• Korean Journal of Construction Engineering and Management
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• v.14 no.1
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• pp.63-70
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• 2013

A judgment on the redevelopment projects' predicted profitability is an essential decision-making element for the success of the redevelopment projects. It is necessary to review the literature on profitability of redevelopment project and draw risk factors that could affect profitability through the risk analysis based on surveys. It is also necessary to judge profitability prediction toward the business value of the redevelopment project in the planning phase according to the risk analysis results which can affect the profitability prediction. In order to prevent the growing difficulties in executing the projects, a profitability prediction model is proposed using the method of management and disposal based on a proportional calculation that can estimate the share of expenses in order to judge profitability in the planning phase. With the improvement of profitability prediction models, it is possible to appropriately judge profitability in the planning phase in order to allow the prevention of suspension, reduction of project term, reduction of cost, and making of rational decisions.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.6
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• pp.851-862
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• 2006

Satellites have been valuable tool for global/regional scale atmospheric environment monitoring as well as emission source detection. In this study, we present the results of application of satellite remote sensing data for air quality forecast in Seoul metropolitan area. AOT (Aerosol Optical Thickness) data from TERRA/MODIS (Moderate Resolution Imaging Spectre-radiometer) satellite were compared to ground based $PM_{10}$ mass concentrations, and used to estimate the possibility of the aerosol forecasting in Seoul metropolitan area. Although correlation coefficient (${\sim}0.37$) between MODIS AOT products and surface $PM_{10}$ concentration data was relatively low, there was good correlation between MODIS AOT and surface PM concentration under certain atmospheric conditions, which supports the feasibility of using the high-resolution MODIS AOT for air quality forecasting. The MODIS AOT data with trajectory forecasts also can provide information on aerosol concentration trend. The success rate of the 24 hour aerosol concentration trend forecast result was about 75% in this study. Finally, application of satellite remote sensing data with ground-based air quality observations could provide promising results for air quality monitoring and more exact trend forecast methodology by high resolution satellite data and verification with long term measurement dataset.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.123.1-123.1
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• 2012

We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2386-2395
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• 2010

The entrepreneurship is a key success factor of industrial development in global competitive environments. but there is no entrepreneurship index/indicator which gives comprehensive advantages for monitoring and forecasting entrepreneur environments in Korea. The purpose of the study is developing Entrepreneurship Survey Index(ESI) which considering various significant entrepreneur factors. The suggested ESI in this exploratory study consists of entrepreneurship business index(EBI), entrepreneurship environment index(EEI) and entrepreneurship preparation index(EPI). The EBI is composed of overall business factors which revised from practical studies and expert reviews. The EEI is mainly retrieved Global Entrepreneurship Monitor(GEM) and partially modified by an expert advisor to identify entrepreneur environments. The EPI is developed for evaluating and confirming the capability, plan and intention of the pre-entrepreneurship. The practical survey of using the proposed ESI will enhance the power of forecasting the entrepreneurship environment changes and provide effective entrepreneurship policy making for stakeholder.

• Journal of the Korea Institute of Building Construction
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• v.19 no.5
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• pp.439-448
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• 2019

There are various factors affecting the success and failure of an apartment building project. However, after the unit sale price has been determined and the sale has started, the most important factor affecting on the project is the initial sales ratio for one month after the sale. Generally, developers predict an initial sales ratio by various data such as economic situation, the trend of the housing market, and the house price near the business place. However, it is very difficult for these factors to be calculated quantitatively in connection with the initial sales ratio. Therefore, the purpose of this study is to develop a regression model for forecasting the initial sales ratio of apartment building projects. For this study, pre-sales data collection, correlation analysis between influencing factors, and regression model development are performed sequentially. The results of this study are used as basic data for predicting the initial sales ratio in the feasibility analysis of apartment building projects and are used as key data for the development of the risk management model.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.823-830
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• 2022

The risk of project execution increases due to the enlargement and complexity of Engineering, Procurement, and Construction (EPC) plant projects. In the fourth industrial revolution era, there is an increasing need to utilize a large amount of data generated during project execution. The design is a key element for the success of the EPC plant project. Although the design cost is about 5% of the total EPC project cost, it is a critical process that affects the entire subsequent process, such as construction, installation, and operation & maintenance (O&M). This study aims to develop a system using machine-learning (ML) techniques to predict risks and support decision-making based on big data generated in an EPC project's design and construction stages. As a result, three main modules were developed: (M1) the design cost estimation module, (M2) the design error check module, and (M3) the change order forecasting module. M1 estimated design cost based on project data such as contract amount, construction period, total design cost, and man-hour (M/H). M2 and M3 are applications for predicting the severity of schedule delay and cost over-run due to design errors and change orders through unstructured text data extracted from engineering documents. A validation test was performed through a case study to verify the model applied to each module. It is expected to improve the risk response capability of EPC contractors in the design and construction stage through this study.

