• Nuclear Engineering and Technology
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• v.7 no.4
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• pp.295-310
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• 1975

A system of model price data for the fuel cost estimation of the Go-ri plant is developed. With the application of MITCOST-II computer code the levelized unit fuel costs over the entire lifetime of the plant are evaluated. It is found that the overall levelized unit fuel cost is 7.332 mills/Kwhe and that the uranium ore and enrichment service represent more than 85% of the unit cost, assuming a simple once-through fuel cycle process with no reprocessing of the spent fuel. The effects of the cost fluctuations in these fuel cycle elements and the capacity factor changes are also evaluated. The results indicate that the fuel costs are most sensitive to the variation of uranium ore price. Efforts must, therefore, be employed for the arrangement of cheap and timely supply of uranium ore in order to achieve the economic generation of nuclear power.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.479-487
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• 2013

State Highway Agencies (SHAs) have started utilizing cost-plus-time bidding (A+B bidding) since Federal Highway Agency (FHWA) declared it operational on May 4, 1995. Although this technique has successfully accelerated many projects by incorporating construction time in the bidding competition, a framework to illustrate the interactions of incentive/disincentive (I/D) rates on the competitiveness of contractors participating in the bid competition is yet to be developed. In a previous research, authors indicated that for each bid competition there is an efficient cap for I/D rates which are dictated by the capabilities of contractors in project acceleration. However, the results of previous study were based on the assumption that there is a statistically significant relationship between cost and time. In this study, the entire cost-plus-time projects implemented by the Oklahoma Department of Transportation (ODOT) were investigated. Then the significance of relationship between cost and time were analyzed for each contractor utilizing Analysis of Variance (ANOVA) technique, and the price-time function of each contractor was determined by regression analysis. The results of the analysis indicate that there is a significant relationship between cost and time for the majority of contractors. However, a quadratic relationship is not always significant and for some contractors a linear price-time relationship is significant. The results of this project can be used not only by ODOT to optimize the incentive/disincentive rates but also by contractors to determine the most competitive strategies of other bid participants.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1504-1511
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• 2009

By development of renewable energies and high-efficient facilities and deregulated electricity market, the operation cost of distributed generation(DG) becomes more competitive. The amount of distributed resource is considerably increasing in the distribution network consequently. Also, international environmental regulations of the leaking carbon become effective to keep pace with the global efforts for low-carbon paradigm. It contributes to spread out the business of DG. Therefore, the operator of DG is able to supply electric power to customers who are connected directly to DG as well as loads that are connected to entire network. In this situation, community energy system(CES) having DGs is recently a new participant in the energy market. DG's purchase price from the market is different from the DG's sales price to the market due to the transmission service charges and etc. Therefore, CES who owns DGs has to control the produced electric power per hourly period in order to maximize the profit. If there is no regulation for carbon emission(CE), the generators which get higher production than generation cost will hold a prominent position in a competitive price. However, considering the international environment regulation, CE newly will be an important element to decide the marginal cost of generators as well as the classified fuel unit cost and unit's efficiency. This paper will introduce the optimal operation of CES's DG connected to the distribution network considering CE. The purpose of optimization is to maximize the profit of CES and Particle Swarm Optimization (PSO) will be used to solve this problem. The optimal operation of DG represented in this paper is to be resource to CES and system operator for determining the decision making criteria.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.1
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• pp.71-85
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• 2013

This paper examines the impact of urban space structure on real estate markets, especially on commercial real estate markets. Based on a large scale of GIS dataset, volumes of each land use type are examined. This vast dataset enables 3-dimensional analysis of land use in the entire Seoul area, overcoming the limits of previous research relying on simple 2-dimensional analysis. After then, the Herfindahl index is used to calculate the level of mixed-uses. It analyzes whether a building price is influenced by circumjacent commercial buildings and its residential development pattern. The regression outcomes verify that a nearby area's development patterns make an impact on an office building price. It shows the possibility that a new-urbanism's argument can be actualized.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1B
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• pp.24-33
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• 2009

This paper considers network competition where the subscribers experience network congestion when they use the network and the network providers determine the network price and capacity. This paper discusses the impact of the network competition on social welfare. Network provider determines the price and capacity considering this characteristics of this sensitivity to network congestion where the subscriber has different preference about the congestion. This paper shows that network provider who wants to serve the intolerable customers (who is very sensitive to the congestion) offers higher price and capacity. However, this provider prepares lower capacity than socially optimal capacity. This is because the network provider seeks to earn more profits from additional subscriber while it is desirable to invest the capacity to give the entire subscribers a non-congestion network in the view of social welfare.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.223-228
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• 2020

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.219-224
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• 2020

One of the main reasons for the commercial failure of various early blockchain-based art trade platforms, including Maecenas, is the lack of clear ways for owners to fully restore ownership of artworks. In this paper, we proposed a procedure for the owner of the artwork to rebuy shares and restore his or her entire ownership in the blockchain system. Using the proposed procedure, we can find a balanced price between the owner and investors, and then restore ownership through a public purchase of the stake. The balanced price can be induced by penalizing the owner for proposing unreasonably low price, and by rewarding investors for deciding reasonable prices. The proposed procedure of restoring ownership is expected to be utilized not only on the block chain-based art trading platform but also on the block chain-based trade platform in other applications.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.589-599
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• 2016

Most existing resource management problem models arise from the original desire of allocating resources in either a user-centric or network-centric manner. The difference between their objectives is obvious: user-centric methods attempt to optimize the utility of individual users, whereas network-centric models intend to optimize the collective utilities of the entire network. In this paper, from the above two aspects, we analyze the robust power control problem in device-to-device (D2D) communication underlaying cellular networks, where two types of channel uncertainty set (e.g., ellipsoidal and column-wise) are considered. In the user-centric method, we formulate the problem into the form of a Stackelberg game, where the energy efficiency (EE) of each user is the ingredient of utility function. In order to protect the cellular user equipment's (CUE) uplink transmission, we introduce a price based cost function into the objectives of D2D user equipment (DUE). The existence and uniqueness of the game with the influence of channel uncertainty and price are discussed. In the network-centric method, we aim to maximize the collective EE of CUEs and DUEs. We show that by the appropriate mathematical transformation, the network-centric D2D power control problem has the identical local solution to that of a special case of the user-centric problem, where price plays a key role. Numerical results show the performance of the robust power control algorithms in the user-centric and network-centric models.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.17-24
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• 2022

Bitcoin is a peer-to-peer cryptocurrency designed for electronic transactions that do not depend on the government or financial institutions. Since Bitcoin was first issued, a huge blockchain financial market has been created, and as a result, research to predict Bitcoin price data using machine learning has been increasing. However, the inefficient Hyper-parameter optimization process of machine learning research is interrupting the progress of the research. In this paper, we analyzes and presents the direction of Hyper-parameter optimization through experiments that compose the entire combination of the Timesteps, the number of LSTM units, and the Dropout ratio among the most representative Hyper-parameter and measure the predictive performance for each combination based on Bitcoin price prediction model using LSTM layer.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.