• Industrial Engineering and Management Systems
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• 제15권4호
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• pp.324-334
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• 2016

Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

• 한국농공학회지
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• 제38권2호
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• pp.108-122
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• 1996

This study was to introduced estimation model for optimum probabilistic rainfall intensity on hydrological area. Originally, probabilistic rainfall intensity formula have been characterized different coefficient of formula and model following watersheds. But recently in korea rainfall intensity formula does not use unionize applyment standard between administration and district. And mingle use planning formula with not assumption model. Following the number of year hydrological duration adjust areal index. But, with adjusting formula applyment was without systematic conduct. This study perceive the point as following : 1) Use method of excess probability of Iwai to calculate survey rainfall intensity value. 2) And, use method of least squares to calculate areal coefficient for a unit of 157 rain gauge station. And, use areal coefficient was introduced new probabilistic rainfall intensity formula for each rain gauge station. 3) And, use new probabilistic rainfall intensity formula to adjust a unit of fourteen duration-a unit of fifteen year probabilistic rainfall intensity. 4) The above survey value compared with adjustment value. And use three theory of error(absolute mean error, squares mean error, relative error ratio) to choice optimum probabilistic rainfall intensity formula for a unit of 157 rain gauge station.

• 한국지반공학회논문집
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• 제25권11호
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• pp.87-103
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• 2009

지반정수의 신뢰성 높은 확률분포형을 결정하기 위해서는 분석자료에 대한 이상치 및 무작위성 검정, 적용한 확률 분포형의 매개변수 추정 및 매개변수 적합성 검정, 마지막으로 확률분포형의 적합성 검정의 과정이 필요하며, 위의 순서로 지반정수의 확률분포형을 산정할 것을 제안하였다. 본 연구에서는 제안한 절차에 따라 분석대상 지반으로 선정된 인천 송도지역 지반정수들의 확률분포형을 추정하였으며, 추가로 지반정수들의 변동성을 나타내는 변동계수를 산정하였다. 이와 같이 신뢰성 높은 지반정수들의 확률분포형과 변동계수는 확률론적 설계방법에 사용될 수 있을 뿐만 아니라 결정론적 설계에 사용될 지반정수의 합리적인 결정에 사용될 수 있는 중요한 자료로 판단된다.

• 지역연구
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• 제11권1호
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• pp.31-60
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• 1995

The distribution of economic activity over a mutually exclusive and exhaustive categorical industry-region matrix is modeled as a composition of two random components: the probability-like share distribution of jobs and the dynamic evolution of absolute aggregates. The former describes the individual activity location choice by comparing the predicted profitability of the current industry-region pair against that of all other alternatives based on the available information on industry-specific, region specific, or activity specific attributes. The latter describes the time evolution of macro-level aggregates using a dynamic reduced from model. With the seperation of micro choice behavior and macro dynamic aggregate constraint, the usual independence and identicality assumptions become consistent with the activity share distribution, hence multi-regional industrial migration can be represented by a set of probability evolution equations in a conservative Markovian from. We call this a Micro-Macro Composition Approach since the product of the aggregate prediction and the predicted activity share distribution gives the predicted activity distribution gives the predicted activity distribution which explicitly considers the underlying individual choice behavior. The model can be applied to interesting practical problems such as the plant location choice of multinational enterprise, the government industrial ploicy to attract international firms, and the optimal tax-transfer mix to influence activity location choice. We consider the latter as an example.

• 한국철도학회논문집
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• 제12권2호
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• pp.205-211
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• 2009

국가 물류 수송의 총 사회적 비용을 감소시키기 위해 기존의 물류 수송 체계를 철도 중심의 물류체계로 전환해야 할 필요성이 있다. 이를 위해 DMT와 같은 다양한 새로운 운송 수단의 도입이 고려되고 있으며, 이러한 운송수단의 도입은 기존의 도로의 수송 분담률을 낮춰줄 것으로 기대된다. 그러나 새로운 운송 수단의 도입에 따른 경제성 평가는 대부분 화물 운송 소비자의 효용함수를 도출하고, 이 효용함수로부터 수단별 운송 분담률이 어느 정도 될 것인지를 추측하는 데에 치중해왔다. 본 연구는 운송 소비자의 효용과 사회적 비용을 동시에 고려하여, 물류수송에 관여되는 사회적 비용을 최소화하는 적정 수송분담률을 구하기 위한 수리 모형을 제시하고자 한다. 이를 이용하여 컨테이너 화물에 대한 운송 수단별 적정 수송 분담률을 계산해 본다.

