• Structural Engineering and Mechanics
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• v.53 no.6
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• pp.1183-1200
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• 2015

Reinforcement corrosion can cause serious safety deterioration to aging concrete structures exposed in aggressive environments. This paper presents an approach for reliability analyses of deteriorating reinforced concrete structures affected by reinforcement corrosion on the basis of the representative symptoms identified during the deterioration process. The concrete cracking growth and rebar bond strength evolution due to reinforcement corrosion are chosen as key symptoms for the performance deterioration of concrete structures. The crack width at concrete cover surface largely depends on the corrosion penetration of rebar due to the expansive rust layer at the bond interface generated by reinforcement corrosion. The bond strength of rebar in the concrete correlates well with concrete crack width and decays steadily with crack width growth. The estimates of cracking development and bond strength deterioration are examined by experimental data available from various sources, and then matched with symptom-based lifetime Weibull model. The symptom reliability and remaining useful life are predicted from the predictive lifetime Weibull model for deteriorating concrete structures. Finally, a numerical example is provided to demonstrate the applicability of the proposed approach for forecasting the performance of concrete structures subject to reinforcement corrosion. The results show that the corrosion rate has significant impact on the reliability associated with serviceability and load bearing capacity of reinforced concrete structures during their service life.

• Korea Trade Review
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• v.47 no.3
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• pp.211-232
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• 2022

Due to the impact of the public health event COVID-19 epidemic, the Chinese futures market showed "Black Swan". This has brought the unpredictable into the economic environment with many commodities falling by the daily limit, while gold performed well and closed in the sunshine(Yan-Li and Rui Qian-Wang, 2020). Volatility is integral part of financial market. As an emerging market and a special precious metal, it is important to forecast return of gold futures price. This study selected data of the SHFE gold futures returns and conducted an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. Comparing the statistics of AIC, SC and H-QC, ARMA (12,9) model was selected as the best model. But serial correlation in the squared returns suggests conditional heteroskedasticity. Next part we established the autoregressive moving average ARMA-GARCH-type model to analysis whether Volatility Clustering and the leverage effect exist in the Chinese gold futures market. we consider three different distributions of innovation to explain fat-tailed features of financial returns. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE), Theil inequality coefficient(TIC) and root mean-squared error (RMSE). The results show that the ARMA(12,9)-TGARCH(2,2) model under Student's t-distribution outperforms other models when predicting the Chinese gold futures return series.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.1
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• pp.98-103
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• 2009

The forecasting, mitigation and preparedness of the natural disaster impacts require relevant information regarding the disaster desirable in real time. In the meantime it is requiring the rapid and continuous data and information generation or gathering for possible prediction and monitoring of the natural disaster. Since disasters that cause huge social and economic disruptions normally affect large areas or territories and are linked to global change. The use of traditional and conventional methods for management of the natural disaster impact can not be effectively implemented for intial data col1ection with the further processing. The space technology or remote sensing tools offer excellent possibilities of collecting vital data. The main reason is capability of this technology of collecting data at global and regional scales rapidly and repetitively. This is unchallenged advantage of the space methods and technology. The satellite or remote sensing techniques can be used to monitor the current situation, the situation before based on the data in sight. as well as after disaster occurred. They can be used to provide baseline data against which future changes can be compared while the GIS techniques provide a suitable framework for integrating and analyzing the many types of data sources required for disaster monitoring. Developed GIS is an excellent instrument for definition of the social impact status of the natural disaster which can be undertaken in the future database developments. This methodology is a good source for analysis and dynamic change studies of the natural disaster impacts.

• Atmosphere
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• v.23 no.3
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• pp.321-329
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• 2013

This study examines the effect of aerosols on the vertical invigoration of continental stratiform clouds, using a dataset of Atmospheric Radiation Measurement (ARM) Intensive Operational Period (IOP, March 2000) at the Southern Great Plains (SGP) site. To provide further support to our observation-based findings, the weather research and forecasting (WRF) sensitivity simulations with changing cloud condensation nuclei (CCN) concentrations have been carried out for the golden episode over SGP. First, cross correlation between observed aerosol scattering coefficient and cloud liquid water path (LWP) with a 160-minutes lag is the highest of r = 0.83 for the selected episode, which may be attributable to cloud vertical invigoration induced by an increase in aerosol loading. Modeled cloud fractions in a control run are well matched with the observation in the perspective of cloud morphology and lasting period. It is also found through a simple sensitivity with a change in CCN that aerosol invigoration (AIV) effect on stratiform cloud organization is attributable to a change in the cloud microphysics as well as dynamics such as the corresponding modification of cloud number concentrations, drop size, and latent heating rate, etc. This study suggests a possible cloud vertical invigoration even in the continental stratiform clouds due to aerosol enhancement in spite of a limited analysis based on a few observed continental cloud cases.

