• 품질경영학회지
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• 제23권3호
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• pp.113-125
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• 1995

This paper describes the 2-stage randomized response model in the Bayesian view point. The classical Bayesian analysis needs the complete information for a prior density, but the Bayes linear estimator needs only the first and second moments. Therefore, it is convenient to find the estimator and this estimator robusts to a prior density. We show that MSE's of the Bayes linear estimators for the 2-stage randomized response models are smaller than those of the MLE's for the 2-stage randomized response models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 추계학술대회 논문집 전력기술부문
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• pp.384-386
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• 2002

As restructuring in power industry has introduced competitive markets, a new method on demand side management has been developed. Many programs using the method were developed with providing several choices for customer. Nowadays the programs are called demand response as the load management is done by customer's responding to the market price signal. It was proven that the method was effective for demand control with the active consumer's attending for the program. This paper analyses the perspective and the requirement for designing the demand response system.

• 품질경영학회지
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• 제7권2호
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• pp.22-28
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• 1979

The problem considered in this paper is to select the vital factor effect to the product quality through the experimental design and analysis of response surface, so as to control the quality improvement of industrial product. In this time, even through the mathematical model is unknown it could be applicable to control the quality of industrial products and to determine optimum operating condition for many technical fields, particulary, for industrial manufacturing process. When a set of data is available from an experimental design, it is often of interest 1:0 fit polynominal repression model in independent variables (eg, time, temperature, pressure, etc) the optimize the response variable (eg. yield, strength etc). This paper proposes a method known to obtain the optimum operating condition, and how to find the condition by using table of orthogonal array experiments, and optimization methodology of statistical model. A criterion can be applied determining to optimum operating conditions in manufacturing industry and improving the fit of response surface which may be used for prediction of responses and quality control of industrial products.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2009년도 추계학술대회
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• pp.15-22
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• 2009

Quality variations in mixture products such as medicine, food, engineering chemicals, and alloy materials can be caused by their own sub-components. For instance, discharging characteristics of a lithium-ion rechargeable battery depend upon the mixture ratio of ethylene, dimethyle, and ethyle-methyle, all of which consists an electrolyte solution in the battery. Thus it is important to determine tolerances of mixture components in maintaining the product quality at a desired level. This paper proposes a simple but efficient approach to a mixture tolerancing method with multi-response variables. We use a response surface method for empirical modelling between mixture components. An illustrative example of the proposed method is given.

• 품질경영학회지
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• 제38권3호
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• pp.413-424
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• 2010

Mixture experiments involve combining ingredients or components of a mixture and the response is a function of the proportions of ingredients which is independent of the total amount of a mixture. The purpose of the mixture experiments is to find the optimum blending at which responses such as the flavor and acceptability are maximized. We assume the quadratic or special cubic canonical polynomial model over the experimental region for a mixture since the current mixture is assumed to be located in the neighborhood of the optimal mixture. The cost of the mixture is proportional to the cost of the ingredients of the mixture and is the linear function of the proportions of the ingredients. In this paper, we propose mixture response surface methods to develop a mixture such that the cost is down more than ten percent as well as mean responses are as good as those from the current mixture. The proposed methods are illustrated with the well known the flare experimental data described by McLean and Anderson(1966).

• 보건행정학회지
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• 제28권3호
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• pp.308-314
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• 2018

Injury is a serious problem that not only causes death but also significantly degrades the quality of life of the people and causes loss of socioeconomic opportunities and costs. Damage occurs as a result of an accident. Among them, natural disasters and artificial disasters take lives of many people in a short time and threaten their physical and mental health. The United States has responded to the disaster by establishing relevant laws and regulations and a response system with the recognition that health is recognised soon to be as national security in the wake of the 9/11 terrorist attacks and the Katrina disaster. It is necessary to build a knowledge infrastructure to train disaster response experts in public health area and to have health competence to cope with disasters.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.123-126
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• 2008

This research develops a methodology for standard design of spatial Database utilizing the disaster ethnography. Especially, the disaster response operation is sensitive to the size of the disaster, location, damage situation, resource a variability, etc. Moreover, there are many unknown and unexpected factors that will affect the disaster response strategy. But, the future Crisis Management Systems is needed that past disaster teaching. In another words, from now on the response systems need to prepare several scenarios and spatial data and manual etc. before the disaster. Then, this research is the experimental research which examined the relationship between the disaster-ethnography and the GIS spatial data of disaster.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2001년도 학술대회지
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• pp.65-72
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• 2001

최근 들어 건설환경이 더욱 복잡해지고 경쟁이 치열해짐에 따라 위험관리의 중요성이 더욱 부각되고 있다. 그러나 건설공사에 적용되는 대부분의 위험관리 기법들이 사업초기단계에서 위험분석에 집중되어 있어, 건설과정에서 공정관리, 원가관리 또는 품질관리처럼 일상적인 관리기법으로 개발되어 있지 않다. 본 논문에서는 건설공사에서 위험관리를 일상적인 관리기법으로 적용하기 위하여 위험허용도 중심의 위험대응 프로세스를 제안하고, VaR개념을 응용한 위험허용도 산정방법을 제시하고자 한다.

• 한국안전학회지
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• 제28권1호
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• pp.158-163
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• 2013

While job stress evaluations are reported in the recent surveys upon the nuclear power plants(NPPs), any significant advance in the types of questionnaires is not currently found. There are limitations to their usefulness as analytic tools for the management of safety resources in NPPs. Data mining(DM) has emerged as one of the key features for data computing and analysis to conduct a survey analysis. There are still limitations to its capability such as dimensionality associated with many survey questions and quality of information. Even though some survey methods may have significant advantages, often these methods do not provide enough evidence of causal relationships and the statistical inferences among a large number of input factors and responses. In order to address these limitations on the data computing and analysis capabilities, we propose an advanced procedure of survey analysis incorporating the DM method into a statistical analysis. The DM method can reduce dimensionality of risk factors, but DM method may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. We propose three steps to address these limitations. The first step shows data mining with response surface method(RSM), to deal with specific situations by creating a new method called response surface data mining(RSDM). The second step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest, and shows the demonstration the proposed RSDM can effectively find significant physical, psycho-social, and environmental risk factors by reducing the dimensionality with the process providing detailed statistical inferences. The final step suggest a robust stress management system which effectively manage job stress of the workers in NPPs as a part of a safety resource management using the surrogate variable concept.

• 대한산업공학회지
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• 제39권5호
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.