school and park within a radius of 1km.

• Water for future
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• v.26 no.2
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• pp.79-87
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• 1993

A new method to estimate the mean areal precipitation using kriging is developed. Unlike the conventional approach, points for double and quadruple numerical integrations in the kriging equation are selected randomly, given the boundary of area of interest. This feature eliminates the conventional approach's necessity of dividing the area into subareas and calculating the center of each subarea, which in turn makes the developed method more powerful in the case of complex boundaries. The algorithm to select random points within an arbitrary boundary, based on the theory of complex variables, is described. The results of Monte Carlo simulation showed that the error associated with estimation using randomly selected points is inversely proportional to the square root of the number of sampling points.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.131-136
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• 1997

This paper represent the variable structure adaptive mode control technique which is new approach to implement the robust control of industrial robot manipulator with external disturbances and parameter uncertainties. Sliding mode control is a well-known technique for robust control of uncertain nonlinear systems. The robustness of sliding model controllers can be shown in contiuous time, but digital implementation may not preserve robustness properties because the sampling process limits the existence of a true sliding mode. the sampling process often forces the trajectory to oscillate in the neighborhood of the sliding surface. Adaptive control technique is particularly well-suited to robot manipulators where dynamic model is highly complex and may contain unknown parameters. Adaptive control algorithm is designed by using the principle of the model reference adaptive control method based upon the hyperstability theory. The proposed control scheme has a simple sturcture is computationally fast and does not require knowledge of the complex dynamic model or the parameter values of the manipulator or the payload. Simulation results show that the proposed method not only improves the performance of the system but also reduces the chattering problem of sliding mode control, Consequently, it is expected that the new adaptive sliding mode control algorithm will be suited for various practical applications of industrial robot control system.