• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Korean Journal of Construction Engineering and Management
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• v.24 no.2
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• pp.88-97
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• 2023

The purpose of this study is to forecast future changes in the construction market following the reorganization of the construction production system by analyzing the possible market size in which general contractors and specialty contractors may participate in each other, and by carrying out a survey. The ratio of correlation between general construction and specialty construction industries was derived by analyzing the relevance of work area of general contractors and specialty contractors, the similarity of registration standards, and the market in which general contractors and specialty contractors may be able to mutually participate. In order to overcome the limitation of previous studies which analyze the changes in construction market based on the statistical data, and to analyze in more detail the impact of reorganization of construction production system from market participants' view, a survey targeting general contractors and specialty contractors for their willingness and method of participating in the mutual market was conducted. As a result of the survey, it was found that 52% of general contractors were willing to participate in the specialized construction market and 55.1% of specialty contractors were willing to participate in the general construction market. It was found that there was a high willingness to participate in the earthworks, reinforced concrete works, facility maintenance and management, water and sewage facility works, and interior works, and high competition is expected for projects with a scale of 500 million to less than 3 billion won. Through this study, it will be possible for general and specialty contractors to understand the changes in the construction market due to the reorganization of the construction industry production system, and to respond effectively to these changes.

• Analyses & Alternatives
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• v.7 no.3
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• pp.1-26
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• 2023

This article will analyze and forecast important variables and dynamics in global power politics after the war in Ukraine. It tries to use several perspectives to analyze international relations, particularly liberal internationalism and structural realism. In short, core variables are as follows; First, how is the US-led liberal international order and globalization being adjusted? Second, how will the U.S.-China strategic competition, which is the biggest and structural variable, cause changes in the international order in the future? The third variable, how stable are Sino-Russia relations in the context of a structuring U.S.-China-Russia strategic new triangle? Fourth, to what extent will third middle hedging states outside the U.S. and China be able to exercise strategic autonomy in the face of multipolarization? To summarize, the first of these four variables is the largest basic variable at the global political and economic level in terms of its impact on the international community, and it has been led by the United States. The second variable, in terms of actors, seems to be the most influential structural variable in global competition, and the US-China strategic competition is likely to be a long game. Thus the world will not be able to escape the influence of the competition between the two global powers. For South Korea, this second variable is probably the biggest external variable and dilemma. The third variable, the stability of Sino-Russia relations, determines balance of global power in the 21st century. The U.S.-China-Russia strategic new triangle, as seen in the current war in Ukraine, will operate as the greatest power variable in not only global power competition but also changes in the international order. Just as the U.S. is eager for a Sino-Russia fragmentation strategy, such as a Tito-style wedge policy to manage balance of power in the early years of the Cold War, it needs a reverse Kissinger strategy to reset the U.S.-Russia relationship, in order to push for a Sino-Russia splitting in the 21st century. But with the war in Ukraine, it seems that this fragmentation strategy has already been broken. In the context of Northeast Asia, whether or not the stability of Sino-Russia relations depends not only on the United States, but also on the Korean Peninsula. Finally, the fourth variable is a dependent variable that emerged as a result of the interaction of the above three variables, but simultaneously it remains to be seen that this variable is likely to act as the most dynamic and independent variable that can promote multilateralism, multipolarization, and pan-regionalism of the global international community in the future. Taking into account these four variables together, we can make an outlook on the change in the international order.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• The Korean Journal of Financial Management
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• v.27 no.2
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• pp.169-204
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• 2010

This paper examines the dynamic relationship between fund flow and market risk at the aggregate level and explores whether sudden sharp changes in fund flow (fund run) can cause a systemic risk in the Korean financial markets. We use daily and weekly data and regression and VAR analysis. Main results of the paper are as follows: First, in the stock market, a concurrent and a lagged unexpected fund flows have a positive relationship with market volatility. A positive shock in fund flow predicts an increase in stock market volatility. In the bond market, an unexpected fund flow has a negative relationship with the default risk premium, but a positive relationship with the term premium. And an unexpected fund flow of the money market fund has a negative relationship with the liquidy risk, but the explanatory power is very low. Second, for examining whether changes in fund flow induce a systemic risk, we construct a spillover index based on the forecast error variance decomposition of VAR model. A spillover index represents that how much the shock in fund flow can explain the change of market risk in a market. In general, explanatory powers from spillover indexes are so fluctuant and low. In the stock market, the impact of shocks in fund flow on market risk is relatively high and persistent during the period from the end of 2007 to 2008, which is the subprime-mortgage crisis period. In bond market, since the end of 2008, the impact of shocks in fund flow spreads to default risk continually, while in the money market, such a systematic effect doesn't take place. The persistent patterns of spillover effect appearing around a certain period in the stock market and the bond market suggest that the shock to the unexpected fund flow may increase the market risk and can be a cause of systemic risk in the financial markets. However, summarizing the results of regression and VAR model analysis, and considering the very low explanatory power of spillover index analysis, we can conclude that changes in fund flow have a very limited power in explaining changes in market risk and it is not very likely to induce the systemic risk by a fund run in the Korean financial markets.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.115-124
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• 2023

