• Journal of Navigation and Port Research
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• v.47 no.3
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• pp.167-173
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• 2023

The manufacturing industry is the backbone of the Korean economy. Among them, the petrochemical industry is a strategic growth industry, which makes a profit through reexports based on eminent technology in South Korea which imports all of its crude oil. South Korea imports whole amount of crude oil, which is the raw material for many manufacturing industries, by sea transportation. Therefore, it must respond swiftly to a highly volatile tanker freight market. This study aimed to make an early warning model of crude oil shipping market using a signal approach. The crisis of crude oil shipping market is defined by BDTI. The overall leading index is made of 38 factors from macro economy, financial data, and shipping market data. Only leading correlation factors were chosen to be used for the overall leading index. The overall leading index had the highest correlation coefficient factor of 0.499 two months ago. It showed a significant correlation coefficient five months ago. The QPS value was 0.13, which was found to have high accuracy for crisis prediction. Furthermore, unlike other previous time series forecasting model studies, this study quantitatively approached the time lag between economic crisis and the crisis of the tanker ship market, providing workers and policy makers in the shipping industry with an framework for strategies that could effectively deal with the crisis.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1137-1147
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• 2022

River and groundwater stages are the main elements in the hydrologic cycle. They are spatially correlated and can be used to evaluate hydrological and agricultural drought. Stochastic simulation is often performed independently on hydrological variables that are spatiotemporally correlated. In this setting, interdependency across mutual variables may not be maintained. This study proposes the Bayesian vector autoregression model (VAR) to capture the interdependency between multiple variables over time. VAR models systematically consider the lagged stages of each variable and the lagged values of the other variables. Further, an autoregressive model (AR) was built and compared with the VAR model. It was confirmed that the VAR model was more effective in reproducing observed interdependency (or cross-correlation) between river and ground stages, while the AR generally underestimated that of the observed.

• Atmosphere
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• v.32 no.4
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• pp.367-379
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• 2022

In this paper, the evaluation of the performance of Korea Meteorological Administratio (KMA) Global Seasonal forecasting system version 6 (GloSea6) is presented by assessing the effects of larger ensemble size and carrying out the test using different initial conditions for hindcast in sub-seasonal to seasonal scales. The number of ensemble members increases from 3 to 7. The Ratio of Predictable Components (RPC) approaches the appropriate signal magnitude with increase of ensemble size. The improvement of annual variability is shown for all basic variables mainly in mid-high latitude. Over the East Asia region, there are enhancements especially in 500 hPa geopotential height and 850 hPa wind fields. It reveals possibility to improve the performance of East Asian monsoon. Also, the reliability tends to become better as the ensemble size increases in summer than winter. To assess the effects of using different initial conditions, the area-mean values of normalized bias and correlation coefficients are compared for each basic variable for hindcast according to the four initial dates. The results have better performance when the initial date closest to the forecasting time is used in summer. On the seasonal scale, it is better to use four initial dates, where the maximum size of the ensemble increases to 672, mainly in winter. As the use of larger ensemble size, therefore, it is most efficient to use two initial dates for 60-days prediction and four initial dates for 6-months prediction, similar to the current Time-Lagged ensemble method.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.3
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• pp.253-264
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• 2010

This study investigates the relationship between daily mortality and heat stress in Seoul, using perceived temperatures (PT) derived from a heat budget model. During the summer season, observed PT intensity showed the biggest magnitude of summer heat stress from the middle 10 days of July to the first 10 days of August. The elderly (65 and above) were found to be the most vulnerable to heat stress. The threshold PT, with a significant increase in excess mortality, was $38^{\circ}C$. No time lagged effect was observed with summer heat stress, while a high correlation was observed between anomalies in PT and relative deviation of mortality. A comparison of the heat index and the discomfort index with excess mortality revealed that the discomfort index underestimated excess mortality, whereas the heat index could not appropriately explain the increase in excess mortality correlated with the increase in excess heat. In contrast, PT was found to be the weather element that best represents excess mortality due to heat stress, and is thus expected to serve as a more reliable forecast index of human biometeorology.

• Current Optics and Photonics
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• v.3 no.6
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• pp.555-565
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• 2019

