• The Journal of Korean Society for Radiation Therapy
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• v.26 no.1
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• pp.127-135
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• 2014

Purpose : By taking advantage of each imaging modality, the use of fused CT/MRI image has increased in prostate cancer radiation therapy. However, fusion uncertainty may cause partial target miss or normal organ overdose. In order to complement such limitation, our hospital acquired MRI image (Planning MRI) by setting up patients with the same fixing tool and posture as CT simulation. This study aims to evaluate the usefulness of the Planning MRI through comparing and analyzing the diagnostic MRI image and Planning MRI image. Materials and Methods : This study targeted 10 patients who had been diagnosed with prostate cancer and prescribed nonhormone and definitive RT 70 Gy/28 fx from August 2011 to July 2013. Each patient had both CT and MRI simulations. The MRI images were acquired within one half hour after the CT simulation. The acquired CT/MRI images were fused primarily based on bony structure matching. This study measured the volume of prostate in the images of Planning MRI and diagnostic MRI. The diameters at the craniocaudal, anteroposterior and left-to-right directions from the center of prostate were measured in order to compare changes in the shape of prostate. Results : As a result of comparing the volume of prostate in the images of Planning MRI and diagnostic MRI, they were found to be $25.01cm^3$(range $15.84-34.75cm^3$) and $25.05cm^3$(range $15.28-35.88cm^3$) on average respectively. The diagnostic MRI had an increase of 0.12 % as compared with the Planning MRI. On the planning MRI, there was an increase in the volume by $7.46cm^3$(29 %) at the transition zone directions, and there was a decrease in the volume by $8.52cm^3$(34 %) in the peripheral zone direction. As a result of measuring the diameters at the craniocaudal, anteroposterior and left-to-right directions in the prostate, the Planning MRI was found to have on average 3.82cm, 2.38cm and 4.59cm respectively and the diagnostic MRI was found to have on average 3.37cm, 2.76cm and 4.51cm respectively. All three prostate diameters changed and the change was significant in the Planning MRI. On average, the anteroposterior prostate diameter decrease by 0.38cm(13 %). The mean right-to-left and craniocaudal diameter increased by 0.08cm(1.6 %) and 0.45cm(13 %), respectively. Conclusion : Based on the results of this study, it was found that the total volumes of prostate in the Planning MRI and the diagnostic MRI were not significantly different. However, there was a change in the shape and partial volume of prostate due to the insertion of prostate balloon tube to the rectum. Thus, if the Planning MRI images were used when conducting the fusion of CT/MRI images, it would be possible to include the target in the CTV without a loss as much as the increased volume in the transition zone. Also, it would be possible to reduce the radiation dose delivered to the rectum through separating more clearly the reduction of peripheral zone volume. Therefore, the author of this study believes that acquisition of Planning MRI image should be made to ensure target delineation and localization accuracy.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.3
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• pp.32-45
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• 2014