• Wind and Structures
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• 제38권4호
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• pp.309-323
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• 2024

The gust factor and turbulence intensity are two crucial parameters that characterize the properties of turbulence. In tropical cyclones (TCs), these parameters exhibit significant variability, yet there is a lack of established formulas to account for their probabilistic characteristics with consideration of their inherent connection. On this condition, a probabilistic analysis of gust factors and turbulence intensities of TCs is conducted based on fourteen sets of wind data collected at the Sutong Cable-stayed Bridge site. Initially, the turbulence intensities and gust factors of recorded data are computed, followed by an analysis of their probability densities across different ranges categorized by mean wind speed. The Gaussian, lognormal, and generalized extreme value (GEV) distributions are employed to fit the measured probability densities, with subsequent evaluation of their effectiveness. The Gumbel distribution, which is a specific instance of the GEV distribution, has been identified as an optimal choice for probabilistic characterizations of turbulence intensity and gust factor in TCs. The corresponding empirical models are then established through curve fitting. By utilizing the Gumbel distribution as a template, the nexus between the probability density functions of turbulence intensity and gust factor is built, leading to the development of a generalized probabilistic model that statistically describe turbulence intensity and gust factor in TCs. Finally, these empirical models are validated using measured data and compared with suggestions recommended by specifications.

• 한국조경학회지
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• 제23권1호
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• pp.141-156
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• 1995

This research is executed to find out the boundary of use by the conscious of local residents and to get the basic materials for the distribution of outdoor leisure-facilities. The map of use distribution of the outdoor leisure-facilities in a neighbourhood unit is made by applying a concept of the probabilistic contour line based on the choice model of outdoer leisure-facilities in the city of Seoul, Taegu an\ulcorner Kwangju. The results are listed as follows. 1) The use of outdoor leisure-facilities is influenced on the accessibility by the physical obstacles of streets and hills, etc. 2) The limitation of uses applying the model of choice probability are different according to the accessibility, the percentage of utilities and the arriving range based on the questionnaires which are surveyed the choice of outdoor leisure facilities of the residents of Seoul, Taegu and Kwangju. 3) The distribution of outdoor leisure facilities is decided by the limitation of use with the conscious of local residents.

• Structural Engineering and Mechanics
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• 제25권3호
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• pp.331-345
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• 2007

Uncertainties enter a complex analysis from a variety of sources: variability, lack of data, human errors, model simplification and lack of understanding of the underlying physics. However, for many important engineering applications insufficient data are available to justify the choice of a particular probability density function (PDF). Sometimes the only data available are in the form of interval estimates which represent, often conflicting, expert opinion. In this paper we demonstrate that Bayesian estimation techniques can successfully be used in applications where only vague interval measurements are available. The proposed approach is intended to fit within a probabilistic framework, which is established and widely accepted. To circumvent the problem of selecting a specific PDF when only little or vague data are available, a hierarchical model of a continuous family of PDF's is used. The classical Bayesian estimation methods are expanded to make use of imprecise interval data. Each of the expert opinions (interval data) are interpreted as random interval samples of a parent PDF. Consequently, a partial conflict between experts is automatically accounted for through the likelihood function.

• 대한교통학회지
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• 제26권3호
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• pp.211-221
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• 2008

본 논문에서는 교통사고로 인해 사고 피해자 및 그 가족이나 친지, 주변사람들이 느끼는 정신적 고통을 사회적비용으로 환산한 심리적비용에 대해 고찰하여 보았다. 선택실험방법, 직접질문법, 양분선택형방법 등 3가지 방법을 사용하여 설문설계를 하였으며 이들 설문 설계방법별로 각각의 모형을 구축하였다. 모형 구축 시에는 확률선택모형에서 가장 일반적으로 사용되는 로짓모형을 이용하였으며 직접질문법은 토빗모형을 이용하였다. 이들 모형을 검증한 결과 모형에 따라 차이는 있었으나 대부분 모형의 적합도 및 각 계수의 신뢰도가 95% 신뢰수준에서 유의한 것으로 나타났다. 심리적비용 산정모형으로 산출된 국내 도로교통사고의 심리적비용은 사상자 1인당 1,563만원으로 총 5조 1천억 원이며, 전체 교통사고비용의 37.1%를 점유하는 것으로 분석되었다.

• Communications for Statistical Applications and Methods
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• 제24권2호
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• pp.143-154
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• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.