• Current Industrial and Technological Trends in Aerospace
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• v.6 no.1
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• pp.14-24
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• 2008

After 2001, the world aircraft industry grows consistently with world's economic recovery. The environmental changes after 9.11, including the market decent and revival, rise in oil price, and the environmental problems, make the aircraft industry change gradually. The increasing demand of point-to-point flight needs over 200 seat class large jets and changes the main model of regional jet over 100 seat class. And the needs of various flight schedule raises the demand of business jet and VLJ. The competition between airliners including the main streams, the regionals and the low prices goes harder and it needs more efficient airplanes which reduce the cost. In the military side, still the development of 5th generation fighter is proceeding and it diffuses to the more countries. Before its popularization, the 4th generation fighter is chosen for good alternatives of it. And there are some changes in the military demand after the war against terrorism. The army needs more unmanned reconnaissance and they want new aircraft which gives more accessibility.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.77-89
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• 2009

With an increasing number of cars in Korea, the government is constantly providing roads and their related facilities. However the fundamental problems of cities like the structure of cities and the environment of roads make the traffic congestion of downtowns. To solve this problem the construction of toll roads such as tunnels and bridges is increasing but use rates of drivers is low. With more tolls required, less persons will use the roads. Thus this study is to consider offering discounted charges when using the two or more toll roads together. This study analyzes the impact that discounted charges would bring to the demand. In the meantime we looks into what the proper range should be for the discount. The results of this study are expected to be used as basis for the introduction of a discount system in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.933-940
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• 2018

The conversion of the internal combustion engine vehicle to the electric vehicle is suggested as a solution to the problem of global climate change and environmental pollution. Accordingly, this study was started to promote the use of electric vehicles. The purpose of this study is to identify the basic background knowledge and current status of electric vehicles in Korea and abroad, and expand from previous understanding on which factors affect ones choice on electric vehicles by considering individual characteristics and context in detail. In the analysis, a set of demand forecasting models were constructed by grouping the respondents based on the household characteristics as well as the vehicle ownership. At the time in need for better understanding of the feasibility of electric vehicles, it is expected that the research can assist the promotion of electric vehicles. In the follow-up study, I would like to continue the research on the activation of electric vehicles.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.

• The Journal of Fisheries Business Administration
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• v.41 no.1
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• pp.25-52
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• 2010

The purpose of this paper is to suggest major strategies and necessary new projects for the medium- and long-term development of the Korean Fisheries Outlook Project. To suggest the Korean Fisheries Outlook Center with the above purpose, this paper employs Analytic Hierarchy Process analysis based on surveys obtained by special groups related with the KFOP. The survey is broadly composed of two goals; the medium- and long-term development directions and setting up of new furtherance projects. Each goal has upper and lower strategies respectively. The first goal, the medium- and long-term development directions, has four factors as upper strategies. The upper strategies are composed of accuracy, efficiency, timeliness, and political effectiveness of the fisheries outlook information. In addition, each upper strategy has three lower strategies respectively. For example, accuracy of the fisheries outlook information includes strength of data collection function, strength of satellite photography function, and strength of data analysis function. The second goal, setting up of new furtherance projects, has three factors as upper strategies. The upper strategies consist of accuracy promotion of outlook information using high-technique, field expansion of outlook species, and strength of analyzing function on oversea fisheries information. Each upper strategy has three lower strategies respectively. For instant, accuracy promotion of outlook information using high-technique has strength of information analysis function covered from production to consumption, strength of satellite information function, and structure of forecasting model on demand and supply by outlook species. The above upper and lower strategies were analytically drawn out through insightful interviews with special groups such as officials of the government, presidents of the producer and distributor groups, and researchers of the Korea Maritime Institute and other research institutes. As a result of AHP analysis, first, priorities of upper strategies with the medium- and long-term development directions are analyzed as accuracy, timeliness, political effectiveness, and efficiency in order. Also, priorities of all lower strategies reflecting priorities of upper strategies are examined as includes strength of data collection function on the fisheries outlook information, delivery of rapid information on outlook products for all people interested, strength of data analysis function on fisheries outlook information, strength of consumption outlook function on fish products, and strength of early warning system for domestic fish products in order. Second, priorities of upper strategies with the setting up of new furtherance projects are analyzed as accuracy promotion of outlook information using high-technique, field expansion of outlook species, and strength of analysis function on oversea fisheries information in order. In addition, priorities of all lower strategies reflecting priorities of upper strategies are examined as building up of forecasting model on demand and supply by outlook species, strength of information analysis function covering all steps from production to consumption, expansion of consumption outlook for consumers, strength of movement analysis function of oversea farming industry, and outlook expansion of farming species.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.93-100
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• 2010

