• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.609-612
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• 2007

This paper describes a new method to predict the load current of a dc-dc converter. The load current is calculated using the video information of the PDP. The output capacitance of the dc-dc converter can be reduced by utilizing the predicted load current, which results in a cost reduction of the power system in the PDP.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.382-386
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• 2007

In this study, the qualitative prediction and evaluation of clean room being utilized for mass production of electrronic components have been performed with the help of flow simulation. Compared to the experimental analysis based on measurements of the number of particles, concentration of contaminants and flow characteristics, the numerical analysis used in this study is much cost-effective.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.22-34
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• 2023

Predicting accurately the construction cost budget in the early stages of construction projects is crucial to support the client's decision-making and achieve the objectives of the construction project. This holds true for public construction projects as well. However, the current methods for predicting construction cost budgets in the early stages of public construction projects are not sophisticated enough in terms of accuracy and reliability, indicating a need for improvement. The objective of this study is to develop a construction cost budget prediction model that can be utilized in the early stages of public building projects using an artificial neural network (ANN). In this study, an artificial neural network model was developed using the SPSS Statistics program and the data provided by the Public Procurement Service. The level of construction cost budget prediction was analyzed, and the accuracy of the model was validated through additional testing. The validation results demonstrated that the developed artificial neural network model exhibited an error range for estimates that can be utilized in the early stages of projects, indicating the potential to predict construction cost budgets more accurately by incorporating various project conditions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.4
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• pp.87-96
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• 2017

It is important to estimate the future maintenance budget of all SOC infrastructure at the national strategic level. In this study, Based on a currently available statistics data, we predicted future maintenance investment for all SOC infrastructure in Korea. We have studied the applicable prediction models, and we developed the prediction models that can calculated the future maintenance cost by a real expenditure date. The subjects of facilities are bridges, tunnels, pavements, harbors, dams, airports, water supply, rivers and port. As a result of total estimated cost, eight types of SOC infrastructures are about 23 trillion won for the next 10years, and the most expensive facilities are road pavements and bridges.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.103-111
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• 2023

Accurate cost estimation in the early stages of a construction project is critical to the successful execution of the project. In this study, an ANFIS model was presented to predict construction costs in the early stages of a construction project. To increase the usability of the model, open construction cost data was used, and a model using limited information in the early stage of the project was presented. We analyzed existing studies related to ANFIS to identify recent trends, and after reviewing the basic structure of ANFIS, presented an ANFIS model for predicting conceptual construction costs. The variation in prediction performance depending on the type and number of membership functions of the ANFIS model was analyzed, the model with the best performance was presented, and the prediction accuracy of representative machine learning models was compared and analyzed. Through comparing the ANFIS model with other machine learning models, it was found to show equal or better performance, and it is concluded that it can be applied to predicting construction costs in the early stage of a project.

• Journal of the Korean Institute of Gas
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• v.7 no.4 s.21
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• pp.61-66
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• 2003

The objective of this study is to develop a Cost-Benefit analysis system which would help us to make optimal decision among safety investment alternatives, calculating and comparing costs and benefits for facilities in chemical plants. So, the accident frequency analysis module and the accident damage prediction module were developed for estimating quantitative risks in chemical facilities, and domestic societal risk criterion was presented after the comparative analysis of major industrial cases and societal risk criteria of advanced countries like the Netherlands, Australia, U.S.A., U.K., and Germany. Also, the Cost-Benefit Analysis System which compares the safety investment alternatives based on their deduced net present values was developed through the selection of proper cost and benefit items by field studies

• Environmental Mutagens and Carcinogens
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• v.20 no.1
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• pp.21-25
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• 2000