Development of a biomarker for predicting tumor-treatment efficacy is a matter of great concern, to reduce time, medical expense, and effort in oncology therapy. In a preclinical study, we hypothesized that the blood-flow parameter based on laser speckle flowmetry (LSF) could be a potential indicator to estimate the efficacy of breast-cancer treatment. To verify this hypothesis, a 13762-MAT-B-III rat breast tumor was grown in a dorsal skinfold window chamber applied to a nude mouse, and the change in blood flow rate (BFR) - or the speckle flow index (SFI) is used together as the same meaning in this manuscript - was longitudinally monitored during tumor growth and metronomic cyclophosphamide treatment. Based on the daily LSF angiogram, several BFR parameters (baseline SFI, normalized SFI, and △rBFR) were compared to tumor size in the normal, treated, and untreated tumor groups. Despite the incomplete tumor treatment, we found that the daily changes in all BFR parameters tended to have partially positive correlation with tumor size. Moreover, we observed that the changes in baseline SFI and normalized SFI responded one day earlier than the tumor shrinkage during chemotherapy. However, daily variations in the hypercapnia-induced △rBFR lagged tumor shrinkage by one day. This study would contribute not only to evaluating tumor vascular response to treatment, but also to monitoring blood-flow-mediated diseases (in brain, skin, and retina) by using LSF in preclinical settings.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.5 no.2
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• pp.86-94
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• 2000

In order to extract the spatio-temporal characteristics of long-term variability of the net heat flux on the sea surface in the East Asian marginal seas, empirical orthogonal function (EOF) analysis was conducted using data set calculated every 12 hours interval during 1978-1995. Among the first three modes explaining 73% of the total variance, the first mode having high peak at 1 year period indicates high variability area around the Sandong Peninsula and central and northeastern part of the East Sea. In the second mode which has spatial distribution of dipole type at the north and south, the peaks appear at 3.6 year and 2.3 year cycles. Time coefficient of the second EOF is believed to have close relation with the E1 Nino and has out-of-phase variation with NINO3 SST. Lagged correlation between NINO3 SST and time coefficient of the second EOF indicates four month time delay in the NINO3 SST. In the third mode which has opposite sign at the east and west, the periodicity of 6-9 year cycle has relatively clear appearance compared to other two EOFs. Also, high heat loss exceeding 800 W/$m^{2}$ in winter time occured at the south part of the Sandong Peninsula and Vladivostok. It reveals more frequent occurrence of about two times at the Sandong Peninsula than Vladivostok. The event is concentrated in January at Vladivostok, but it occurs primarily in December and January at the Sandong Peninsula.

• Journal of Korea Technology Innovation Society
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• v.19 no.3
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• pp.465-482
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• 2016

Previous researches have introduced various ways to analyze the impact of standardization on innovation while the works are not only small in number but based on interview or case study. This paper addresses the impact of standardization activities within South Korean industries on technological innovation applying an empirical analysis of standardization activities and technological innovation. Drawing on Korean Industrial Standards Classification from panel data of 2003 to 2012, we employed corresponding data of each industrial classification: Number of standards, Accumulated number of standards, Number of patents applied in Korea, Sales, Operational profit, Intangible asset, and R&D invest. In the first model, we run panel data models employing the number of patents applied in Korea as an independent variable, and the number of standards, accumulated number of standards, sales, and operational profit as dependent variables to observe industrial impacts upon the relationship between standards and patents, along with time lagged consideration. The result shows that number of standards are revealed to have a negative influence on patent applications in the year of research, and no significant effect appears for the next two years while positive effect shows up on the third year. Meanwhie, accumulated number of standards turned out to have positive effects on patent applications in Korea. This implies it takes time for innovation subjects to embrace newly established standards while having a significant amount of positive effect on technological innovation in the long term. In the second model, we use canonical correlation analysis to find industrial-wide characteristics. The result of this model is equivalent to the result of panel data analysis except in a few industries, where some industry specific characteristics appear. The implications of our results present that Korean policy makers have to take account of industrial effects on standardization to promote technological innovation.

• Journal of Korea Water Resources Association
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• v.49 no.7
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• pp.589-597
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• 2016

In this study, we investigated the use of satellite-derived flow (SDF) signals and a data-based model for the estimation of outflow for the river reach where in situ measurements are either completely unavailable or are difficult to access for hydraulic and hydrology analysis such as the upper basin of Imjin River. It has been demonstrated by many studies that the SDF signals can be used as the river width estimates and the correlation between SDF signals and river width is related to the shape of cross sections. To extract the nonlinear relationship between SDF signals and river outflow, Artificial Neural Network (ANN) model with SDF signals as its inputs were applied for the computation of flow discharge at Imjin Bridge located in Imjin River. 15 pixels were considered to extract SDF signals and Partial Mutual Information (PMI) algorithm was applied to identify the most relevant input variables among 150 candidate SDF signals (including 0~10 day lagged observations). The estimated discharges by ANN model were compared with the measured ones at Imjin Bridge gauging station and correlation coefficients of the training and validation were 0.86 and 0.72, respectively. It was found that if the 1 day previous discharge at Imjin bridge is considered as an input variable for ANN model, the correlation coefficients were improved to 0.90 and 0.83, respectively. Based on the results in this study, SDF signals along with some local measured data can play an useful role in river flow estimation and especially in flood forecasting for data-scarce regions as it can simulate the peak discharge and peak time of flood events with satisfactory accuracy.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.