• International Area Studies Review
• /
• v.15 no.3
• /
• pp.349-368
• /
• 2011

The emergence of new multipolar world economy along with the predominant growth of emerging economies encourages these emerging countries to internationalize their currencies. Currently the discrepancy between qualification and status of international currency is easily observed, and the emerging market currencies are no doubt underestimated considering their share of the world's economic size and trade volume. This paper studies the determinant factors of currency internationalization for five key currencies (US Dollar, Yen, Euro, Pound, and Swiss Franc). The analysis shows economic size, trade volume, and the stability of price and exchange rate are most important. Based on this result, Chinese Yuan is forecast to become a new international currency in the near future. Therefore, Korea needs to preempt the issue of regional economic integration, and even currency integration, by taking into account the possibility of internationalized Yuan.

• The Journal of the Korea Contents Association
• /
• v.19 no.1
• /
• pp.132-140
• /
• 2019

Recently, many cities around the world introduced and operated shared bicycle system to reduce the traffic and air pollution. Seoul also provides shared bicycle service called as "Ddareungi" since 2015. As the use of shared bicycle increases, the demand for bicycle in each station is also increasing. In addition to the restriction on budget, however, there are managerial issues due to the different demands of each station. Currently, while bicycle rebalancing is used to resolve the huge imbalance of demands among many stations, forecasting uncertain demand at the future is more important problem in practice. In this paper, we develop forecasting model for demand for Seoul shared bicycle using statistical time series analysis and apply our model to the real data. In particular, we apply Holt-Winters method which was used to forecast electricity demand, and perform sensitivity analysis on the parameters that affect on real demand forecasting.

• Atmosphere
• /
• v.29 no.1
• /
• pp.1-12
• /
• 2019

For the allergy patient who needs to know the situation about the extent of pollen risk, the National Institute of Meteorological Sciences developed a pollen forecasting system based on the Community Multiscale Air Quality Modeling (CMAQ). In the old system, pollen emission from the oak was estimated just based on the airborne concentration and meteorology factors, resulted in high uncertainty. For improving the quality of current pollen forecasting system, therefore the estimation of pollen emission is now corrected based on the observation of pollen emission at the oak forest to better reflect the real emission pattern. In this study, the performance of the previous (NIMS2014) and current (NIMS2016) model system was compared using observed oak pollen concentration. Daily pollen concentrations and emissions were simulated in pollen season 2016 and accuracy of onset and end of pollen season were evaluated. In the NIMS2014 model, pollen season was longer than actual pollen season; The simulated pollen season started 6 days earlier and finished 13.25 days later than the actual pollen season. The NIMS2016 model, however, the simulated pollen season started only 1.83 days later, and finished 0.25 days later than the actual pollen season, showing the improvement to predict the temporal range of pollen events. Also, the NIMS2016 model shows better performance for the prediction of pollen concentration, while there is a still large uncertainty to capture the maximum pollen concentration at the target site. Continuous efforts to correct these problems will be required in the future.

• Journal of IKEEE
• /
• v.23 no.1
• /
• pp.127-133
• /
• 2019

Electric power demand forecasting is one of the important research areas for future smart grid introduction. However, It is difficult to predict because it is affected by many external factors. Traditional methods of forecasting power demand have been limited in making accurate prediction because they use raw power data. In this paper, a probability-based CRBM is proposed to solve the problem of electric power demand prediction using raw power data. The stochastic model is suitable to capture the probabilistic characteristics of electric power data. In order to compare the mid-term power demand forecasting performance of the proposed model, we compared the performance with Recurrent Neural Network(RNN). Performance comparison using electric power data provided by the University of Massachusetts showed that the proposed algorithm results in better performance in mid-term energy demand forecasting.

• Proceedings of Korean Society of Archives and Records Management
• /
• 2019.05a
• /
• pp.17-23
• /
• 2019

People who usually receive weather information are now becoming agents providing such information through crowdsourcing based on the Internet. As the archival academia recognizes the significance of information management including data, it is necessary to focus on the change and the current state of the meteorological information. Therefore, this dissertation has confirmed the current state and the problem of the meteorological network built by the information provided by the agent. In addition, it has analyzed the collection, use, and possibility of meteorological information by participating in the forecast process through crowdsourcing to identify how to gather information in the field of meteorology. Furthermore, it suggests a future development prospect of meteorological application through crowdsourcing.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.3
• /
• pp.265-272
• /
• 2019

In this study, we conducted a prediction study to qualitatively identify the continuous growth rate that causes problems every year for deposit bank mortgage loans, identify the characteristic factors that could once again stabilize, and come up with measures for future quantitative analysis of mortgage loans and growth trends. Based on data analysis using the R program, which is widely used for big data analysis, the parameters of ARIMA model (0.1,1)(0.1,1)[12] were found to be most suitable. In these indicators, estimates over the next five years (60 months) increased 4.5% on average. However, this has limitations that do not reflect socio-environmental factors, which require further study of these limitations.

