• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Economic and Environmental Geology
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• v.39 no.4 s.179
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• pp.369-384
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• 2006

Korean Peninsula, located on the southeastern part of Eurasian plate, belongs to the intraplate region. The characteristics of intraplate earthquake show the low and rare seismicity and the sparse and irregular distribution of epicenters comparing to interplate earthquake. To evaluate the exact seismic activity in intraplate region, long-term seismic data including historical earthquake data should be archived. Fortunately the long-term historical earthquake records about 2,000 years are available in Korea Peninsula. By the analysis of this historical and instrumental earthquake data, seismic activity was very high in 16-18 centuries and is more active at the Yellow sea area than East sea area. Comparing to the high seismic activity of the north-eastern China in 16-18 centuries, it is inferred that seismic activity in two regions shows close relationship. Also general trend of epicenter distribution shows the SE-NW direction. In Korea Peninsula, the first seismic station was installed at Incheon in 1905 and 5 additional seismic stations were installed till 1943. There was no seismic station from 1945 to 1962, but a World Wide Standardized Seismograph was installed at Seoul in 1963. In 1990, Korean Meteorological Adminstration(KMA) had established centralized modem seismic network in real-time, consisted of 12 stations. After that time, many institutes tried to expand their own seismic networks in Korea Peninsula. Now KMA operates 35 velocity-type seismic stations and 75 accelerometers and Korea Institute of Geoscience and Mineral Resources operates 32 and 16 stations, respectively. Korea Institute of Nuclear Safety and Korea Electric Power Research Institute operate 4 and 13 stations, consisted of velocity-type and accelerometer. In and around the Korean Peninsula, 27 intraplate earthquake mechanisms since 1936 were analyzed to understand the regional stress orientation and tectonics. These earthquakes are largest ones in this century and may represent the characteristics of earthquake in this region. Focal mechanism of these earthquakes show predominant strike-slip faulting with small amount of thrust components. The average P-axis is almost horizontal ENE-WSW. In north-eastern China, strike-slip faulting is dominant and nearly horizontal average P-axis in ENE-WSW is very similar with the Korean Peninsula. On the other hand, in the eastern part of East Sea, thrust faulting is dominant and average P-axis is horizontal with ESE-WNW. This indicate that not only the subducting Pacific Plate in east but also the indenting Indian Plate controls earthquake mechanism in the far east of the Eurasian Plate. Crustal velocity model is very important to determine the hypocenters of the local earthquakes. But the crust model in and around Korean Peninsula is not clear till now, because the sufficient seismic data could not accumulated. To solve this problem, reflection and refraction seismic survey and seismic wave analysis method were simultaneously applied to two long cross-section traversing the southern Korean Peninsula since 2002. This survey should be continuously conducted.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• (The)Study of the Eastern Classic
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• no.63
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• pp.9-41
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• 2016

This paper focuses on 'clean(cheong: 淸)' kinds of style terminologies among various style terminologies appearing in Heo Gyun's Seongsushihwa("惺?詩話") and tries to analyze the distinctive points which 'clean(cheong: 淸)' kinds of style terminologies include. In Heo Gyun's Seongsushihwa, 11 of 'clean' kinds of style terminologies, such as "cheonggyeong(淸勁), cheonghryang(淸亮), cheongryeo(淸麗), cheongseom(淸贍), cheongso(淸?), cheongweol(淸越), cheongjang(淸壯), cheongjeol(淸絶), cheongjeol(淸切), cheongchang(淸?), cheongcho(淸楚)," were used. This paper focuses and analyzes 'cheonggyeong(淸勁)', 'cheongjeol(淸切)', 'cheongcho(淸楚)', and 'cheongweol(淸越)' that he suggested through applying to real literary pieces. The result of analysis indicates that 'clean' kinds of style terminologies 'cheonggyeong', 'cheongjeol', 'cheongcho', and 'cheongweol' share the same 1st character 'clean(淸)', yet have distinctive qualities by the 2nd characters. These 4 style terminologies all share 'cheong(淸)' image which means clear and clean, yet each one has the attribute of the 2nd character that indicates each one's individual characteristic. It is apparent that 'Cheonggyeong(淸勁)' reflects the 'gyeong(勁)' image meaning upright and solid and implies poems of poets' steadfast spirit within clear boundary; 'cheongjeol(淸切)' reflects the 'jeol(切)' image meaning either desperation and imminence or pitifulness and sorrow and implies poems of poets' urgent