• Proceedings of the KIEE Conference
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• 2004.11d
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• pp.93-96
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• 2004

In this paper, we analyzed the current limiting characteristics according to increase of source voltage in the flux-lock type high-Tc superconducting fault current limiter (SFCL). The flux-lock type SFCL consisted of two coils, which were wound in parallel each other through an iron core, and high-Tc superconducting (HTSC) element connected with coil 2 in series. The flux-lock type SFCL has the characteristics better in comparison with the resistive type SFCL because the fault current in the flux-lock type SFCL can be divided into two coils by the inductance ratio of coil 1 and coil 2. The fault current limiting operation of the flux-lock type SFCL can be different due to winding direction of the two coils. The winding method where the decrease of linkage flux between two coils in the accident happens is called the subtractive polarity winding and the winding method in case of the increase of linkage flux is called the additive polarity winding. The fault current limiting experiments according to the source voltage were performed for these two winding methods. Through the comparison and the analysis of the experimental data, we confirmed that the quench time was shorter, irrespective of the winding direction as the source voltage increased and that the fault current and the HTSC's resistance increased as the amplitude of the source voltage increased. The additive polarity winding made the fast quench time and the lower resistance of HTSC element in comparison with the subtractive polarity winding. The fault current of the subtractive polarity winding was larger than that of the additive polarity winding. In conclusion, we found that the additive polarity winding reduced the burden of SFCL because the quench time was shorter and the fault current was smaller than those of the subtractive polarity winding.

• Journal of Korea Port Economic Association
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• v.32 no.4
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• pp.165-182
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• 2016

This study examines the competitiveness and cooperativeness among the container ports in East Asia by analyzing their monthly dynamics in eight years (2008-2015). Time series data on container throughput divided into origin and destination (O/D), such as the top six Chinese ports and the transshipment (T/S) ports such as Hong Kong, Busan, and Singapore, are computed with two methods based on the Vector Error Correction Model (VECM). The first Granger causality test results show that Busan T/S has significant bilateral relations with three Chinese O/D ports; and significant unidirectional relations with three other O/D ports. Shenzhen port has significant bilateral relations with Singapore, and has a significant unidirectional relation with Hong Kong port. Co-integrating test results showed that Busan holds negative co-integration with all Chinese O/D ports. Impulse response function (IRF) results show an opposite direction between paired ports. The ratios of the impulse from T/S ports are significantly high to one another in the short-run, but its power declines as time passes. The ratio of the impulse from the Chinese ports to T/S ports is less significant in the short-run period, however, it becomes more significant as time passes. The significance of most shocks was high in the second period, but was diluted after the sixth period.

• Economic and Environmental Geology
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• v.29 no.6
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• pp.699-712
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• 1996

Field scale experiments using an automated 144-channel TDR system were conducted which monitored the movement of solute through unsaturated loamy soils. The experiments were carried out on two different field plots of 0.54 ha to study the vertical movement of solute plume created by applying a square pulse of $CaCl_2$ as a tracer. The residence concentration was monitored at 24 locations on a transect and 5 depths per location by horizontally-positioning 50 cm long triple wire TDR probes to study the heterogeneity of solute travel times and the governing transport concept at field scale. This paper describes details of experimental methodology and calibration aspects of the TDR system. Three different calibration methods for estimation of solute concentration from TDR-measured bulk soil electrical conductivity were used for each field site. Data analysis of mean breakthrough curves (BTCs) and parameters estimated using the convection-dispersion model (CDE) and the convective-lognormal transfer function model (CLT) reveals that the automated TDR system is a viable technique to study the field scale solute transport providing a normal distribution of resident concentration in a high resolution of time series, and that calibration method does not significantly affect both the shape of BTC and the parameters related to the peak travel time. Among the calibration methods, the simple linear model (SLM), a modified version of Rhoades' model, appears to be promising in the calibration of horizontally-positioned TDR probes at field condition.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.55-73
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• 2024

Unicorn companies are attracting attention around the world as they are recognized for their high corporate value in a short period of time as an innovative business models. Their growth process presents good lessons for the startup ecosystem and have a positive impact on national economic development and job creation. However, previous studies related to unicorn companies are focused on 'event studies' and 'case studies' such as characteristics of founders, environmental factors, business models and success/failure cases of companies already recognized as unicorns rather than a multifaceted approach. The occurrence of unicorn companies and Macroscopic analysis of related factors is lacking. Against this background, this study are considering the characteristics of unicorns examined through previous research and the current status unicorns with a high proportion of technology companies, the purpose was to analyze the impact of the country's technological competitiveness, such as 'technology human resource index', 'R&D index', and 'technology infrastructure index', on the increase in unicorn companies. For statistical analysis, data published by various international organizations, the Bank of Korea, and Statistics Korea from 2017 to 2020 and unicorn company data compiled by CB Insights were used as panel data for 44 countries to be tested by multiple regression analysis. As a result of the study, it was confirmed that the number of science majors had a positive (+) effect on the increase of unicorn companies in the case of technology human resource index, and in the case of R&D index, the total amount of R&D investment had a positive (+) effect on the increase of unicorn companies, while the number of Triad Patents Families and the number of scientific and technological papers published had a negative (-) effect on the increase of unicorn companies. Finally, in the case of technology infrastructure index, it was confirmed that the number of the world's 500th-ranked universities had a positive (+) effect on the increase of unicorn companies. This study is the first to reveal the causal relationship between national technological competitiveness and unicorn company growth based on country-specific and time-series empirical data, which were insufficiently covered in previous studies. and compared to the UN's ranking of the global industrial competitiveness index and the OECD's total R&D investment by country, Korea is considered to have technological and growth potential, while the number of unicorn companies driving growth as leaders of the innovative economy is relatively small, so the research results can be used when establishing policies to discover and foster unicorn companies in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.668-678
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• 2017

