• Journal of Digital Convergence
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• v.11 no.2
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• pp.175-182
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• 2013

In this paper, the simulation model of house price is presented on the basis of pricing mechanism between the demand and the supply of apartments in seoul. The algorithm of house price simulation model for calculating the rate of price over time includes feedback control theory. The feedback control theory consists of stock variable, flow variable, auxiliary variable and constant variable. We suggest that the future price of apartment is simulated using mutual interaction variables which are demand, supply, price and parameters among them. In this paper we considers three items which include the behavior of apartment price index, the size of demand and supply, and the forecasting of the apartment price in the future economic scenarios. The proposed price simulation model could be used in public needs for developing a house price regulation policy using financial and non-financial aids. And the quantitative simulation model is to be applied in practice with more specific real data and Powersim Software modeling tool.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.472-482
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• 2012

There are many investors in the stock market, and more and more people get interested in the stock investment. In order to avoid risks and make profit in the stock investment, we have to determine several aspects using various information. That is, we have to select profitable stocks and determine appropriate buying/selling prices and holding period. This paper proposes a data mining tool for the investors' decision support. The data mining tool makes stock investors apply machine learning techniques and generate stock price prediction model. Also it helps determine buying/selling prices and holding period. It supports individual investor's own decision making using past data. Using the proposed tool, users can manage stock data, generate their own stock price prediction models, and establish trading policy via investment simulation. Users can select technical indicators which they think affect future stock price. Then they can generate stock price prediction models using the indicators and test the models. They also perform investment simulation using proper models to find appropriate trading policy consisting of buying/selling prices and holding period. Using the proposed data mining tool, stock investors can expect more profit with the help of stock price prediction model and trading policy validated on past data, instead of with an emotional decision.

• Proceedings of the Safety Management and Science Conference
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• 2005.11a
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• pp.511-515
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• 2005

기업은 고객이 원하는 시기에 원하는 제품을 구매할 수 있도록 항상 준비가 되어 있어야 한다. 고객의 수요를 만족시키기 위하여 기업은 다양한 수요예측방법을 통하여 적절한 재고 수준과 수요예측을 하고 있다. 제조 기업의 경우에는 다른 산업에 비하여 정확한 수요예측과 낮은 재고 수준의 유지가 비용과 직접적인 연관이 있기 때문에 제조 기업은 경제적인 주문량 결정(Economic Order Quantity: EOQ)이 매우 중요한 문제이다. 주문량을 결정하는 방법에는 여러 가지가 있지만, 본 논문에서는 고객 지연을 방지하기 위하여 경제적 주문량 결정에 고객 지연과 관련된 비용을 포함시키는 것은 물론 고객 지연이라는 상황을 방지하는 노력의 한 방법으로 가격 할인(discount system)을 이용하고자 한다. 가격 할인을 이용하여 고객으로 하여금 빠른 주문을 유도하고 그로 인하여 고객 지연 상황의 발생을 줄여보려고 한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.329-335
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• 2023

Avocado, a superfood selected by Time magazine and one of the late ripening fruits, is one of the foods with a big difference between local prices and domestic distribution prices. If this sorting process of avocados is automated, it will be possible to lower prices by reducing labor costs in various fields. In this paper, we aim to create an optimal classification model by creating an avocado dataset through crawling and using a number of deep learning-based transfer learning models. Experiments were conducted by directly substituting a deep learning-based transfer learning model from a dataset separated from the produced dataset and fine-tuning the hyperparameters of the model. When an avocado image is input, the model classifies the ripeness of the avocado with an accuracy of over 99%, and proposes a dataset and algorithm that can reduce manpower and increase accuracy in avocado production and distribution households.

• Korean Journal of Construction Engineering and Management
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• v.11 no.6
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• pp.54-64
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• 2010

A firm decides to go to the project based on its investment analysis. However, the cash flows generated from the real project can not be always coincident with what expected as it follows uncertain behavior and the asymmetric payoff caused by the managerial flexibilities involved in the real asset affects the project value. Amongst various managerial flexibilities entailed in most of the real assets, although investment delay has been known to enhance the project value thanks to its ability to provide new market information to management, the related research to select the time to invest have been just few. Therefore, this research aims to show the theoretical framework to decide when to invest reflecting the behaviors of increasing project value and loss recovery cost due to investment delay with option pricing, related financial economic, and variational theories.