and pitiful emotions within clear and clean boundary; 'cheongcho(淸楚)' reflects the 'cho(楚)' image meaning either delicacy and fineness or slenderness and tenderness and implies poems of poets' beautiful but not luxurious, delicate and tender emotions within clear and clean boundary; and 'cheongweol(淸越)' reflects the image of 'weol(越)' meaning unworldliness and excellency and implies poems, within clear and clean boundary, of excellent appearance and mentality surpassing mundane world. Compared with the 1st character's attributes of the style terminologies which Heo Gyun used, the 2nd characters's attributes do not appear that vivid. Especially, in the case that the 2nd characters have similar meanings, it is not easy to clarify the categories. Indeed, in order to grasp clear and distinctive qualities of style terminologies, the kinds of them need to be initially categorized by the 1st characters, and then sorted by the 2nd characters. In this case, the contents which the 2nd characters of style terminologies indicate should be considered. It is because style terminologies explain both literary pieces' aesthetic qualities and writers' personalities, and because explanations about literary pieces' aesthetic qualities includes not only the conclusive poetic or semantic boundaries which literary pieces' created but also literary pieces' creation processes and expression techniques. Through the style terminologies with Heo Gyun used in Seongsushihwa, it can be aware that he evaluated poems focussing more on the conclusive semantic boundaries that poets' spirits and poems created than expression techniques or creation methods. The overall aspects Heo Gyun's such style criticism has will be checked out in more detail through further studies by examining more materials.

• MISULJARYO - National Museum of Korea Art Journal
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• v.96
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• pp.54-88
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• 2019

The Album of Complete Views of Seas and Mountains comprises sixty real scenery landscape paintings depicting Geumgangsan Mountain, the Haegeumgang River, and the eight scenic views of Gwandong regions, as well as fifty-one pieces of writing. It is a rare example in terms of its size and painting style. The paintings in this album, which are densely packed with natural features, follow the painting style of the Southern School yet employ crude and unconventional elements. In them, stones on the mountains are depicted both geometrically and three-dimensionally. Since 1973, parts of this album have been published in some exhibition catalogues. The entire album was opened to the public at the special exhibition "Through the Eyes of Joseon Painters: Real Scenery Landscapes of Korea" held at the National Museum of Korea in 2019. The Album of Complete Views of Seas and Mountains was attributed to Kim Eung-hwan (1742-1789) due to the signature on the final leaf of the album and the seal reading "Bokheon(painter's penname)" on the currently missing album leaf of Chilbodae Peaks. However, there is a strong possibility that this signature and seal may have been added later. This paper intends to reexamine the creator of this album based on a variety of related factors. In order to understand the production background of Album of Complete Views of Seas and Mountains, I investigated the eighteenth-century tradition of drawing scenic spots while travelling in which scenery of was depicted during private travels or official excursions. Jeong Seon(1676-1759), Sim Sa-jeong(1707-1769), Kim Yun-gyeom(1711-1775), Choe Buk(1712-after 1786), and Kang Se-hwang(1713-1791) all went on a journey to Geumgangsan Mountain, the most famous travel destination in the late Joseon period, and created paintings of the mountain, including Album of Pungak Mountain in the Sinmyo Year(1711) by Jeong Seon. These painters presented their versions of the traditional scenic spots of Inner Geumgangsan and newly depicted vistas they discovered for themselves. To commemorate their private visits, they produced paintings for their fellow travelers or sponsors in an album format that could include several scenes. While the production of paintings of private travels to Geumgangsan Mountain increased, King Jeongjo(r. 1776-1800) ordered Kim Eung-hwan and Kim Hong-do, court painters at the Dohwaseo(Royal Bureau of Painting), to paint scenic spots in the nine counties of the Yeongdong region and around Geumgangsan Mountain. King Jeongjo selected these two as the painters for the official excursion taking into account their relationship, their administrative experience as regional officials, and their distinct painting styles. Starting in the reign of King Yeongjo(r. 1724-1776), Kim Eung-hwan and Kim Hong-do served as court painters at the Dohwaseo, maintained a close relationship as a senior and a junior and as colleagues, and served as chalbang(chief in large of post stations) in the Yeongnam region. While Kim Hong-do was proficient at applying soft and delicate