• International conference on construction engineering and project management
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• 2015.10a
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• pp.77-81
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• 2015

The unit price contracting is the standard contracting method for highway projects in the U.S. As a result, state highway agencies have collected a large amount of historical bid data that they can use to determine engineer's estimates for future projects. The estimator must carefully consider various characteristics of a new project such as its location to determine an engineer's estimate as accurate as possible before bid letting. Higher cost estimates can result in the loss of the available budget and lower cost estimates may lead to deferral and delay of projects. The study uses the historical bid data obtained from Iowa Department of Transportation and develops a Geographic Information System (GIS) tool to visually show the variation of unit prices over the map using a spatial interpolation technique. The interpolation map can be used to estimate the unit price of the item at any location across Iowa. This noble method allows the estimator to effectively and fully utilize the historical bid data in a very time efficient manner and determine more accurate cost estimation.

• The Journal of Information Systems
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• v.14 no.2
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• pp.55-74
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• 2005

In order to purchase a product cheaper, a lot of customers have been trying to search one or more marketplaces. Ever since the commercial use of the Internet, several types of marketplaces have been operating successfully on the Internet. Some of them are online shopping malls, auction markets, and group-buying markets. They have the price settlement mechanisms of their own. Online shopping malls where many stores are located support a customer to purchase the product that matches his/her requests such as price, function, design, and so forth. In online auction market, a customer can buy the product by making bids sequentially and competitively until a final price is reached. In online group-buying market, a customer can purchase the product by aggregating the orders from several buyers so that cheaper prices can be negotiated. The cheaper customers could purchase the same product item, the more satisfied they would be. However, it is very difficult for the customer to determine the marketplace to purchase, considering different kinds of marketplaces at the same time. Even though the purchasing price is cheapest in one marketplace, it is very difficult for customers to convince it the cheapest for all marketplaces. Therefore, rules and methods have been developed for purchase decision making in multiple marketplaces to reach the optimal purchase decision as a whole. They can maximize customer's utility and resolve the conflicts with other marketplaces through multi-agent negotiation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.10
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• pp.1390-1396
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• 2013

The paper estimated the reasonable market price of lead-acid battery energy storage system (BESS) intended for demand management of electricity customers. As time-of-use (TOU) tariffs have extended to a larger number of customers and gaps in the peak and off-peak rates have gradually risen, deployment of BESS has been highly needed. However, immature engineering techniques, lack of field experiences and high initial investment cost have been barriers to opening up ESS markets. This paper assessed electricity cost that BESS operation could save for customers and, based on the possible cost savings, estimated reasonable prices at which BESSs could become a more prospective option for demand management of customers. Battery scheduling was optimized to maximize the electricity cost savings that BESS would possibly achieve under TOU tariffs conditions. Basic economic factors such as payback period and return on investment were calculated to determine reasonable market prices. Actual load data of 12 industrial customers were used for case studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1091-1096
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• 2011

Currently, the concern about the environment is the issue all over the world, and in particular, carbon emissions of the power plants will not be able to disregard from the respect of generation cost. This paper proposes DR (demand response) as a method of reducing carbon emissions and therefore, carbon emissions cost. There are a number of studies considering DR, and in this paper, the effect of DR is focused on the side of carbon emission reduction effect considering Time-Of-Use (TOU) program, which is one of the most important economic methods in DSM. Demand-price elasticity matrix is used in this paper to model and analyze DR effect. Carbon emissions is calculated by using the carbon emission coefficient provided by IPCC (Intergovernmental Panel on Climate Change), and generator's input-output characteristic coefficients are also used to estimate carbon emission cost as well as the amount of carbon emissions. Case study is conducted on the RBTS IEEE with six buses. For the TOU program, it is assumed that parameters of time period partition consist of three time periods (peak, flat, off-peak time period).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2165-2172
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• 2010

This paper presents the operation algorithm of microgrid on the Real Time Pricing(RTP) for building the smart grid. RTP is higher power price variability than flat rate and time of use. However it has an effect on peak clipping and peak load shifting due to the increased price on peak time power demand. When the RTP are applied to the microgrid system, the proposed algorithm is able to be effective and economic operation. The implemented system is operated for the economic operation in microgrid connected with the power system. On the other hand, when the microgrid is operated on isolation mode, it focus on the improvement of stability and the power supply reliability of the sensitive loads. The test system are implemented and calculated on various operation modes based on non-dispachable generator output and RTP data for validating the proposed operation algorithm. The calculated results are compared to the implemented results using real-time simulator. It can be confirmed that the proposed operation system are identical results to the calculated one. When the proposed operation algorithm is applied to the system, it can be show the effectiveness of the peak clipping and peak load shifting and the improvement of economic feasibility.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• Journal of IKEEE
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• v.22 no.2
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• pp.369-374
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• 2018