• Journal of the Korean Geographical Society
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• v.39 no.6 s.105
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• pp.907-921
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• 2004

Lots of characteristics such as dwelling, neighborhood, and accessibility characteristics affect to the housevalue. Many researches have been done to identify values of each characteristic using hedonic technique. However, there is a limit to identify interaction of each characteristic and variation of each characteristic among the accessibility context. This paper has implemented the Expansion Method research paradigm to model the housevalue determination process in the city of Seoul. The findings of this paper have revealed the presence of contextual variations in the housevalue determination process. The initial model for housevalue reveals that as $F_1$ increases (i.e., larger the number of rooms/bathrooms, larger parking space) and/or $F_2$ increases (i.e., higher owner occupied housing units, higher apartment housing units) and/or $F_3$ increases, (i.e., higher the ratio of higher than college graduated households, 8 school zone, older housing units) the estimated housevalue increases. However, the above relationships drift across their respective contexts. The houses which have negative $F_1$ value, the housevalue does not fluctuate according to the distance to the city center or subcenters. However, the houses which have positive $F_1$ value, the closer to the subcenters or shorter to the river, the higher the estimated housevalues. On the other hand, in areas far from the subcenters, the estimated housevalues does not fluctuate much according to the corresponding $F_2$ level. In areas close to the subcenters, the estimated housevalues vary tremendously according to the $F_2$ value. In the residual analysis, it is revealed that large apartment which are located in Kangnam, IchongDong, MokDong are underestimated. This paper has contributed to our understanding of the housevalue determination process by providing an alternative conceptualization to the traditional approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.635-646
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• 2016

This paper proposes a housing business model, where the presale and Chonsei housing are supplied under a presale system at the same time based on the characteristic correlation between the housing presale market and Chonsei market in Korea. Markowitz portfolio theory was used to review the risk diversification effects from the changes in the ratio between the presale housing supply and the Chonsei housing supply. The housing sale price indicator was used as a proxy variable to determine the presale housing supply. The housing Chonsei price indicator was used as a proxy variable to determine the Chonsei housing supply. The proposed housing business model was applied to major areas in Korea to examine the risk diversification effect. Comparisons of the regional portfolio analyses showed that the flexibility of the proposed housing business model can be quite effective because each regional housing market exhibits different characteristics. Market participants, such as developers, construction companies, consumers, and government, can expect various effects through the proposed housing business model. Nevertheless, policy support is necessary for practical applications of the proposed housing business model. In particular, public funds from the government need to be introduced.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.143-146
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• 2000

기업을 둘러싼 시장 환경이 급변하면서 기업의 입장에서는 비즈니스 프로세스를 보다 효율적으로 통합, 조정하여 경쟁사보다 빠르고 능동적으로 대처해야 할 필요성을 느끼고 있다. 특히 기업의 영업과 생산 부문은 프로세스 통합, 조정의 필요성이 가장 큰 분야이면서 서로 판이한 전략 및 목표를 가짐으로 인해 가장 통합하기 어려운 분야이다. 그 이유는 영업 부문에서는 가격과 판촉을 통해 고객 서비스를 제고하면서 많은 판매 수입을 올리는 것이 목표라면 생산 부문은 생산성을 높이면서 최소의 비용으로 제품을 생산하는 것이 목표이기 때문이다. 본 연구에서는 한정적인 판매 시즌을 가지는 상품에 대해서 동일 기업 내의 영업 부문과 생산 부문의 문제들을 동시에 고려한 통합 의사 결정 정책을 제시하였다. 영업과 생산 부문의 의사 결정이 순차적으로 분리되어 결정되는 순차적 모델과 영업과 생산 부문의 의사 결정이 동시에 통합적으로 이루어지는 통합 모델을 비교 분석함으로써 그 효과를 검증하였다

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.525-526
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• 2021

최근 금리 인하로 주식을 비롯한 다양한 금융상품에 대한 투자가 급증하고 있다. 주식 시장에서 가격은 시장의 모든 정보들이 반영된 결과로서 주식의 가격 변동을 이용하여 가격 패턴을 찾아낸 후 다양한 분석기법으로 주가 지수를 예측하는 연구들이 진행되어 왔다. 그러나 주식 시장은 기업의 내·외부 요인들의 상호관계가 주가 형성에 많은 영향을 주는 가격 결정 메카니즘으로 인해 주가의 변동을 설명할 수 없는 경우가 자주 발생하고 있다. 따라서 주식 시장 예측을 위해서는 시장 내부의 변화와 외부 사건들을 함께 반영할 수 있는 방법이 필요하다. 본 논문에서는 뉴스 기사들에 대한 감성 분석과 주가지수의 시계열 데이터를 딥러닝 예측 모델을 통해 주식 시장의 추세를 예측할 수 있는 주가지수 예측 프로그램을 제안한다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.173-183
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• 2017

We investigate the factors affecting the price of apartments using the spatial and temporal data of private real estate prices. The factors affecting the price of apartment were analyzed using geographical and temporal weighted regression (GTWR) model which incorporates the temporal and spatial variation. In contrast to the OLS, a general approach used in previous studies, and GWR method which is most widely used for analyzing spatial data, GTWR considers both temporal and spatial characteristics of the house price, and leads to better description of the house price determination. Year of construction and floor area are selected as the significant factors from the analysis, and the house price are affected by them temporally and geographically.