• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.48 no.1
• /
• pp.72-81
• /
• 2012

Using artificial neural network (ANN) technique, auction prices for common mackerel were forecasted with the daily total sale and auction price data at the Busan Cooperative Fish Market before introducing Total Allowable Catch (TAC) system, when catch data had no limit in Korea. Virtual input data produced from actual data were used to improve the accuracy of prediction and the suitable neural network was induced for the prediction. We tested 35 networks to be retained 10, and found good performance network with regression ratio of 0.904 and determination coefficient of 0.695. There were significant variations between training and verification errors in this network. Ideally, it should require more training cases to avoid over-learning, which leads to improve performance and makes the results more reliable. And the precision of prediction was improved when environmental factors including physical and biological variables were added. This network for prediction of price and catch was considered to be applicable for other fishes.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.5 s.43
• /
• pp.59-67
• /
• 2006

This paper proposes a forecasting method for court auction information system using exponential smoothing. The system forecast a highest bid price for claim analysis, and it is designed to offer an quota information by the bid price. For this realization, we implemented input interface of object data and web interface of information support. Input interface can be input, update and delete function and web interface is support some information of court auction object. We propose a forecasting method using exponential smoothing of a highest bid price for auto-claim analysis with real time information support and the results are verified the feasibility of the proposed method by experiment.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.6
• /
• pp.2196-2207
• /
• 2017

This paper presents a two-stage market auction model in a pool-based electricity market, which explicitly takes into account the system network security. The security network-constrained market auction model considers the use of corrective control to yield economically efficient actions in the post-contingency state, while ensuring a certain security level. Under this framework, the proposed model shows not only for quantifying the correlation between secure system operation and efficient market operation, but also for providing transparent information on the pricing system security for market participants. The two-stage market auction procedure is formulated using Benders decomposition (BD). In the first stage, the market participants bid in the market for maximizing their profit, and the independent system operator (ISO) clears the market based on social welfare maximization. System network constraints incorporating post-contingency control actions are described in the second stage of the market auction procedure. The market solutions, along with the BD, yield nodal spot prices (NSPs) and nodal congestion prices (NCPs) as byproducts of the proposed two-stage market auction model. Two benchmark systems are used to test and demonstrate the effectiveness of the proposed model.

• Korean Journal of Agricultural Science
• /
• v.46 no.3
• /
• pp.461-469
• /
• 2019

The aim of this study was to analyze the contribution of carcass traits (backfat thickness, eye muscle area, carcass weight and marbling score) and the season at slaughter to the price (auction and market) using squared semi-partial correlation. The season at slaughter (summer expressed as season_2, autumn as season_3, and winter as season_4) were added into the estimation as dummy variables, and spring was set as a default variable. In this study, the carcass grades of 22,298 Hanwoo steers slaughtered from 2012 to 2017 were used to performmultiple regression analysis. The rankings of the contribution of the carcass traits and the seasons at slaughter to the auction prices were in the order of marbling score (68.63%), season_4 (11.88%), backfat thickness (10.45%), eye muscle area (6.11%), season_3 (2.19%), season_2 (0.45%) and carcass weight (0.28%). (R-square of the regression = 0.4101). The rankings of the contribution to the total prices were in the order of carcass weight (51.74%), marbling score (32.12%), season_4 (6.04%), backfat thickness (5.54%), eye muscle area (3.22%), season_3 (1.14%), and season_2 (0.19%). (R-Square of the regression = 0.6486). As a result, season_3 and season_4 had a negative effect on the auction price and total price. Because of seasonal event such as Korean Thanksgiving Day and Korean New Year's Day on season_3 and season_4, much supply was needed to meet the high demand. Thus, the seasonal effect at slaughter could be another factor to be cosideredin when considering of slaughter or breeding.