In this study, the hydrological and meteorological drought index with precipitation as a major factor were calculated, and various analyses of hydrological drought were conducted. The Modified Surface Water Supply Index (MSWSI) was applied to the hydrological drought index and Standardize Precipitation Index (SPI) was used to estimate the meteorological drought index. The target area for the estimation is the dam area among MSWSI categories. The 4001 basin with 43 years data from 1975 to 2017 was analyzed for the drought occurrence status and time series plotted with the monthly SPI and MSWSI. For the dam watershed based on the precipitation that has the role of a water supply in the hydrological cycle, correlation analysis of precipitation, dam inflow, and stream flow was performed by the monthly and moving average (2~9 months), and the correlation between meteorological and hydrological index by monthly and moving average (3, 6 months) was then calculated. The result of multifaced analysis of the hydrological drought index and meteorological drought index is believed to be useful in developing water policy.

• Journal of Korean Society of Transportation
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• v.35 no.6
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• pp.511-524
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• 2017

This study aims to evaluate the availability of ABM (Active-Based Model), FEATHERS, as a policy evaluation tool. To achieve the goal, scenario analysis on flexible working policy was conducted to measure its impact on activity-travel behavior. As a consequence, there seems no significant change in worker's daily life, other than mitigating traffic congestion due to decreasing commuting travel in the rush hour. The result of VKT (vehicle kilometers traveled) shows an opposite pattern according to given household/individual constraints. The scenario analysis on telecommuting indicates a decreasing trend in both travel frequency and distance because of the diminished number of commuting trips. As the activity space of telecommuters is shifted to a residential area, there are more short-distance trips by using non-motorized transport, which leads to decrease in VKT (using a private vehicle). Thus, the sensitivity of VKT by population groups varies due to transport mode shift (between personal and another mode) and growing non-work trips (using a private mode). This study found few things. First, it is necessary to evaluate the details of policy impact by population groups since it can be varied depending on household/individual characteristics. Second, the case study shows a promising performance of ABM as policy measurement that provides reality in policy evaluation. Third, ABM allows us to do more accurate analysis (i.e. time-series analysis by population groups) of policy assessment than those of FSM (Four-Step Model). Lastly, a further effort in data collection, literature review, and expert survey should be made to enhance the accuracy and confidence of future research.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.613-623
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• 2019

Over the past decades, daily sea surface temperature (SST) composite data have been produced using periodically and extensively observed satellite SST data, and have been used for a variety of purposes, including climate change monitoring and oceanic and atmospheric forecasting. In this study, we evaluated the accuracy and analyzed the error characteristic of the SST composite data in the sea around the Korean Peninsula for optimal utilization in the regional seas. We evaluated the four types of multi-satellite SST composite data including OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis), OISST (Optimum Interpolation Sea Surface Temperature), CMC (Canadian Meteorological Centre) SST, and MURSST (Multi-scale Ultra-high Resolution Sea Surface Temperature) collected from January 2016 to December 2016 by using in-situ temperature data measured from the Ieodo Ocean Research Station (IORS). Each SST composite data showed biases of the minimum of 0.12℃ (OISST) and the maximum of 0.55℃ (MURSST) and root mean square errors (RMSE) of the minimum of 0.77℃ (CMC SST) and the maximum of 0.96℃ (MURSST) for the in-situ temperature measurements from the IORS. Inter-comparison between the SST composite fields exhibited biases of -0.38-0.38℃ and RMSE of 0.55-0.82℃. The OSTIA and CMC SST data showed the smallest error while the OISST and MURSST data showed the most obvious error. The results of comparing time series by extracting the SST data at the closest point to the IORS showed that there was an apparent seasonal variation not only in the in-situ temperature from the IORS but also in all the SST composite data. In spring, however, SST composite data tended to be overestimated compared to the in-situ temperature observed from the IORS.

• Economic and Environmental Geology
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• v.34 no.4
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• pp.375-383
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• 2001

We extracted sea surface heights(SSH) from the TopexJPoseidon(T/P) radar altimeter data to compare with fhe SSH estimated from in-situ lide gauges(T/G) at Ulleungdo, Pohang, and SockcholMucko sites. Selection criteria such as wet/dry troposphere, ionosphere, and ocean tide were used to estimate accurate SSH. For time series analysis, the one-hour interval tide gauge SSHs were resampled al lO-day interval of the satellite SSHs. The ocean tide model applied in the altimeter data processing showed periodic aliasings of 175.5 day, 87.8 day, 62J day, 58.5 day, 49.5 day and 46.0 day, and, hence, the ZOO-day filtering was applied to reduce these spectral noises. Wavenumber correlation analysis was also applied to extract common components between the two SSHs, resulting in enhancing the correlation coefficient(CC) dramatically. The original CCs between the satenite and tide gauge SSHs are 0.46. 0.26, and 0.]5, respectively. Ulleungdo shows the largest cc bec;luase the site is far from the coast resulting in the minimun error in the satellite observations. The CCs were then increased to 0.59, 030, and 0.30, respectively, after 200.day filtering, and to 0.69, 0.63. and 0.59 after removing inversely correlative components using wavenumber correlation analysis. The CCs were greatly increased by 87, 227, and 460% when the wavenumber correlation analysis was followed by 2oo-day filtering, resulting in the final CCs of 0.86, 0.85, 0.84, respectively. It was found that the best SSHs were estimated when the two methods were applied to the original data. The low-pass filtered TIP SSHs were found to be well correlated with the TIG SSHs from tide gauges, and the best correlation results were found when we applied both low-pass filtering and spectral correlation analysis to the original SSHs.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.