• Environmental and Resource Economics Review
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• v.22 no.1
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• pp.3-29
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• 2013

The purpose of this study is to estimate consumer preferences on hybrid cars and electric cars by employing a choice experiment reflecting the various market conditions, such as different projected market shares of green vehicles and $CO_2$ emission regulations. Depending on different market scenarios, we examine as to which attribute and individual characteristic affect the preferences of potential consumers on green vehicles and further, forecast the potential market shares of green cars. The primary results, estimated by a conditional logit and panel probit models, indicate that sales price, fuel cost, maximum speed, emission of air pollutants, fuel economy, and distance between fuel stations can significantly affect consumer's choice of environment-friendly cars. The second finding is that the unique features of electric cars might better appeal to consumers as the market conditions for electric cars are improved. Third, education, age, and gender can significantly affect individual preferences. Finally, as the market conditions become more favorable toward green cars, the forecasted market shares of hybrid and electric vehicles will increase up to 67% and 14%.

• KIEAE Journal
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• v.8 no.1
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• pp.19-24
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• 2008

The wind-power among the new and renewable energies uses the wind, a limitless, clean and pure energy which is available at any place. It requires low installation cost compared to the generation of other renewable energies, and is easy to operate, and furthermore, can be automated for operation. Korea has been taking a great deal of interest in the development of renewable energy generating equipment, specifically wind power generation as the nation has a nearly total reliance on imported petroleum. A measuring poll 30m high was installed at a location with an altitude of 142m above the sea level in order to measure and analyze the wind power potentiality at H University's Asan Campus, and the wind velocity and wind direction were measured for 1 year. As for the wind power resource of the area adjacent to Asan campus, the Weibull Distribution coefficient was C=2.68, K =1.29 at H30m. Weibull Distribution coefficient was modified on the basis of compensated wind velocity (=3.1m/s) at H 60m, and the energy density was $42W/m^2$. AEP 223,750 KWh was forecast based on the simulation of an 800KW grade wind turbine. It is considered that the wind power generation has to be studied further in the inland zone with low wind velocity to cope with the possible exhaustion of fossil fuel and ensure a sustainable environmental preservation.

• Korean Journal of Agricultural Science
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• v.23 no.2
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• pp.285-300
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• 1996

This study is aimed to provide data of low-input rice cultivation for agricultural policy, to reveal the problems of low-input cultivation through comparing the economic result of low-input cultivation with the common one, to search for solution or mitigation of the problems of low-input cultivation, and to forecast the future prospect of low-input rice cultivation. The following were the results obtained from the survey and analysis. The working hours per 10a inputted 45.4 hours which is 32% more than 34.5 hours of common cultivation. Yield per 10a was 355kg which was 101kg less than 456kg of common cultivation. But the farm received price per kg was 1,984.9 won which was 547.9 won more than 1,436.5 won of common cultivation. Gross receipts per 10a was 704,438 won which was higher than 655,044 won of common cultivation, and management cost was 230,820 won which slightly higher than 188,157 won of common cultivation. Consequently, the income of low-input rice cultivation was 473,617 won which somewhat exceed to 466,887 won of common cultivation.

• Journal of the Korean Institute of Navigation
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• v.19 no.1
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• pp.33-48
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• 1995

In the process of containerization, the problem of regional maldistribution of container management plan arises seriously due to several factors like a number of unbalances of containers between loading and discharging ports. This study focus on the minimizing cost. This study is composed of two models which in effective management decision making show decision of the number of containers and transfer of empty containers. One is decision of the number of containers which carriers should possess by appropriate forecasting and the other is effective management decision making which includes the transfer of empty containers on calling ports. This study has suggested as follows, First, the Time Series analysis method, especially the "Exponential Smooting with Trend Adjustment" was used to forecast the trade volumes for the designated traffic route. Second, the Time Series analysis method in deciding the optimal number of owned container at the unbalances trade situation between East Bound and West Bound service, most important variables were found such as total traffic volume, the calling interval at a port, the number of days of voyage and the length of stay on shore of container for the optimal number of owned container. Third, effective management decision making model, which makes it possible to analyze the impacts of change in important matters such as lease and positioning policy, and actually influence decision making.on making.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.928-931
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• 2014