• Journal of Distribution Science
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• v.11 no.12
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• pp.25-37
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• 2013

Purpose - The structure of retailing has changed as retailers develop markets in response to business environment changes. This study aims to analyze the general situation of retailers in order to predict future global strategy using case studies of overseas expansion strategy and the Metro Group's global strategy. Research design, data, and methodology - The backgrounds to the new retail business model and retailer classification are analyzed as theoretical data. In addition, the key success point of the Metro Group's "cash and carry" strategy is analyzed as is the Metro Group's global CFAR (collaborative planning, forecasting, and replenishment) strategy. Finally, the plan for cooperation and precise forecasting under the Metro Group's supply chain management are analyzed from the promotion environment viewpoint. Related materials analyzed included the 2012 annual report, the Metro Group's web page, and a video interview with the executive in charge of global strategy and the new market development department. Some data were revised to avoid disrupting essential aspects of the case studies. Results - The important finding was that the Metro Group could be a world-class retail company with its successful global expansion strategy. The Metro Group's global strategy's primary goal is to have a leading business position in Eastern and Western Europe. The "cash and carry" strategy is highest priority in its overseas expansion strategy. Moreover, the Metro Group has standardized product planning capacity, which could be applied in various countries with different structural and cultural backgrounds. This is the main reason that the Metro Group could rapidly become successful in the Eastern Europe and Asian markets through its structural overseas expansion strategies. In addition, the Metro Group emphasizes the importance of supply chain management. Conclusions - First, retailers should create additional value through utilizing the domestic market, market power, and economies of scale to launch a global strategy to maximize benefits from diversification. Second, the political, economic, and cultural background of the target country needs to be understood to successfully implement the overseas expansion strategy. Third, the main factor of successful cooperation with a local partner is how quickly the company gains total understanding of the business resources and core competence of its partner. All organizations should focus on the achievement of goals in order to successfully operate the partnership. Fourth, retailers should improve their business, financial and organizational structure. Moreover, the work processes and company culture should also be improved to respond strongly in the competitive global market. Fifth, the essential point of a successful retail business is the control capacity of its branding and format. The retailer could avoid forecasting errors through supply chain management by perfectly distributing the actual amount of its inventory. In addition, the risks along the supply chain are effectively shared between the supply chain partners. Finally, the central tendency of the market is to gain in strength with this taking place across all parts of the business.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.405-414
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• 2011

The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.97-114
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• 2016

One of the key success factors for the intelligent IT service which is characterized by personalization and automation, is to obtain relevant data from either sensors or data storage for reasoning, analyzing and forecasting. The availability of the open data sources such as public portal sites remarkably increases the efficiency and quality of the intelligent IT service. However, with the condition that not all data in the existing public or private sites are opened or have various types of openness, it prohibits the value of utilization. For these reasons, it is highly required to evaluate the extent of openness of data storage. However, there are only a few studies which explore the factors which affect the degree of data openness with respect to intelligent IT services. Hence, this study aims to propose an evaluation model including the indices to evaluate a process of opening data for the intelligent IT service from a viewpoint of data utilization process. The indices are applied to evaluate the actual multinational websites, which provide public data for verification. We also discuss the implications of the evaluation according to the results.

• Journal of Applied Tourism Food and Beverage Management and Research
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• v.9
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• pp.273-302
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• 1998

INTERNATIONAL MONITORY FUNDS wave swept the Economic structural insolvency lies one upon another with low growth, low trust, low prices, low interest and low employment as[2 down 3 up] phenomenons have been distinguished and low enterprise a control of structures due to forecasting 200 million unemployment, including 600 million unemployed the head of a family population have a difficulty in their life. Only way to give them hope is through the commencement of an enterprises to have 2nd career development. But end of 1995, 467,00 dining out companies have been established and recently business are in depression. There are many business conditions of change of business or reduce operations, if unemployment populations of 5%, 100,000 peoples doing the commencement of an enterprises, enormous number of dining out companies will be increased and the competition will be fierce, especially those who have short knowledge and experience doing the commencement of an enterprises have high failure than success which will give a problems to society. Our study is to make the commencement of an enterprise to reducing the faiure and to be successful for main point to successful commencement of an enterprise, the established can self capability and mental condition, the main important factor is types of industry selection, successful and those established who takes this conditions will very carefully inspect various matters by scientifically and rationally mind industrys propulsion graphs and open official fixture graphs will framing detail factors. One by inspect the reduction of failure, and successful commencement of an enterproses mind industry have been studied.