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

• Cartoon and Animation Studies
• /
• s.17
• /
• pp.83-95
• /
• 2009

As an exploratory research on China's animation, this study aims to enhance an understanding of the trends and characteristics of China's animation through examining its history and to forecast its future development trajectory. From its founding to recent period, China tried to maintain Communist political system through imbuing national identity to its people through management and supervision of media products under direct government's leadership in combination with ideological education. Such policy was also implemented in animation, major audience of which is children. With regard to the introduction of the policy and its influence, five historical phases could be identified as follows. During the first phase, from the founding of the Republic until the Cultural Revolution, national culture was introduced to China's animation. The second phase, which corresponds with the Cultural Revolution period, marks the decline of national culture. National culture was reemphasized during the third phase that follows the Cultural Revolution, which led to the nomination of the 'China school,' followed by the fourth phase, during which China's animation suffered the second decline due to the spread of TVs and foreign animation imports. Reintroduction of national culture on China's animation in the context of rapid industrialization process before and after 2000 characterizes the recent phase. It can be expected that although there could be some change in methods and forms, China's animation, which introduced national culture from its inception and maintained remarkable resilience following the period of decline, will continuously stress the its own national cultural identity.

• Journal of Korea Technology Innovation Society
• /
• v.19 no.4
• /
• pp.799-821
• /
• 2016

Unmanned Aerial Vehicles (UAVs) are essential systems for Intelligence, Surveillance, and Reconnaissance (ISR) operations in current battlespace. And its importance will be getting extended because of complexity and uncertainty of battlespace. In this study, we forecast the advancement of 96 UAVs during the period of 32 years from 1982 to 2014 using TFDEA. TFDEA is a quantitative technology forecasting method which is characterized as non-parametric and non-statistical mathematical programming. Inman et al. (2006) showed that TFDEA is more accurate in forecasting compared with classical econometrics (e.g. regression). This study got 4.06% point of annual technological rate of change (RoC) for UAVs by applying TFDEA. And most UAVs in the period are inefficient according to the global SOA frontiers. That is because the countries which develop UAVs are in the middle class of technological level, so more than 60% of world UAVs markets are shared by North America and Europe which are advanced countries in terms of technological maturity level. This study could give some insights for UAVs development and its advancement. And also can be used for evaluating the adequacy of Required Operational Capability (ROC) of suggested future systems and managing the progress of Research and Development (R&D).

• Journal of Navigation and Port Research
• /
• v.36 no.6
• /
• pp.521-526
• /
• 2012

More efficient and effective volume management of trade cargo is recently requested due to FTA with foreign country. Above all, the grain is the main cargo needed in Korean food life and was appointed as the core trade cargo during FTA. This study is aimed to forecast future demands of grain volumes which are handled at Incheon port because most of the grain volumes are traded at Incheon port in Korea. System Dynamics (SD) was used for forecasting as the methodology. Also, population, yearly grain consumption per a man, GDP, GRDP, exchange rate, and BDI were used as the factors that influence grain volumes. Simulation duration was from 2000 to 2020 and real data was used from 2000 to 2007. According to the simulation, 2020's grain volumes at Incheon port were forecasted to be about 2 million tons and grain volumes handled at Incheon port were continuously reduced. In order to measure accuracy of the simulation, this study implemented MAPE analysis. And after the implementation, the simulation was decided as a much more accurate model because MAPE value was calculated to be 6.3%. This study respectively examined factors using the sensitivity analysis. As a result, in terms of the effects on grain volume in Incheon Port, the population factor is most significant and exchange rate factor is the least.

• Journal of Service Research and Studies
• /
• v.3 no.2
• /
• pp.75-89
• /
• 2013

In the services industry and firms, the successful new service development is very important issue today, But the innovation process for service firms is comprehensively little treated until now. This study was performed to suggest the new service development process model for the firms level in the perspective of the radical service innovation. So, in this paper the new process development model can be made by reviewing the concepts about the radical service innovation and by analyzing the some existing new service development process models. In the suggested service development process model, the three key process such as technology forecast, market analysis, and strategy development were included for front phase activity as the new service development process. Also the four key process for searching phase, and the other three key process for implementation phase were included. And for the application for the service firms' service innovation, the innovation's outcome estimation reference model is included. I hope to be executed the various case research and the improvement and optimization for this suggested process model in the future.

• Journal of Korea Water Resources Association
• /
• v.42 no.1
• /
• pp.61-73
• /
• 2009

To efficiently carry out the flood management of a multipurpose dam, two flood forecasting models are developed, each of which has the capabilities of forecasting upstream inflows and flood discharges downstream of a dam, respectively. The models are calibrated, validated, and evaluated by comparison of the observed and the runoff forecasts upstream and downstream of Namgang Dam. The upstream inflow forecasting model is based on the Grey system theory and employs the sixth order differential equation. By comparing the inflows forecasted by the models calibrated using different data sets with the observed in validation, the most appropriate model is determined. To forecast flood discharges downstream of a dam, a Grey model is integrated with a modified Muskingum flow routing model. A comparison of the observed and the forecasted values in validation reveals that the model can provide good forecasts for the dam's flood management. The applications of the two models to forecasting floods in real situations show that they provide reasonable results. In addition, it is revealed that to enhance the prediction accuracy, the models are necessary to be calibrated and applied considering runoff stages; the rising, peak, and falling stages.