The study attempts to interpret original location and landscape composition of Yowol-pavilion Garden under the premise that tablet and poetry are important criteria for inference of unique location and landscape composition of a pavilion garden. The study raises the meaning, status, and value of Yowol Pavilion Garden as a cultural asset. The results of the study are as follows. First, Yowol-pavilion Garden was a place where famous Confucius scholars in Joseon Dynasty in 16th Century, including Kim, Kyung-Woo, the owner of the garden, used to share the taste for the arts and poetries with their colleagues. Along with a main characteristic of Yowol Pavilion Garden as a hideout for the Confucius scholars who stayed away from a political turmoil, the new place characteristic of the garden, a bridgehead for the formation of regional identity, was discovered in the record of "Joseon-Hwanyeo-Seungram Honam-Eupji JangSeong-Eupji", As described in "The first creative poetry of Yowol-pavilion", the intention for the creation of Yowol-pavilion Garden and the motive for its landscape composition is interpreted as a space of rivalry where the world, reality and ideals are mixed up. Second, related to outstanding scenic factors and natural phenomena when taking a view from the pavilion, the name of the house 'Yowol', which means 'Greeting the moon rising on the Mt. Wolbong' is the provision of nature and taste for the arts, and is directly connected to the image of leaving the worldly. In other words, the name was identified to be the one that reflected the intention for landscape composition to follow the provision of nature separating from joy and sorrow of the mundane world. Third, as for the location, it was confirmed through "YeongGwang-Soksu-Yeoji-Seungram" that Yowol-pavilion Garden was a place where the person who made the pavilion prepared for relaxation after stepping down from a government post, and literature and various poetry show that it was also a place of outstanding scenic where Yellow-dragon River meandered facing Mt. Wolbong. Especially, according to an interview with a keeper, the visual perception frequency of the nightscape of Yowol-pavilion Garden is the highest when viewing by considering the east, the direction of Yellow-dragon River, as Suksigak[normal angle's view], towards Yowel-pavilion from the keeper's house. In addition, he said that the most beautiful landscape with high perception strength is when the moon came up from the left side of Yowol-pavilion, cuts across the Lagerstroemia india heal in front of Yowol-pavilion, and crosses the meridian between Mt. Wolbong peaks facing Yowol-pavilion. Currently, the exposure of Yowol-pavilion Garden is $SE\;141.2^{\circ}$, which is almost facing southeast. It is assumed that the exposure of Yowol-pavilion Garden was determined considering the optimized direction for appreciating the trace of the moon and the intention of securing the visibility as well as topographic conditions. Furthermore, it is presumed that the exposure of Yowol-pavilion Garden was determined so that the moon is reflected on the water of Yellow-dragon River and the moon and its reflection form a symmetry. Fourth, currently, Yowol-pavilion Garden is divided into 'inner garden sphere' composed of Yowol-pavilion, meeting place of the clan and administration building, and 'outer garden sphere' which is inclusive of entrance space, Crape Myrtle Community Garden and Pine Tree Forest in the back. Further, Yowol-pavilion Garden has been deteriorated as the edge was expanded to 'Small lake[Yong-so] and Gardens of aquatic plants sphere' and recently-created 'Yellow-dragon Pavilion and park sphere'. Fifth, at the time it was first made, Yowol-pavilion Garden was borderless gardens consisting of mountains and water taking a method of occupying a specific space of nearby nature centering around pavilion by embracing landscape viewed from the pavilion, but interpreted current complex landscapes are identified to be entirely different from landscapes of the original due to 'Different Changes', 'Fragmentation' and 'Apart piece' in many parts. Lastly, considering that Yowol-pavilion Garden belongs to the Cultural Properties Protection Zone, though not the restoration to the landscapes of the original described in tablet and literature record, at least taking a measure from the aspect of land use for minimizing adverse effect on landscape and visual damage is required.

• Economic and Environmental Geology
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• v.8 no.2
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• pp.73-87
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• 1975

Regional unconformities have been used as boundaries of major stratigraphic units in Korea. The term "synthem" has already been propsed for formal unconformity-bounded stratigraphic units of maximum magnitude (ISSC, 1974). The unconformity-based classification of the strata in the cratonic area in Korea comprises in ascending order the Kyerim, $Sangw{\check{o}}n$, $Jos{\check{o}}n$, $Py{\check{o}}ngan$, Daedong, and $Ky{\check{o}}ngsang$ Synthems, and the Cenozoic Erathem. The unconformites separating them from each other are either orogenic or epeirogenic (and vertical tectonic). The sub-$Sangw{\check{o}}n$ unconformity is a non-conformity above the basement complex in Korea. The unconformities between the $Sangw{\check{o}}n$, $Jos{\check{o}}n$, and $Py{\check{o}}ngan$ Synthems are disconformities denoting late Precambrian and Paleozoic crustal quiescence in Korea. The unconformities between the $Py{\check{o}}ngan$, Daedong, and $Ky{\check{o}}ngsang$ Synthems are angular unconformities representing Mesozoic orogenies. The bounding unconformities of the $Ky{\check{o}}ngsang$ Synthem involve non-conformable parts overlying the Jurassic and late Cretaceous granitic rocks.

