• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.317-326
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• 1998

Water consumption characteristics on the northern part of Seoul were analyzed using wavelet transform with a base function of Coiflets 5. It turns out that long term evolution mode detected at 212 scale in 1995 was in a shape of hyperbolic tangent over the entire period due to the development of Sanggae resident site. Furthermore, there was seasonal water demand having something to do with economic cycle which reached its peak at the ends of June and December. The amount of this additional consumption was about $1,700\;\textrm{cm}^3/hr$ on June and $500\;\textrm{cm}^3/hr$ on December. It was also shown that the periods of energy containing sinusoidal component were 3.13 day, 33.33 hr, 23.98 hr and 12 hr, respectively, and the amplitude of 23.98 hr component was the most humongous. The components of relatively short frequency detected at $2^i$[i = 1,2,…12] scale were following Gaussian PDF. The most reliable predictive models are multiple AR[32,16,23] and ARMA[20, 16, 10, 23] which the input of temperature from the view point of minimized predictive error, mutual independence or residuals and the availableness of reliable meteorological data. The predicted values of water supply were quite consistent with the measured data which cast a possibility of the deployment of the predictive model developed in this study for the optimal management of water supply facilities.

• Journal of Food Hygiene and Safety
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• v.27 no.4
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• pp.339-348
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• 2012

The purpose of this study is to predict the economic size of foods-for-elderly market, which will be valuable information for establishing related policy and backup system. After setting the scope of related industry, detailed information for current market situation was investigated and a systematic forecast for market changes in the future was performed. Economic growth, changes in consumer expenditure and economic status of the elderly, current subscription of medical insurance and saving for pension were reflected. In addition, a survey toward related firms was completed and changes in aged population and incidence of chronic disease in the elderly were taken into account. Results show that the annual growth rate of the market was predicted to be the minimum 4.54% through the maximum 8.32% from 2010 to 2025 and its market size was forecasted to be the minimum 7,073 ten million won through the maximum 10,976 ten million won. It is expected that the market of foods-for-elderly will grow rapidly with development of foods technology and fast increase of aged population. Especially, growth of health functional foods and foods for special dietary uses for elderly will be distinguished. However, it seems that related firms are on the hedge, watching current trend of the related industry. This may results in insufficient supply against the demand. Therefore, policy for foods-for-elderly should be introduced and systematically administered, including R&D support, standardization and authentication for foods-for-elderly, construction of related database system.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Korean Journal of Construction Engineering and Management
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• v.22 no.2
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• pp.3-11
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• 2021

Myanmar is an undeveloped country with high development value among Asian countries. Therefore, various countries including the U.S. are considering entering the market. In this respect, demand for real estate development project is forecast to grow on increased inflow of foreigners and Myanmar's economic growth. However, Myanmar is a high-risk country in terms of overseas companies, including national risk. In this study, we conducted an in-depth interview with experts (law, finance, technology, and local experts) after analyzing data on Myanmar to extract risk-causing factors. Through this, 106 risk factors were extracted, and the final risk classification system was established by conducting three-time groupings using the affinity diagramming. And the relative importance of each factor was presented using the analytic hierarchy process (AHP) technique. As a result, the country-related risk, the fund-related risk, and the pre-sale-related risk were highly important. The research results are expected to provide risk management standards to companies entering the Myanmar real estate development type project.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.1-14
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• 2021

Onions are a representative produce that requires supply-demand control measures due to large fluctuations in production and price by growing season. Accurate forecasts of crop production can improve the effectiveness of such measures. However, it is challenging to obtain accurate estimates of crop productivity for onions because they are mainly grown on the open fields. The objective of this study was to perform the empirical analysis of the relationship between factors for crop growth and meteorological conditions, which can support the development of models to predict crop growth and production. The growth survey data were collected from open fields. The survey data included the weight of above ground organs as well as that of the bulbs. The estimates of meteorological data were also compiled for the given fields. Correlation analysis between these factors was performed. The random forest was also used to compare the importance of the meteorological factors by the growth stage. Our results indicated that insolation in early March had a positive effect on the growth of the above-ground. There was a negative correlation between precipitation and the growth of the above-ground at the end of March although it has been suggested that drought can deter the growth of onion. The negative effects of precipitation and daylight hours on the growth of the above-ground and under-ground were significant during the harvest period. These meteorological factors identified by growth stage can be used to develop models for onion growth and production forecast.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.148-163
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• 2021

