• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1715-1721
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• 2011

For an efficient railroad operations the demand forecasting is required. Time series models can quickly forecast the future demand with fewer data. As well as the accuracy of forecasting is excellent compared to other methods. In this study is proposed the intervention ARIMA model for forecasting methods of KTX passenger demand. The intervention ARIMA model may reflect the intervention such as the Kyongbu high-speed rail project second phase. The simple seasonal ARIMA model is predicted to overestimate the KTX passenger demand. However, intervention ARIMA model is predicted the reasonable results. The KTX passenger demands were predicted to be a week units separated by the weekday and weekend.

• Current Photovoltaic Research
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• v.7 no.4
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• pp.85-96
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• 2019

Renewable energy is emerging as a reliable alternative source of energy, it is much safer, cleaner than conventional sources and has contributed significantly in this sector. However, there are still some challenges that needed to address this evolving technology. Artificial Intelligence (A. I.) can assess the past, optimize the present, and forecast the future. Therefore, A. I. will resolve most of these problems. Artificial intelligence is complex in nature, but it reduces error and aims to reach a greater degree of precision which make renewables smarter. This paper provides an overview of frequently used A. I. methods in solar energy applications. A sample algorithm is also provided for literature purposes and knowledge transfer.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.152-155
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• 1991

The analysis and forecast of summer cooling load is one of the major concerns of utility company(KEPCO). In this paper, various methodologies to assess the weather sensitive load are introduced and the cause of remarkable growth of the summer cooling load in the last years are analized. To establish the effective measures to migrate the peak building by the summer cooling, a number of practical institutional policies are offered for future implementation.

• Journal of Animal Science and Technology
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• v.46 no.3
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• pp.479-494
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• 2004

The study was designed to forecast and derive future core technologies using Delphi method for Korean livestock industry. The technologies will make livestock industJy a core and strategic industry that has high value-added sector in 21 century. Questions were given to specialists of each technology in order to survey importance, realization time. level of R&D in Korea and foremost countries, leading group of R&D, effective policy, etc. for each technology. The target of the survey for Delphi is confined to specialists in the area of Animal Science and Veterinary Medicine. 90 core technologies were derived and 62 specialists answered the questionnaire.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.3
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• pp.377-385
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• 2012

The Automatic Dependent Surveillance Broadcast (ADS-B) system is a key component of CNS/ATM recommended by the International Civil Aviation Organization (ICAO) as the next generation air traffic control system. ADS-B broadcasts identification, positional data, and operation information of an aircraft to other aircraft, ground vehicles and ground stations in the nearby region. This paper explores the ADS-B based trajectory prediction and the conflict detection algorithm. The multiple-model based trajectory prediction algorithm leads accurate predicted conflict probability at a future forecast time. We propose an efficient and accurate algorithm to calculate conflict probability based on approximation of the conflict zone by a set of blocks. The performance of proposed algorithms is demonstrated by a numerical simulation of two aircraft encounter scenarios.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.143-146
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• 2004

With the rapid growth of e-commerce, marketers are able to observe not only purchasing behavior on what and when customers purchased, but also the individual Web usage behavior that affect purchasing. The richness of this information has the potential to provide marketers with an in-depth understanding of customer. Using commonly available Web log data, this paper examines Web usage behaviors at the individual level. By decomposing the buying process into a pattern of visits and purchase conversion at each visit, we can better understand the relationship between Web usage behavior and purchase decision. This allows us to more accurately forecast a shopper's future purchase decision at the site and hence determine the value of individual customers to the siteAccording to our research, not only information seeking behavior but also visiting duration of a customer and participative behavior such as participation in event should be considered as important predicators of purchase decision of customer in a cosmetic internet shopping mall.

• Journal of Environmental Science International
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• v.13 no.8
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• pp.733-737
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• 2004

FThe aim of this study was to carry out a preliminary test of the air quality modelling system (HIRES-AIRCHEM) developed at The University of New South Wales, particularly with regard to the forecast ozone distribution. This was achieved by assimilating the New South Wales State Environment Protection Authority (EPA) emissions inventory, consisting of road and non-road sources, and running the system over the Sydney metropolitan area for the four day period 25-28 February 1998. During this period ozone readings exceeded the EPA's goal of 8pphm on several occasions. The model forecasts of ozone distribution verified well with the EPA's ozone readings. This result has important implications for possible future use of the system as a tool for routinely predicting and assessing air quality.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.12
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• pp.138-146
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• 2010

On wind turbine generators, the speed and volume of the wind affect the turbine angle speed which finally determines the output level of the electric power. However it is very difficult to forecast correctly the future power output and quality based on previous fixed sampling methods. This paper proposes a variable sampling method based on Buneman frequency estimation algorithm to reflect the variations of the frequency and amplitude on wind power outputs. The proposed method is also verified through the performance test by comparing with the results from previous fixed sampling methods and the real measurement data.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.408-412
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• 2007

This paper presents, with some actual cases of employing clear case, as a R&D project configuration management tool, a new methodology for the evaluation and enhancement of project performance in R&D organization. This methodology enables you to forecast future results of the project with story telling skill, to innovate R&D project execution using the concept of Technology Supply Chain with global R&D human resources, to improve the performance of each development stage based on the voices of customer, and to enhance the quality of output and to minimize risk of project with timely positioning of R&D human resources.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.345-351
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• 2001

We are concerned with a multiple replenishment contract with a purchase price discount in a supply chain. The chain is composed of one supplier, one buyer and consumers for a product. The replenishment contract is based upon the well-known (s, Q) policy but allows contracting several firmed orders at a time with a price discount. Due to a larger forecast error of the future demand, the buyer should keep a higher level of safety stock to provide the same level of service of the usual (s, Q) policy but can reduce his purchase cost by placing larger quantity. Thus there exists a trade-off between the price discount and inventory holding cost. We present a model for the contract and an algorithm to find the optimum number of the firmed orders. Computer experiments show that the algorithm finds the global optimum solution very fast.