• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.461-475
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• 2021

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.3
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• pp.33-42
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• 2006

From middle of 90's, SCM had emphasized the importance of connection of all companies which participate to a market, and outsourcing has emphasizes the slim structure of companies for pursing cost reduction and service improvement. But outsourcing as '2-1=1' and integration as '1+1=2' not adapt to the advanced market but return to the former state. Besides, it is impossible of subcontractors and logistics companies to execute these strategies which are win-win strategy and outsourcing. So many logistics companies have sharp competition in price recently. Therefore logistics companies have increased the need for a profitable business model and strategy. This paper researches the limitations of logistics market and redefines the role of logistics through the evolution of logistics. Also this paper proposes new role(Logister) and structure(Velcro structure) for not maker-oriented logistics but market-oriented logistics. The introductory case study will help to understand 2 terms(logister and velcro structure).

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.289-294
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• 2005

The international lighting market's growth about technologies and Methodologies to develop the products. but, domestic lighting fixtures are doing to price competition. Wish to accomplish process establishment for the effective Method to develop about the lighting technology and the new product design, through the development of PLS lighting technology and designs of 300W class lighting fixtures.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.100-110
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• 1990

An optimal raw mix model in stainless steel making is developed. The key raw materials in stainless steel making are stainless steel scrap, steel scrap, and alloy materials like Fe-Ni, Fe-Cr. Among those raw materials, the alloy metals are very expensive as well as rapidly price-changing items. Consequently, it is very important to develop an minimal cost raw mix scheme while the produced stainless steel satisfies the required specification in it's composition. The linear programming model is employed to determine the minimal cost raw mix scheme. Compared with the method being used, the developed linear programming model gives much faster and better solution (lower cost raw mix plan). Together with the linear programming model, the database is also developed, which includes the following: 1) data for raw materials, such as compositions, costs, densities, available inventory levels, and so on, 2) the required specifications process. The developed optimal raw mix model will be implemented in VAX computer.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.451-459
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• 2011

Fundamentally, there are 5 systems are needed for autonomous navigation of unmanned ground vehicle: Localization, environment perception, path planning, motion planning and vehicle control. Path planning and motion planning are accomplished based on result of the environment perception process. Thus, high reliability of localization and the environment perception will be a criterion that makes a judgment overall autonomous navigation. In this paper, via map matching using vehicle dynamic model and LIDAR sensors, replace high price localization system to new one, and have researched an algorithm that lead to robust autonomous navigation. Finally, all results are verified via actual unmanned ground vehicle tests.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.4
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• pp.28-44
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• 2023

This study investigates aircraft lessor business models by studying cases and interviewing experts to analyze investors and business strategies of aircraft lessor. The results confirm that there is a wide range of investors including institutional investors, financial institutions, insurance companies, corporations, and wealthy individuals for aircraft lessor. Aircraft lessors can be categorized based on its required rate of return (cost of capital) into bank-investing core, institutional investor-investing value-added, and hedge fund-investing opportunistic. Aircraft lessor decides leasing rate by aircraft purchasing price and lessee's credit rating. Core aircraft lessors invest in new aircrafts for new placement or sale-and-leaseback strategy requiring little technical risk in aircraft, value-added lessors invest in middle-aged aircrafts for re-leasing, opportunistic lessors invest in old aircrafts for freighter conversion or part-out strategy requiring high level of expertise. This study provides insights for future Korean aircraft lessor establishment and investment.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.8-14
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• 2009

A Hidden Markov Model(HMM)-based information extraction method is proposed in this paper. The proposed extraction method is applied to extraction of products' prices. The input of the proposed IESHMM is the URLs of a search engine's interface, which contains the names of the product types. The output of the system is the list of extracted slots of each product: name, price, image, and URL. With the observation data set Maximum Likelihood algorithm and Baum-Welch algorithm are used for the training of HMM and The Viterbi algorithm is then applied to find the state sequence of the maximal probability that matches the observation block sequence. When applied to practical problems, the proposed HMM-based system shows improved results over a conventional method, PEWEB, in terms of recall ration and accuracy.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.842-854
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• 2001

Today\`s first generation shopping agent is limited to comparing merchant offerings usually on price instead of their full range of attributes. Even in the full range comparison, there is not a good model which considers the overall features in the negotiation process. Therefore, the negotiation model needs to be extended to include negotiations over the more attributes. In this paper, we propose a negotiation model in the agent-mediated electronic commerce to negotiate over prices, product features, warranties and service policies based on utility theory and simple heuristics. We will describe a prototype agent-mediated electronic commerce framework called Pmart. This framework provides the software reuse and the extensibility based on the object-oriented technology. It is implemented on Windows-based platforms using Java and CORBA for the network transparency and platform independence.

• Journal of Distribution Science
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• v.12 no.2
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• The Journal of the Korea Contents Association
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• v.15 no.12
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• pp.483-492
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• 2015

This study involved elementary schoolchildren in Busan Metropolitan city and assumed the foundation of social cooperative associations that provide education services for meteorological fields, then we analyzed expected profits in a year for successful operation of first year. Twelve variables relating to profits and expenses were derived, and we used the decision tree for analyzing optimal expected profits. Profit-related variables were lecture's fee per hour and price of textbooks. Expense-related variables were production costs for the textbooks, annual salary for a teacher, education costs for a teacher, developing costs for the textbooks, traveling expenses, rental fees, and operating costs. Besides, by adding education demands, the number of grades, and the number of teachers, we analyzed changes in expected profits, considering variability of profits and expenses. As a result, despite of expected lower demands, to increase price of textbooks and education costs per hour was of advantage to enhance expected profits. The reason is that the more demand, the more increased production costs for textbooks, which is because not to make enough profits to offset the increased expenses due to lowered price of textbooks and education costs. Considering the value of public interest for social cooperative associations, price determination only concerning increase in demands will be avoided.