• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.37 no.1
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• pp.56-67
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• 2019

In order to investigate the issue with the proper name of eshel(Tamarix aphylla) mentioned in the Bible, analysis of morphological taxonomy features of plants, studies on the symbolism of the Tamarix genus, analysis of examples in Korean classics and Chinese classics, and studies on the problems found in translations of Korean, Chinese and Japanese Bibles. The results are as follows. According to plant taxonomy, similar species of the Tamarix genus are differentiated by the leaf and flower, and because the size is very small about 2-4mm, it is difficult to differentiate by the naked eye. However, T. aphylla found in the plains of Israel and T. chinensis of China and Korea have distinctive differences in terms of the shape of the branch that droops and its blooming period. The Tamarix genus is a very precious tree that was planted in royal courtyards of ancient Mesopotamia and the Han(漢) Dynasty of China, and in ancient Egypt, it was said to be a tree that gave life to the dead. In the Bible, it was used as a sign of the covenant that God was with Abraham, and it also symbolized the prophet Samuel and the court of Samuel. When examining the example in Korean classics, the Tamarix genus was used as a common term in the Joseon Dynasty and it was often used as the medical term '$Ch{\bar{e}}ngli{\check{u}}$(檉柳)'. Meanwhile, the term 'wiseonglyu(渭城柳)' was used as a literary term. Upon researching the period and name of literature related to $Ch{\bar{e}}ngli{\check{u}}$(檉柳) among Chinese medicinal herb books, a total of 16 terms were used and among these terms, the term Chuísīliǔ(垂絲柳) used in the Chinese Bible cannot be found. There was no word called 'wiseonglyu(渭城柳)' that originated from the poem by Wang Wei(699-759) of Tang(唐) Dynasty and in fact, the word 'halyu(河柳)' that was related to Zhou(周) China. But when investigating the academic terms of China currently used, the words Chuísīliǔ(垂絲柳) and $Ch{\bar{e}}ngli{\check{u}}$(檉柳) are used equally, and therefore, it appears that the translation of eshel in the Chinese Bible as either Chuísīliǔ (垂絲柳) or $Ch{\bar{e}}ngli{\check{u}}$(檉柳) both appear to be of no issue. There were errors translating tamarix into 'やなぎ(willow)' in the Meiji Testaments(舊新約全書 1887), and translated correctly 'ぎょりゅう(檉柳)' since the Colloquial Japanese Bible(口語譯 聖書 1955). However, there are claims that 'gyoryu(ぎょりゅう 檉柳)' is not an indigenous species but an exotics species in the Edo Period, so it is necessary to reconsider the terminology. As apparent in the Korean classics examples analysis, there is high possibility that Korea's T. chinensis were grown in the Korean Peninsula for medicinal and gardening purposes. Therefore, the use of the medicinal term $Ch{\bar{e}}ngli{\check{u}}$(檉柳) or literary term 'wiseonglyu' in the Korean Bible may not be a big issue. However, the term 'wiseonglyu' is used very rarely even in China and as this may be connected to the admiration of China and Chinese things by literary persons of the Joseon Dynasty, so the use of this term should be reviewed carefully. Therefore, rather than using terms that may be of issue in the Bible, it is more feasible to transliterate the Hebrew word and call it eshel.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Horticultural Science & Technology
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• v.34 no.2
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• pp.305-313
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• 2016