• Sim-seong Yeon-gu
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• v.29 no.1
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• pp.1-45
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• 2014

Mystic experience such as seeing an vision could be explained as experiencing elusive and mysterious unique existence in religious way. In depth psychology, which is based on unconsciousness like analytical psychology, this could be explained as a something that gives a meaning of life and purpose through discovering health and healing. The importance of primodial experience in depth psychology is that it can possibly discover the base of present acts. In Christian theology, symbolic mystery and truth of religious experience that appear in Christian tradition have interest on human situation. These two fields' approach methods are different, but both show common interest on unique experience which can be said properly as raw experience. Various visions appear in many parts of the Bible. Among many visions, the book of Zechariah, one of the 12 Prophets, describes rich and diverse 8 visions through chapter 1 to chapter 8. However, due to the Genre of revelation, it lacks historicity, and because of vagueness and symbolic meanings, its visions are hard to understand and interpret. Theologically, visions of Zechariah show communality of Israelites by reconstructing kingdom of Judah and church in a way of historical circumstances. Though, these visions could deliver the meaning of an ethnical aspect as reporting continuous conversation between the God and humans. Furthermore, it could mean a personal aspect of the Prophet Zechariah as reaching for a opportunity of new change. Moreover, those who read these visions could try to interpret the meanings of various images which represent meeting mysterious existences. Therefore, the Author would concentrate on the fact that 8 visions in the book of Zechariah, which has not been received much attention to neither Christians nor non-believers, develop in chiastic structure (stylistic contrast), so that tries to interpret the first, second, seventh, and the eighth visions in analytic psychology way. In visions of Zechariah, excepting the 4th vision which probably was inserted later, rest of 7 visions each shows the stage of the hours of darkness. 1st to 3rd visions represent evening, 5th vision represents deep in the night, and 6th to 8th visions represent dawn to morning. Moreover, since structure of visions arranged in chiastic way, horse appears in 1st and 8th vision, measuring rope and measure tools are used as main motif in 2nd and 7th vision. However, same motifs could have different symbolic meanings and roles as visions are formed in different situations and conditions. In the first vision, angels who ride horses look around the world and report it is calm and peaceful. Concerning the political situation back in the day, the world being calm and peaceful in the beginning of evening means that it is not ready to change to a whole new world. Psychologically, if there is no readiness to adopt new world, it means being hopeless. It is sending you a message to get out of those kinds of situation. Moreover, appearance of four angels who rode red, brown, and white horses to a myrtus tree in the valley means that it is time for individuation and it is right and good timing for changing. In second vision, you will be able to see that Israelites had long years being caught in the shadows by foreign country, and long years succumbed by the strength of four horns, which shows the progress of renewing strength and being oneness with oneself from overwhelmed situation by paternity. In seventh vision, meaning of two women bringing the godness of the sky, who were locked up in a rice basket, back to the temple in Babylon is going towards in a level of Self-actualization by separating one's ego captured excessively by matherhood and putting back to a place where it was before. In eighth vision, chariots pulled by horses are scattered far and wide, and horses which went to north had rest in the land of North. After horses and chariots are seen between two mountains of bronze with the image of Self and anima/animus. These images can be explained as the changing progress are almost completed and the God and human, in other words Self and ego are being united and is now time for rest. All of 8 visions contains the conversation between angel and Zechariah who willing to know the meaning of visions. Zechariah asks the angel actively about the meaning of visions because of his wish for Israelites to return home and rebuild church. Conversation among the God, Zechariah, who asks questions until he knows everything, an Angel, who gives answer to given questions, is conversation between ego and anima/animus. Eventually, it is a conversation between Self and ego.