Currently, as the frequency of extreme weather events increases, the scale of damage increases when extreme weather events occur. This has been providing forecast information by investing a lot of time and resources to predict rainfall from the past. However, this information is difficult for non-experts to understand, and it does not include information on how much damage occurs when extreme weather events occur. Therefore, in this study, a risk matrix based on heavy rain damage rating was presented by using the impact forecasting standard through the creation of a risk matrix presented for the first time in the UK. First, through correlation analysis between rainfall data and damage data, variables necessary for risk matrix creation are selected, and PERCENTILE (25%, 75%, 90%, 95%) and JNBC (Jenks Natural Breaks Classification) techniques suggested in previous studies are used. Therefore, a rating standard according to rainfall and damage was calculated, and two rating standards were synthesized to present one standard. As a result of the analysis, in the case of the number of households affected by the disaster, PERCENTILE showed the highest distribution than JNBC in the Yeongsan River and Seomjin River basins where the most damage occurred, and similar results were shown in the Chungcheong-do area. Looking at the results of rainfall grading, JNBC's grade was higher than PERCENTILE's, and the highest grade was shown especially in Jeolla-do and Chungcheong-do. In addition, when comparing with the current status of heavy rain warnings in the affected area, it can be confirmed that JNBC is similar. In the risk matrix results, it was confirmed that JNBC replicated better than PERCENTILE in Sejong, Daejeon, Chungnam, Chungbuk, Gwangju, Jeonnam, and Jeonbuk regions, which suffered the most damage.

• 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.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.27-34
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• 2010

During the past 10 years, sales of luxury goods increased significantly to more than US$ 130 billion in 2007. In this industry, more than half of the revenue comes from Asia where the average income has risen significantly, and the demand for luxury products is forecast to grow rapidly. Purchasing luxury brands appears to be an intriguing social phenomenon that is profitable for companies in this region. As a newly developed country, Korea is one of the most attractive luxury markets in Asia. Currently, a total of 120 luxury fashion brands have entered the Korean market, primarily in luxury districts in Seoul where the competition is fierce. The purposes of this study are to: (1) identify antecedents of attitude toward luxury brands, (2) examine the effect of attitudes toward luxury brands on customer equity, (3) determine the impact of attitudes toward luxury brands on customer lifetime value, and (4) investigate the influence of customer equity on customer life time value. Previous studies have examined materialism, social need, experiential need, need for uniqueness, conformity, and fashion involvement as antecedents of attitude toward luxury brands. Richins and Dowson (1992) suggested that that materialism influences consumption behavior relative to quantity of goods purchased. Nueno and Quelch (1998) reported that the ownership of luxury brands conveys information related to the owner's social status, communicates an image of success and prestige, and is a determinant of purchase behavior. Experiential need is recognized as an important aspect of consumption, especially for new products developed to meet consumer demand. Since luxury goods, by definition are relatively scarce, ownership of these types of products may fulfill consumers' need for uniqueness. In this study, value equity, relationship equity, and brand equity are examined as drivers of customer equity. The sample (n = 114) was undergraduate and graduate students at two private women's universities in Seoul, Korea. Data collection was conducted using a self-administered questionnaire survey in March, 2009. Data analysis included descriptive statistics, factor analysis, reliability analysis, and regression analysis using SPSS 15.0 software. Data analysis resulted in a number of conclusions. First, experiential need and fashion involvement positively influence participants' attitude toward luxury brands. Second, attitude toward luxury brands positively influences brand equity, followed by value equity and relationship equity. However, there is no significant relationship between attitude toward luxury brand and customer lifetime value. Finally, relationship equity positively influences customer lifetime value. In conclusion, young consumers are an important potential consumer group that tries different brands to discover the ones most suitable for them. Luxury marketers that use effective marketing strategies to attract and engender loyalty among this potentially lucrative consumer group may increase customer equity and lifetime value.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.