brushstrokes, Kim Eung-hwan was skilled at depicting the beauty of robust and luxuriant landscapes. Both painters produced about 100 scenes of original drawings over fifty days of the official excursion. Based on these original drawings, they created around seventy album leaves or handscrolls. Their paintings enriched the tradition of depicting scenic spots, particularly Outer Inner Geumgang and the eight scenic views of Gwandong around Geumgangsan Mountain during private journeys in the eighteenth century. Moreover, they newly discovered places of scenic beauty in the Outer Geungang and Yeongdong regions, establishing them as new painting themes. The Album of Complete Views of Seas and Mountains consists of four volumes. The volumes I, II include twenty-nine paintings of Inner Geumgangsan; the volume III, seventeen scenes of Outer Geumgangsan; and the volume IV, fourteen images of Maritime Geumgangsan and the eight scenic views of Gwandong. These paintings produced on silk show crowded compositions, geometrical depictions of the stones and the mountains, and distinct presentation of the rocky peaks of Geumgangsan Mountain using white and grayish-blue pigments. This album reflects the Joseon painting style of the mid- and late eighteenth century, integrating influences from Jeong Seon, Kang Se-hwang, Sim Sa-jeong, Jeong Chung-yeop(1725-after 1800), and Kim Hong-do. In particular, some paintings in the album show similarities to Kim Hong-do's Album of Famous Mountains in Korea in terms of its compositions and painterly motifs. However, "Yeongrangho Lake," "Haesanjeong Pavilion," and "Wolsongjeong Pavilion" in Kim Eung-hwan's album differ from in the version by Kim Hong-do. Thus, Kim Eung-hwan was influenced by Kim Hong-do, but produced his own distinctive album. The Album of Complete Views of Seas and Mountains includes scenery of "Jaundam Pool," "Baegundae Peak," "Viewing Birobong Peak at Anmunjeom groove," and "Baekjeongbong Peak," all of which are not depicted in other albums. In his version, Kim Eung-hwan portrayed the characteristics of the natural features in each scenic spot in a detailed and refreshing manner. Moreover, he illustrated stones on the mountains using geometric shapes and added a sense of three-dimensionality using lines and planes. Based on the painting traditions of the Southern School, he established his own characteristics. He also turned natural features into triangular or rectangular chunks. All sixty paintings in this album appear rough and unconventional, but maintain their internal consistency. Each of the fifty-one writings included in the Album of Complete Views of Seas and Mountains is followed by a painting of a scenic spot. It explains the depicted landscape, thus helping viewers to understand and appreciate the painting. Intimately linked to each painting, the related text notes information on traveling from one scenic spot to the next, the origins of the place names, geographic features, and other related information. Such encyclopedic documentation began in the early nineteenth century and was common in painting albums of Geumgangsan Mountain in the mid- nineteenth century. The text following the painting of Baekhwaam Hermitage in the Album of Complete Views of Seas and Mountains documents the reconstruction of the Baekhwaam Hermitage in 1845, which provides crucial evidence for dating the text. Therefore, the owner of the Album of Complete Views of Seas and Mountains might have written the texts or asked someone else to transcribe them in the mid- or late nineteenth century. In this paper, I have inferred the producer of the Album of Complete Views of Seas and Mountains to be Kim Eung-hwan based on the painting style and the tradition of drawing scenic spots during official trips. Moreover, its affinity with the Handscroll of Pungak Mountain created by Kim Ha-jong(1793-after 1878) after 1865 is another decisive factor in attributing the album to Kim Eung-hwan. In contrast to the Album of Famous Mountains in Korea by Kim Hong-do, the Album of Complete Views of Seas and Mountains exerted only a minor influence on other painters. The Handscroll of Pungak Mountain by Kim Ha-jong is the sole example that employs the subject matter from the Album of Complete Views of Seas and Mountains and follows its painting style. In the Handscroll of Pungak Mountain, Kim Ha-jong demonstrated a painting style completely different from that in the Album of Seas and Mountains that he produced fifty years prior in 1816 for Yi Gwang-mun, the magistrate of Chuncheon. He emphasized the idea of "scholar thoughts" by following the compositions, painterly elements, and depictions of figures in the painting manual style from Kim Eung-hwan's Album of Complete Views of Seas and Mountains. Kim Ha-jong, a member of the Gaeseong Kim clan and the eldest grandson of Kim Eung-hwan, is presumed to have appreciated the paintings depicted in the nature of Album of Complete Views of Seas and Mountains, which had been passed down within the family, and newly transformed them. Furthermore, the contents and narrative styles of