The detection of human behavior from social media revolutionized health, business, criminal and political prediction. Significance of it, in incentive transformation of public opinion had already proven for developed countries in improving democratic process of elections. In $3^{rd}$ World countries, voters poll votes for personal interests being unaware of party manifesto or national interest. These issues can be addressed by social media, resulting as ongoing process of improvement for presently adopted electoral procedures. On the optimistic side, people of such countries applied social media to garner support and campaign for political parties in General Elections. Political leaders, parties, and people empowered themselves with social media, in disseminating party's agenda and advocacy of party's ideology on social media without much campaigning cost. To study effectiveness of social media inferred from individual's political behavior, large scale analysis, sentiment detection & tweet classification was done in order to classify, predict and forecast election results. The experimental results depicts that social media content can be used as an effective indicator for capturing political behaviors of different parties positive, negative and neutral behavior of the party followers as well as party campaign impact can be predicted from the analysis.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.3
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• pp.26-30
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• 1996

The successful development of the automobile industry in Korea is the firstfruit of the epochmaking automobile policy in 1973: Top-Down mass production of Korean model cars. In Top-Down system, indigenous model, which is the most important and difficult part of the automobile industry, is devised first, followed by the mass production of the major parts, such as bodies, engines, etc. This system is in contrast to the prevailing Bottom-up system in the third-world, which assembles automobiles by gradually adding up simple domestic parts. The Government, faced with a vicious circle of depen- dence on assembly of foreign cars, high prices, small demand and low production, decided it was time to move to the mass production of the indigenous models. It was hoped that the efficiencies of low cost Korean models would be epochally improved by overcoming the sway of foreign models and by strengthening the production capabilities of the main parts. In contrast to these develop- ment planning, assembled vehicles numbered only 26,300 units in 1973. In the year of 1995, the total number of automobile production have already passed 2,534,000. As steady growth in worldwide demand is forecast, Korea will able to play a major role as an important automobile maker of the next century.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.306-320
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• 2021

To ensure safe port operations around the world, it is important to solve mooring problems. In particular, the many ports that face open seas have difficulties with long-period waves. As a countermeasure, the installation of a breakwater is proposed for mooring safety. However, this often cannot be put into practice because of financial issues. Instead, port terminals control berthing schedules with weather forecasting. However, mooring problems remain unsolved, because of inaccurate wave forecasting. To quantify the current situation, numerical simulations are presented with ship motions, fender deflections, and rope tensions. In addition, novel simulations for mooring ropes are proposed considering tension, friction, bending fatigue, and temperature. With this novel simulation, the optimal mooring method in terms of safety and economic efficiency was confirmed. In terms of safety, the optimal mooring method is verified to minimize dangerous mooring situations. Moreover, the optimal mooring method shows economic benefits and efficiency. It can help to reinforce the safety of port terminals and improve the efficiency of port operations.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3923-3942
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• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3584-3602
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• 2022

NFV realizes flexible and rapid software deployment and management of network functions in the cloud network, and provides network services in the form of chained virtual network functions (VNFs). However, using VNFs to provide quality guaranteed services is still a challenge because of the inherent difficulty in intelligently scaling VNFs to handle traffic fluctuations. Most existing works scale VNFs with fixed-capacity instances, that is they take instances of the same size and determine a suitable deployment location without considering the cloud network resource distribution. This paper proposes a traffic forecasted assisted proactive VNF scaling approach, and it adopts the instance capacity adaptive to the node resource. We first model the VNF scaling as integer quadratic programming and then propose a proactive adaptive VNF scaling (PAVS) approach. The approach employs an efficient traffic forecasting method based on LSTM to predict the upcoming traffic demands. With the obtained traffic demands, we design a resource-aware new VNF instance deployment algorithm to scale out under-provisioning VNFs and a redundant VNF instance management mechanism to scale in over-provisioning VNFs. Trace-driven simulation demonstrates that our proposed approach can respond to traffic fluctuation in advance and reduce the total cost significantly.