In this paper, the energy cost saving in multi-channel electric vehicle charging system. Joint use of the electric car charger battery state of charging proposed a method based charging. A linear programming with two type is used for optimal control, and the time-of-use price is included to calculate the energy costs. Simulation results show that the reductions of energy cost and peak power can be obtained using proposed method.

• Journal of the Korean Society of Costume
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• v.57 no.3 s.112
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• pp.78-89
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• 2007

The objectives of this research were to investigate the relationships between purchase motives and post-purchase satisfaction of cosmetics at internet shopping malls, and to reveal how cosmetic purchase motives and demographic variables influence to the post-purchase satisfaction. Subjects were 202 females in Seoul who had experiences of cosmetic shopping at internet. Five dimensions of cosmetic purchase motives at internet shopping malls were derived by factor analysis: 'information provision', 'service/quality', 'time saver', 'use convenience', and 'economical efficiency'. Consumers showed relatively high cosmetic purchase motives at internet shopping malls because the prices of on-line shopping mall products were cheaper than those of off-line, and because it was easy to compare various products at internet shopping malls. The motive of 'information provision' had significant positive relationships with the satisfaction of price, quality, color, volume, and skin suitability of cosmetics. The motive of 'time saver' and 'use convenience' had positive relationships with the satisfaction of price and quality. The motives of 'service/quality' and 'use convenience' were higher in career women than in college women. The middle class consumers and the consumers who use internet more had a high 'information provision' motive in shopping cosmetics at internet. The post-purchase satisfaction of cosmetics was influenced most by the experience postscripts and next by economical efficiency, frequency of access to the internet cosmetic malls, and social class(-) in order.

• Journal of the Korea Society of Computer and Information
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• v.7 no.2
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• pp.196-204
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• 2002

This paper examines the price discovery role of the KOSPI 200 futures index for its cash index. It was used the intrady data for KOSPI 200 and futures index from July 1998 to June 2001. The existing Preceding study for KOSPI 200 futures index was used the data of early market installation, but this study is distinguished to use a recent data accompanied with the great volume of transaction and various investors. We established three hypothesis to examine whether there is the price discovery role in the KOPSI 200 futures index and the characteristics of that. First, to examine whether the lead-lag relation is induced by the infrequent trading of component stocks, observations are sorted by the size of the trading volume of cash index. In a low trading volume, the long lead time is reported and the short lead time in a high volume. It is explained that the infrequent trading effect have an influence on the price discovery role. Second, to examine whether the lead-lag relation is different under bad news and good news, observations are sorted by the sign and size of cash index returns. In a bad news the long lead time is reported and the short lead time in a good news. This is explained by the restriction of"short selling" of the cash index Third, we compared estimates of the lead and lag relationships on the expiration day with those on days prior to expiration using a minute-to-minute data. The futures-to-spot lead time on the expiration day was at least as long as other days Prior to expiration, suggesting that "expiration day effects" did not demonstrate a temporal character substantially different form earlier days. Thus, while arbitrage activity may be presumed to be the greatest at expiration, such arbitrage transactions were not sufficiently strong or Pervasive to alter the empirical price relationship for the entire day. for the entire day.

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.105-114
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• 2012

Recently, global warming, energy shortage, and environmental disruption have been serious problems in every nation. It became more and more important to reduce the emission of CO2 and to use of energy efficiently. Smart grid was also introduced using the rapidly developing information technology. It deployed the mutual communication concept between customers and the suppliers in the electricity supply. There were increasing demands to adopt the smart meter and to present incentive for efficient energy usage in many developed countries. The objective of this research was to develop the optimal real time pricing model which maximized the profit of the power retailer and reduced the usage of energy. The simulation study was given to show the usefulness of the model. Simulation considered the customer demand response rate and price elasticity rate. The price elasticity rate was compared in the condition of fixed value according to time and variable value according to the customers. The optimal price model could maximize the profit of the power retailer and reduce the energy usage of the consumers.