• The KIPS Transactions:PartD
• /
• v.9D no.5
• /
• pp.963-970
• /
• 2002

In this paper, after defining the requirements to improve the processing of Internet auction, we design and implement Internet Auction Agent System (IAAS) based on the requirements. IAAS consists of a multi - agent system with buying/selling/negotiation agents for auction processing in accordance with the given price strategy, a graphic interface for supporting price strategies, an information agent for supplying real time information and remote control of agents, and DB. For the mobility, scalability, functionality and communication efficiency of agents, IAAS is implemented by using Java JDK1.3, Servlet, RMI and socket. Through the comparative analysis, we show that IAAS fulfills the requirements defined. IAAS can improve the convenience of users and the efficiency of auction processing, and thus increase the utilization of Internet auction.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.11
• /
• pp.2863-2874
• /
• 1999

Apartment auction is a system that is used for the citizens to get a house. This paper deals with the implementation of a web-based intelligent decision support system using OLAP technique and data mining technique for auction decision support. The implemented decision support system is working on a real auction database and is mainly composed of OLAP Knowledge Extractor based on data warehouse and Auction Data Miner based on data mining methodology. OLAP Knowledge Extractor extracts required knowledge and visualizes it from auction database. The OLAP technique uses fact, dimension, and hierarchies to provide the result of data analysis by menas of roll-up, drill-down, slicing, dicing, and pivoting. Auction Data Miner predicts a successful bid price by means of applying classification to auction database. The Miner is based on the lazy model-based classification algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm to reflect the characteristics of auction database.

• Journal of Electrical Engineering and Technology
• /
• v.1 no.1
• /
• pp.1-7
• /
• 2006

In an electricity market, the spot market is normally integrated with a forward or future market. The advantage of the forward market is to allow the market participants to deal in a part or the whole trading portfolio at a fix price in advance and to avoid risk associated to the uncertain price of the spot market. Japan has introduced a continuous auction base forward market from April 2005. This paper analyzes the Japanese forward market rules and operations, and introduces a new algorithm that may improve the efficiency of the market itself. The proposed algorithm enables us to give consideration to the specific characteristics of the power system and to integrate them in the auction mechanism. The benefits of the proposed algorithm are verified on an electronic simulation platform and the results described in this paper.

• The Journal of Society for e-Business Studies
• /
• v.5 no.1
• /
• pp.135-158
• /
• 2000

In recent years, many retail businesses jumped on the Internet auction bandwagon and paid substantially high fees to learn and develop proper business strategies for this new environment. Unlike what most businesses in the real world presume, this research shows that discriminatory-price ascending-bid auctions in a digital economy might be not very beneficial for the sellers on the Internet, if sellers sell the identical digital products through both a typical posted-price market and an auction. Using an extensive technology infrastructure along with suitable incentives and rules for market agents, we found that a discriminatory-price ascending-bid auction, which is the most popular auction mechanism on the Internet, serves consumers better than it does the sellers or producers in the digital economy. That is, the average prices for digital goods in these auctions are substantially lower than the prices in a posted-price market. This shows that it is not so wise for sellers to jump on the bandwagon of Internet auctions, if there is a market place with posted-price mechanisms which sells comparable items, or if a seller does not have special advantages or strategies in this new market institution. Electronic market mechanisms provide powerful means of understanding and measuring consumer characteristics including willingness-to-pay and other demographics for sellers or producers. Many concern that sellers may extract the entire surplus from the market by using customization on the Internet, thus consumers will be worse off in this digital economy. We found that these sellers who can customize their products and prices fail to capture the whole consumers surplus and cannot exercise a monopoly. One major explanation for this phenomenon is that the competition among the sellers prohibits them from charging prices according to customers demand for each product, where switching from one seller to another is not so difficult for the customers, and reselling products among the buyers are prohibited.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.2
• /
• pp.172-178
• /
• 2006

This paper presents a development of court auction information system using time series forecasting. The system forecast a highest bid price for claim analysis, and it is designed to offer an quota information by the bid price. For this realization, we implemented input interface of object data and web interface of information support. Input interface can be input, update and delete function and web interface is support some information of court auction object. We propose a forecasting method of a highest bid price for auto-claim analysis with real time information support and the results are verified the feasibility of the proposed method by experiment.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.01a
• /
• pp.83-88
• /
• 2001

To get the items that a buyer wants in Internet auction. he must search for the items through several auction sites. When the bidding starts, he(the buyer) needs to connect to these auction sites frequently so that he can monitor the bid stats and re-bid. A reserve-price auction reduces the number of connections, but this limits the user's bidding strategy. Another problem is equity between the buyer and the seller. Both the buyer and the seller should profit together within proper limits. In this paper, we propose an auction agent system using a collaborative mobile agent and a brokering mechanism called MoCAAS (Mobile Collaborative Auction Agent System), which mediates between the buyer and the seller and executes bidding asynchronously and autonomously. This reduces connection costs. offers more intelligent bidding and solves the equity problem.