• Journal of Acupuncture Research
• /
• v.21 no.1
• /
• pp.1-20
• /
• 2004

Pulse-feeling took its origin from making a diagnosis along meridians in the course of discovering and forming meridians and for a long time its meaning was mixed with meridians in the course of recognizing "The Pulse" then was separated from meridians in the early days of Western Han Dynasty. Ancient pulse-feeling methods are pulse-feeling method by the twelve regular meridians, pulse-feeling method by three regions and nine modes, pulse-feeling method by Inyeong(人迎) and Chon-gu(寸口), etc. Pulse-feeling was changed in proportion to diagnostic purpose and method of treating and if method and region of pulse-feeling is arranged, we will infer correlation between meridians and pulse-feeling and will infer transitional system of past pulse-feeling and will forecast transition of future pulse-feeling. As the result that I study the transition of the above three pulse-feeling methods of meridians diagnosis: 1. Three pulse-feeling methods of meridians diagnosis flowed into diagnostic method by taking pulse of setting six region for Chon(寸), Gwan(關) and Cheok(尺), i.e. the Chon[寸] spot pulse of $\ll$Nan-gyeong$\gg$ and were changed into diagnostic method being fit for use of five Su points, The Front-Mo points and Back-Su points that grasp the pathology of mutual internal organs and treat the disease. 2. Today it is suggesting the transition of another pulse-feeling method that do not apply diagnostic method by taking pulse of setting six region for Chon(寸), Gwan(關) and Cheok(尺), i.e. the Chon[寸] spot pulse of $\ll$Nan-gyeong$\gg$ to 19C Sasang(四象) Constitutional Medicine or 20C Eight Constitutional Medicine.

• Journal of Internet Computing and Services
• /
• v.18 no.4
• /
• pp.121-131
• /
• 2017

With emergence of Internet, social media, and mobile service, the consumers have actively presented their opinions and sentiment, and then it is spreading out real time as well. The user-generated text data on the Internet and social media is not only the communication text among the users but also the valuable resource to be analyzed for knowing the users' intent and sentiment. In special, economic participants have strongly asked that the social big data and its' analytics supports to recognize and forecast the economic trend in future. In this regard, the governments and the businesses are trying to apply the social big data into making the social and economic solutions. Therefore, this study aims to reveal the capability of social big data analysis for the economic use. The research proposed a social big data analysis model and an online consumer sentiment index. To test the model and index, the researchers developed an economic survey ontology, defined a sentiment dictionary for sentiment analysis, conducted classification and sentiment analysis, and calculated the online consumer sentiment index. In addition, the online consumer sentiment index was compared and validated with the composite consumer survey index of the Bank of Korea.

• KDI Journal of Economic Policy
• /
• v.12 no.4
• /
• pp.3-20
• /
• 1990

The multisector model is designed to analyze and forecast structural change in industrial output, employment, capital and relative price as well as macroeconomic change in aggregate income, interest rate, etc. This model has 25 industrial sectors, containing about 1,300 equations. Therefore, this model is characterized by detailed structural disaggregation at the sectoral level. Individual industries are based on many of the economic relationships in the model. This is what distinguishes a multisector model from a macroeconomic model. Each industry is a behavioral agent in the model for industrial investment, employment, prices, wages, and intermediate demand. The strength of the model lies in the simulating the interactions between different industries. The result of its simulation will be introduced in the next paper. In this paper, we only introduce the structure of the multisector model and the coefficients of the equations. The multisector model is a dynamic model-that is, it solves year by year into the future using its own solutions for earlier years. The development of a dynamic, year-by-year solution allows us to combine the change in structure with a consideration of the dynamic adjustment required. These dynamics have obvious advantages in the use of the multisector model for industrial planning. The multisector model is a medium-term and long-term model. Whereas a short-term model can taken the labor supply and capital stock as given, a long-term model must acknowledge that these are determined endogenously. Changes in the medium-term can be analyzed in the context of long-term structural changes. The structure of this model can be summarized as follow. The difference in domestic and world prices affects industrial structure and the pattern of international trade; domestic output and factor price affect factor demand; factor demand and factor price affect industrial income; industrial income and relative price affect industrial consumption. Technical progress, as measured in terms of total factor productivity and relative price affect input-output coefficients; input-output coefficients and relative price determine the industrial input cost; input cost and import price determine domestic price. The differences in productivity and wage growth among different industries affect the relative price.

• The Journal of Bigdata
• /
• v.4 no.1
• /
• pp.63-72
• /
• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.