• Journal of the Korea Convergence Society
• /
• v.12 no.4
• /
• pp.249-258
• /
• 2021

Bitcoin prices have been soaring recently as investors flock to cryptocurrency exchanges. The purpose of this study is to predict the Bitcoin price using a deep learning model and analyze whether Bitcoin is profitable through investment strategy. LSTM is utilized as Bitcoin prediction model with nonlinearity and long-term memory and the profitability of MA cross-over strategy with predicted prices as input variables is analyzed. Investment performance of Bitcoin strategy using LSTM forecast prices from 2013 to 2021 showed return improvement of 5.5% and 46% more than market price MA cross-over strategy and benchmark Buy & Hold strategy, respectively. The results of this study, which expanded to recent data, supported the inefficiency of the cryptocurrency market, as did previous studies, and showed the feasibility of using the deep learning model for Bitcoin investors. In future research, it is necessary to develop optimal prediction models and improve the profitability of Bitcoin investment strategies through performance comparison of various deep learning models.

• Computers and Concrete
• /
• v.27 no.2
• /
• pp.111-130
• /
• 2021

In the present work, an extensive study for predicting efficiency parameters (��i) of various simulated nanocomposites including Polymethyl methacrylate (PMMA) as matrix and different structures including various sizes of graphene platelets (GPLs), single, double, and multi-walled carbon nanotubes (SWCNTs-DWCNTs-MWCNTs), and single and double-walled boron nitride nanotubes (SWBNNTs-DWBNNTs) are investigated. It should be stated that GPLs, carbon and boron nitride nanotubes (CNTs, BNNT) with different chiralities (5, 0), (5, 5), (10, 0), and (10, 10) as reinforcements are considered. In this research, molecular dynamics (MDs) method with Materials studio software is applied to examine the mechanical properties (Young's modulus) of simulated nanocomposite boxes and calculate η1 of each nanocomposite boxes. Then, it is noteworthy that by changing length (6.252, 10.584, and 21.173 nm) and width (7.137, 10.515, and 19.936) of GPLs, ��1, ��2, and ��3 approximately becomes (0.101, 0.114, and 0.124), (1.15, 1.22, and 1.26), (1.04, 1.05, and 1.07) respectively. After that efficiency parameters of SWCNTs, DWCNTs, and MWCNTs are calculated and discussed separately. Finally efficiency parameters of SWBNNTs and DWBNNTs with different chiralities by PMMA as matrix are determined by MD and discussed separately. It is known that the accurate efficiency parameters helps a lot to calculate the properties of nanocomposite analytically. In particular, the obtained results from this research can be used for analytical work based on the extended rule of mixture (ERM) in bending, buckling and vibration analysis of structure in future study.

• Journal of Navigation and Port Research
• /
• v.44 no.6
• /
• pp.517-523
• /
• 2020

Pyeongtaek·Dangjin port handles the largest volume of finished vehicles in Korea, including more than 95% of imported cars. However, since the volume of imported cars has been stagnant since 2015, officials planning to invest in port development or automobile-related industries must make new forecasts. Economic variables such as the GDP often have been used in predicting automobile volume, but prior research showed that the impact of these economic variables on automobile volume I has been gradually decreasing in developed countries. These variables remain important predictors, however, in developing countries that experience rapid economic growth. In this study, predicting the volume of imported passenger cars at Pyeongtaek·Dangjin port, the decreasing Korean population was a major factor we considered. Our forecast showed that the volume of imported passenger cars at Pyeongtaek·Dangjin port will gradually decrease -by 2021. The Mean Absolute Percentage Error (MAPE) verification was performed to measure the accuracy of the predicted results, and the scenario analysis was performed on the share of imported passenger cars.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.12
• /
• pp.261-270
• /
• 2020

In this study, we identify promising technologies for Piggyback transportation system by analyzing the relevant patent information. In order for this, we first develop the patent database by extracting relevant technology keywords from the pioneering research papers for the Piggyback flactcar system. We then employed textmining to identify the frequently referred words from the patent database, and using these words, we applied the LDA (Latent Dirichlet Allocation) algorithm in order to identify "topics" that are corresponding to "key" technologies for the Piggyback system. Finally, we employ the ARIMA model to forecast the trends of these "key" technologies for technology forecasting, and identify the promising technologies for the Piggyback system. with keyword search method the patent analysis. The results show that data-driven integrated management system, operation planning system and special cargo (especially fluid and gas) handling/storage technologies are identified to be the "key" promising technolgies for the future of the Piggyback system, and data reception/analysis techniques must be developed in order to improve the system performance. The proposed procedure and analysis method provides useful insights to develop the R&D strategy and the technology roadmap for the Piggyback system.