• Journal of Environmental Impact Assessment
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• v.15 no.4
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• pp.249-258
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• 2006

Impacts of global warming have been identified in many areas including natural ecosystem. A good number of studies based on climate models forecasting future climate have been conducted in many countries worldwide. Due to its global coverage, GCM, which is a most frequently used climate model, has limits to apply to Korea with such a narrower and complicated terrain. Therefore, it is necessary to perform a study impact assessment of climate changes with a climate model fully reflecting characteristics of Korean climate. In this respect, this study was designed to compare and analyze the GCM and RCM in order to determine a suitable climate model for Korea. In this study, spatial scope was Korea for 10 years from 1981 to 1990. As a research method, current climate was estimated on the basis of the data obtained from observation at the GHCN. Future climate was forecast using 4 GCMs furnished by the IPCC among SRES A2 Scenario as well as the RCM received from the NIES of Japan. Pearson correlation analysis was conducted for the purpose of comparing data obtained from observation with GCM and RCM. As a result of this study, average annual temperature of Korea between 1981 and 1990 was found to be around $12.03^{\circ}C$, with average daily rainfall being 2.72mm. Under the GCM, average annual temperature was between 10.22 and $16.86^{\circ}C$, with average daily rainfall between 2.13 and 3.35mm. Average annual temperature in the RCM was identified $12.56^{\circ}C$, with average daily rainfall of 5.01mm. In the comparison of the data obtained from observation with GCM and RCM, RCMs of both temperature and rainfall were found to well reflect characteristics of Korea's climate. This study is important mainly in that as a preliminary study to examine impact of climate changes such as global warming it chose appropriate climate model for our country. These results of the study showed that future climate produced under similar conditions with actual ones may be applied for various areas in many ways.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• Proceedings of the Technology Innovation Conference
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• 1997.12a
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• pp.177-196
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• 1997

The engineering industry plays an important role for national competitive, since it has an high impact on other industries. With its importance, the engineering industry development largely depends on its technical manpower ather than capital factor. This study aims at estimating the additional requirement on technical manpower based on the forecasted market size which represents the structure change corresponding to economic growth in related industry. Research scope includes the twelve of fifteen field except three with insufficient historical data and technical manpower above bachelor degree. Specialty, we forecast market size with determinants resulted from historical data analysis on each field. The demand on technical manpower is derived from the forecasted market. We also estimate an additional requirement with the supply analysis. The research results show different patterns over time period. The relative ratio on chemical and construction to total market will steadily grow over short term, while applied, environment, electronic and information will rapidly grow This pattern will be stabilized over mid or long term. The additional requirement on technical manpower represents the similar pattern to market growth. The research result implies manpower policy for having high inflow of technical engineer from educational institute and the related industries through the image improvement.

• Asian Journal of Innovation and Policy
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• v.9 no.2
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• pp.133-144
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• 2020

Korean economy has experienced a very rapid growth largely due to the change of the innovation system since the last half century. The recent outbreak of COVID-19 impacts the global economy as well as Korea's innovation system. In order to understand the influence of the shock to the Korean technological system, we have forecast the future of the system combining qualitative and quantitative techniques such as expert panel, cross impact analysis, and scenario planning. According to the results, we have identified 39 driving forces influencing the change of Korea's technological innovation system. Four scenarios have been suggested based on the predetermined factors and core uncertainties. In other words, uncertainties of emergence of the regions and global value chains generate four scenarios: regional growth, unstable hope, returning to the past, and regional conflicts. The 'regional growth' scenario is regarded as the most preferable, whereas the 'regional conflicts' scenario is unavoidable. In conclusion, we put forward some policy implications to boost the regional innovation system by exploiting the weakened global value chains in order to move on to the most preferable scenario away from the return to the past regime.