Trip generation is the first step in the conventional four-step model and has great effects on overall demand forecasting, so accuracy really matters at this stage. A linear regression model is widely used as a current trip generation model for such plans as urban transportation and SOC facilities, assuming that the relationship between each socio-economic index and trip generation stays linear. But when rapid urban development or an urban planning structure has changed, socio-economic index data for trip estimation may be lacking to bring many errors in estimated trip. Hence, instead of assuming that a socio-economic index widely used for a general purpose, this study aims to develop a new trip generation model by type based on the market separation for the variables to reflect the characteristics of various zones. The study considered the various characteristics (land use, socio-economic) of zones to enhance the forecasting accuracy of a trip generation model, the first-step in forecasting transportation demands. For a market separation methodology to improve forecasting accuracy, data mining (CART) on the basis of trip generation was used along with a regression analysis. Findings of the study indicated as follows : First, the analysis of zone characteristics using the CART analysis showed that trip production was under the influence of socio-economic factors (men-women relative proportion, age group (22 to 29)), while trip attraction was affected by land use factors (the relative proportion of business facilities) and the socio-economic factor (the relative proportion of third industry workers). Second, model development by type showed as a result that trip generation coefficients revealed 0.977 to 0.987 (trip/person) for "production" 0.692 to 3.256 (trip/person) for "attraction", which brought the necessity for type classifications. Third, a measured verification was conducted, where "production" and "attraction" showed a higher suitability than the existing model. The trip generation model by type developed in this study, therefore, turned out to be superior to the existing one.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• The Journal of Information Systems
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• v.29 no.3
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• International Commerce and Information Review
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• v.10 no.1
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• pp.395-414
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• 2008

The objective of this study is to introduce the concepts and theories of conditional heteroscedastic volatility models and the news impact curves and apply them to the Korea inbound tourism market. Three volatility models were introduced and used to estimate the conditional volatility of monthly arrivals of inbound tourists into Korea and news impact curves according to the three models. Results of this study are as follows. As the proportion of American tourists occupied a large amount of Korea inbound tourism market, the markets' forecasting is very important. The news impact curves which used EGARCH model (1,1) and TGARCH model(1,1), with data on these tourists to Korea showed an asymmetry effect of volatility. It was common that bad news means that it was estimated more sensitively than good news. From these results, we will notice that American tourists who visited Korea only for tourism are affected by good news. The result suggests that the Korea government and tourism industry should pay more attention to changes in the tourism environment following bad news because conditional volatility increases more when a negative shock occurs than when a positive shock occurs.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.6
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• pp.95-101
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• 2013

Social media has many advantages. It can gain latest information with real time, be spread rapidly, easily be reproduced and distributed regardless of its form. These advantages can result in real time predictions using the latest information, which is possible due to the increase in social demand for more quick and accurate economic variable predictions. In this paper we adopted ARIMAX and ECM model to predict the unemployment rate and as a social information we used the Google Index provided by Google Trend. Also we used News Index as a domestic social information. The process of fitting statistical model considered in this paper can be adopted to predict various socio/economic indices as well as unemployment rate.