The diversification of the New Deal urban regeneration projects, that started in 2017 in accordance with the "Special Act on Urban Regeneration Activation and Support", generated the increased demand for the accuracy of data-driven diagnosis and project type forecast. Thus, this research was conducted to develop an application model able to identify the most appropriate New Deal project type for "eup", "myeon" and "dong" across the country. Data for application model development were collected through Statistical geographic information service(SGIS) and the 'Urban Regeneration Comprehensive Information Open System' of the Urban Regeneration Information System, and data for the analysis model was constructed through data pre-processing. Four models were derived and simulations were performed through polynomial regression analysis and multinomial logistic regression analysis for the application of the appropriate New Deal project type. I verified the applicability and validity of the four models by the comparative analysis of spatial distribution of the previously selected New Deal projects by targeting the sites located in Seoul by each model and the result showed that the DI-54 model had the highest concordance rate.

• Korea Trade Review
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• v.48 no.3
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• pp.69-88
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• 2023

This study analyzes the dynamic characteristics of cargo volume (demand), ship fleet (supply), and freight rate (price) of container, dry bulk, and tanker shipping markets by using the VAR and VECM models. This analysis is expected to enhance the statistical understanding of market dynamics, which is perceived by the actual experiences of market participants. The common statistical patterns, which are all shown in the three shipping markets, are as follows: 1) The Granger-causality test reveals that the past increase of fleet variable induces the present decrease of freight rate variable. 2) The impulse-response analysis shows that cargo shock increases the freight rate but fleet shock decreases the freight rate. 3) Among the three cargo, fleet, and freight rate shocks, the freight rate shock is overwhelmingly largest. 4) The comparison of adjR² reveals that the fleet variable is most explained by the endogenous variables, i.e., cargo, fleet, and freight rate in each of shipping markets. 5) The estimation of co-integrating vectors shows that the increase of cargo increases the freight rate but the increase of fleet decreases the freight rate. 6) The estimation of adjustment speed demonstrates that the past-period positive deviation from the long-run equilibrium freight rate induces the decrease of present freight rate.

• Journal of Korea Port Economic Association
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• v.40 no.2
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• pp.39-52
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• 2024

As the 'Eco-friendly conversion project for Port's CHE(Cargo handling Equipment) ' which has started in 2014 ends in 2024, in addition to the existing 'Low pollution' paradigm to respond to fine dust problems, a full-fledged 'Zero-emission' conversion is to be required to implement 2050 carbon neutrality at the port level. Accordingly, this study calculated the future replacement demand for container handling equipments at the four major domestic ports(Busan, Incheon, Yeosu Gwangyang, and Ulsan), and assumed a scenario where every CHE supposed to eb replaced is electrified inturn every year. And then the resulting future emission reduction effect accordingly was calculated and analyzed. In particular, compared and analyzed the emission outlook applying the life-cycle concept(LCA), which is being adopted as a new emission calculation standard in most industrial fields, and the existing emission calculation concept that only considers direct emissions within the port, to provide more effective implications for the promotion of follow-up conversion projects. According to the analysis results, if the CHE is replaced according to the proposed schedule, it is expected that the existing emissions can be reduced by 79% compared to BAU in 2025 and 97.4% in 2030. However, if the LCA is applied, it is expected to be reduced by only 27.6% by 2030. This suggests that port's CHE must be converted to zero emissions and at the same time establish an Ports' self-sufficient energy grid based on renewable energy.