Brassica juncea (2n = 4x = 36, AABB genome, 1,068 Mb) is a U's triangle species and an amphidiploid derivative of B. rapa and B. nigra. Fifteen varieties were used to study the ITS (internal transcribed spacer) regions of ribosomal DNA and MITEs (miniature inverted-repeat transposable elements) with a view of developing specific molecular markers. ITSs and MITEs are an excellent resource for developing DNA markers for genomics and evolutionary studies because most of them are stably inherited and present in high copy numbers. The ITS (ITS1 and ITS2) sequence was compared with the consensus sequence of B. rapa and B. nigra. Variation in ITS1 created two separate groups among 15 varieties, with 10 varieties in one group and 5 in the other. Phylogenetic analysis revealed two major clusters for those 10 and 5 varieties. Among the 160 different MITE primers used to evaluate the selected 15 varieties of B. juncea, 70 were related to the Stowaway, 79 to the Tourist, 6 to the hAT, and 5 to the Mutator super-families of MITEs. Of 160 markers examined, 32 were found to be polymorphic when fifteen different varieties of B. juncea were evaluated. The variety 'Blackgat' was different from the other mustard varieties with respect to both phenotype and genotype. The diversity of 47 additional accessions could be verified using eight selected molecular markers derived from MITE family sequences. The polymorphic markers identified in this study can be used for varietal classification, variety protection, and other breeding purposes.

• Journal of the Korean Geographical Society
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• v.40 no.3 s.108
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• pp.296-309
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• 2005

In this study, we investigated the change of future land-surface and relationships of land-surface change with geo-spatial information, using a Bayesian prediction model based on a likelihood ratio function, for analysing the land-surface change of the Gongju area. We classified the land-surface satellite images, and then extracted the changing area using a way of post classification comparison. land-surface information related to the land-surface change is constructed in a GIS environment, and the map of land-surface change prediction is made using the likelihood ratio function. As the results of this study, the thematic maps which definitely influence land-surface change of rural or urban areas are elevation, water system, population density, roads, population moving, the number of establishments, land price, etc. Also, thematic maps which definitely influence the land-surface change of forests areas are elevation, slope, population density, population moving, land price, etc. As a result of land-surface change analysis, center proliferation of old and new downtown is composed near Gum-river, and the downtown area will spread around the local roads and interchange areas in the urban area. In case of agricultural areas, a small tributary of Gum-river or an area of local roads which are attached with adjacent areas showed the high probability of change. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the capability of forest damage is very high. As a result of validation using a prediction rate curve, a capability of prediction of urban area is $80\%$, agriculture area is $55\%$, forest area is $40\%$ in higher $10\%$ of possibility which the land-surface change would occur. This integration model is unsatisfactory to Predict the forest area in the study area and thus as a future work, it is necessary to apply new thematic maps or prediction models In conclusion, we can expect that this way can be one of the most essential land-surface change studies in a few years.

• Korean Journal of Environment and Ecology
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• v.22 no.2
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• pp.173-183
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• 2008

This research was aimed at looking into the vegetation structure of Abies holophylla forest distributed between Iljumun of Woljeong Temple and Keumgang bridge in Odaesan National Park. It was found that existed a total of 977 tree of Abies holophylla which are more than 20cm in DBH within the target site, and in 2006 when the survey was made, the number of fallen trees and poor growth trees was about 96, accounting for 9.8% of all. The age of Abies holophylla ranged from 41 years to 135 years($11\sim82cm$ in DBH). The number of Abies holophylla over 100cm in DBH was 8 and the largest Abies holophylla was 175cm in DBH and 31m in height. Its density was 5.9 individuals per $400m^2$. As a result of the analysis of the plant community structure using the TWINSP AN classification, Abies holophylla was divided into four community types. Firstly, Pinus densiflora-Abies holophylla community was predicted to vary into Abies holophylla community. In case of other three other communities, Abies holophylla communities were predicted to compete with deciduous broadleaf trees, such as Tilia amurensis and Acer pictum subsp. mono. Abies holophylla forest adjacent to Woljeong Temple of Odaesan National Park has a high value as sustainable resources for culture, landscape and tourism. Thus, it is necessary to clarify the reason for the incidence of poor growth trees and fallen trees among all trees of Abies holophylla and take counter-measures against it for the preservation and management of Abies holophylla forest. In addition, a more aggressive managrment like getting rid of the deciduous broadleaf trees, such as Tilia amurensis and Acer pictum subsp. mono, which appear mostly on understory layer or shrub layer within Abies holophylla, and continuous management is also needed for the young trees of Abies holophylla which are feared to be pressurized outside from their neighboring trees because their initial growth after germination is very slow.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.