Yi Yu-won's writings attached to the paintings in the Handscroll of Pungak Mountain are similar to those of the fifty-one writings in Kim Eunghwan's album. This suggests a possible influence of the inscriptions in Kim Eung-hwan's album or the original texts from which these inscriptions were quoted upon the writings in Kim Ha-jong's handscroll. However, a closer examination will be needed to determine the order of the transcription of the writings. The Album of Complete View of Seas and Mountains differs from Kim Hong-do's paintings of his official trips and other painting albums he influenced. This album is a siginificant artwork in that it broadens the understanding of the art world of Kim Eung-hwan and illustrates another layer of real scenery landscape paintings in the late eighteenth century.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.69-76
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• 2013

The incidence of globally infectious and pathogenic diseases such as H1N1 (swine flu) and Avian Influenza (AI) has recently increased. An infectious disease is a pathogen-caused disease, which can be passed from the infected person to the susceptible host. Pathogens of infectious diseases, which are bacillus, spirochaeta, rickettsia, virus, fungus, and parasite, etc., cause various symptoms such as respiratory disease, gastrointestinal disease, liver disease, and acute febrile illness. They can be spread through various means such as food, water, insect, breathing and contact with other persons. Recently, most countries around the world use a mathematical model to predict and prepare for the spread of infectious diseases. In a modern society, however, infectious diseases are spread in a fast and complicated manner because of rapid development of transportation (both ground and underground). Therefore, we do not have enough time to predict the fast spreading and complicated infectious diseases. Therefore, new system, which can prevent the spread of infectious diseases by predicting its pathway, needs to be developed. In this study, to solve this kind of problem, an integrated monitoring system, which can track and predict the pathway of infectious diseases for its realtime monitoring and control, is developed. This system is implemented based on the conventional mathematical model called by 'Susceptible-Infectious-Recovered (SIR) Model.' The proposed model has characteristics that both inter- and intra-city modes of transportation to express interpersonal contact (i.e., migration flow) are considered. They include the means of transportation such as bus, train, car and airplane. Also, modified real data according to the geographical characteristics of Korea are employed to reflect realistic circumstances of possible disease spreading in Korea. We can predict where and when vaccination needs to be performed by parameters control in this model. The simulation includes several assumptions and scenarios. Using the data of Statistics Korea, five major cities, which are assumed to have the most population migration have been chosen; Seoul, Incheon (Incheon International Airport), Gangneung, Pyeongchang and Wonju. It was assumed that the cities were connected in one network, and infectious disease was spread through denoted transportation methods only. In terms of traffic volume, daily traffic volume was obtained from Korean Statistical Information Service (KOSIS). In addition, the population of each city was acquired from Statistics Korea. Moreover, data on H1N1 (swine flu) were provided by Korea Centers for Disease Control and Prevention, and air transport statistics were obtained from Aeronautical Information Portal System. As mentioned above, daily traffic volume, population statistics, H1N1 (swine flu) and air transport statistics data have been adjusted in consideration of the current conditions in Korea and several realistic assumptions and scenarios. Three scenarios (occurrence of H1N1 in Incheon International Airport, not-vaccinated in all cities and vaccinated in Seoul and Pyeongchang respectively) were simulated, and the number of days taken for the number of the infected to reach its peak and proportion of Infectious (I) were compared. According to the simulation, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days when vaccination was not considered. In terms of the proportion of I, Seoul was the highest while Pyeongchang was the lowest. When they were vaccinated in Seoul, the number of days taken for the number of the infected to reach at its peak was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. When they were vaccinated in Pyeongchang, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. Based on the results above, it has been confirmed that H1N1, upon the first occurrence, is proportionally spread by the traffic volume in each city. Because the infection pathway is different by the traffic volume in each city, therefore, it is possible to come up with a preventive measurement against infectious disease by tracking and predicting its pathway through the analysis of traffic volume.