Recently there is increasing interest in the use of structure activity relationships for predicting the biological activity of chemicals. The reasons for the interest include the decrease cost and time per chemical as compared with animal or cell system for identifying toxicological effects of chemicals and the reduction in the use of animals for toxicological testing. This study is to test the validity of the mutagenicity data generated from QSAR (Quantitative Structure Activity Relationship) program. Thirty chemicals, which had been evaluated by Ames test during 1997-1999, were assessed with TOPKAT QSAR mutagenicity prediction module. Among 30chemicals experimented, 28 were negative and 2 were positive for Ames test. On the contrary, 23 chemicals showed the high confidence level indicating high prediction rate in mutagenicity evaluation, and 7 chemicals showed the lsow to moderate confidence level indicating low prediction in mutagenicity evaluation. Overall mutagenicity prediction rate was 77% (23/30). The prediction rates for non-mutagenic chemicals were 79% (22/28) and mutagenic chemicals were 50% (1/2). QSAR could be a useful tool in providing toxicological data for newly introduced chemicals or in furnishing data for MSDS or in determining the dose in toxicity testing for chemicals with no known toxicological data.

• Journal of the Korea Concrete Institute
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• v.16 no.1 s.79
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• pp.102-110
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• 2004

Recently, production cost of ready mixed concrete(remicon) has been increased due to the rising cost of raw materials such as cement and aggregate etc. cause by the upturn of oil price and increase of shipping charge. The delivery cost of remicon companies, however, has been decreased owing to their excessive competition in sale. Consequently, remicon companies began to manufacture the concrete by mixing ground granulated blast furnace slag(GGBF) in order to lower the production cost. Therefore, the objective of this study was to predict 28-day strength of GGBF slag concrete by early strength(1 day-strength, 7 day-strength) for the sake of managing with ease the quality of remicon. In experimental results, the prediction equation for 28 day-strength of GGBF slag concrete could be produced through the linear regression analysis of early strength and 28 day-strength. In order to acquire the reliability, all mixture were repeated as 3 times and each mixture order was carried out by random sampling. The prediction equation for 28 day-strength of GGBF slag concrete by 1-day strength(hot-water method) won the good reliability.

• Journal of the Korean Recycled Construction Resources Institute
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• v.4 no.4
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• pp.91-98
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• 2009

Recently, production cost of ready mixed concrete has been increased due to the rising cost of raw materials such as cement and aggregate etc. cause by the upturn of oil price and increase of shipping charge. The delivery cost of ready mixed concrete companies, however, has been decreased owing to their excessive competition in sale. Consequently, ready mixed concrete companies began to manufacture the concrete by mixing ground granulated blast furnace slag(GGBF) and fly-ash in order to lower the production cost. Therefore, the objective of this study was to predict 28 days strength of GGBF slag concrete by early strength(warm and hot water curing method) for the sake of managing with ease the quality of ready mixed concrete. In experimental results, the prediction equation for 28 days compressive strength of GGBF slag concrete could be produced through the linear regression analysis of early strength and 28 days strength. In order to acquire the reliability, all mixture were repeated as 3 times and each mixture order was carried out by random sampling. The prediction equation for 28 days strength of GGBF slag concrete by 1 day compressive strength(accelerated testing) according to warm and hot water curing method won the good reliability.

• Korean Journal of Construction Engineering and Management
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• v.8 no.5
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• pp.191-200
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• 2007

This paper introduces a tool for predicting potential cost overrun during project execution and for quantifying the uncertainty on the expected project cost, which is occasionally changed by the unknown effects resulted from project's complications and unforeseen environments. The model proposed in this stuff is useful in diagnosing cost performance as a project progresses and in monitoring the changes of the uncertainty as indicators for a warning signal. This model is intended for the use by project managers who forecast the change of the uncertainty and its magnitude. The paper presents a mathematical approach for modifying the costs of incomplete work packages and project cost, and quantifying reduced uncertainties at a consistent confidence level as actual cost information of an ongoing project is obtained. Furthermore, this approach addresses the effects of actual informed data of completed work packages on the re-estimates of incomplete work packages and describes the impacts on the variation of the uncertainty for the expected project cost incorporating Multivariate Probabilistic Analysis (MPA) and Bayes' Theorem. For the illustration purpose, the Introduced model has employed an example construction project. The results are analyzed to demonstrate the use of the model and illustrate its capabilities.