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[ $^1H$ ] MR Spectroscopy of the Normal Human Brains: Comparison between Signa and Echospeed 1.5 T System (정상 뇌의 수소 자기공명분광 소견: 1.5 T Signa와 Echospeed 자기공명영상기기에서의 비교)

  • Kang Young Hye;Lee Yoon Mi;Park Sun Won;Suh Chang Hae;Lim Myung Kwan
    • Investigative Magnetic Resonance Imaging
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    • v.8 no.2
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    • pp.79-85
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    • 2004
  • Purpose : To evaluate the usefulness and reproducibility of $^1H$ MRS in different 1.5 T MR machines with different coils to compare the SNR, scan time and the spectral patterns in different brain regions in normal volunteers. Materials and Methods : Localized $^1H$ MR spectroscopy ($^1H$ MRS) was performed in a total of 10 normal volunteers (age; 20-45 years) with spectral parameters adjusted by the autoprescan routine (PROBE package). In all volunteers, MRS was performed in a three times using conventional MRS (Signa Horizon) with 1 channel coil and upgraded MRS (Echospeed plus with EXCITE) with both 1 channel and 8 channel coil. Using these three different machines and coils, SNRs of the spectra in both phantom and volunteers and (pre)scan time of MRS were compared. Two regions of the human brain (basal ganglia and deep white matter) were examined and relative metabolite ratios (NAA/Cr, Cho/Cr, and mI/Cr ratios) were measured in all volunteers. For all spectra, a STEAM localization sequence with three-pulse CHESS $H_2O$ suppression was used, with the following acquisition parameters: TR=3.0/2.0 sec, TE=30 msec, TM=13.7 msec, SW=2500 Hz, SI=2048 pts, AVG : 64/128, and NEX=2/8 (Signa/Echospeed). Results : The SNR was about over $30\%$ higher in Echospeed machine and time for prescan and scan was almost same in different machines and coils. Reliable spectra were obtained on both MRS systems and there were no significant differences in spectral patterns and relative metabolite ratios in two brain regions (p>0.05). Conclusion : Both conventional and new MRI systems are highly reliable and reproducible for $^1H$ MR spectroscopic examinations in human brains and there are no significant differences in applications for $^1H$ MRS between two different MRI systems.

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Characteristics of Everyday Movement Represented in Steve Paxton's Works: Focused on Satisfyin' Lover, Bound, Contact at 10th & 2nd- (스티브 팩스톤(Steve Paxton)의 작품에서 나타난 일상적 움직임의 특성에 관한 연구: , , 를 중심으로)

  • KIM, Hyunhee
    • Trans-
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    • v.3
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    • pp.109-135
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    • 2017
  • The purpose of this thesis is to analyze characteristics of everyday movement showed in performances of Steve Paxton. A work of art has been realized as a special object enjoyed by high class people as high culture for a long time. Therefore, a gap between everyday life and art has been greatly existed, and the emergence of everyday elements in a work of art means that public awareness involving social change is changed. The postmodernism as the period when a boundary between art and everyday life is uncertain was a postwar society after the Second World War and a social situation that rapidly changes into a capitalistic society. Changes in this time made scholars gain access academically concepts related to everyday life, and affected artists as the spirit of the times of pluralistic postmodernism refusing totality. At the same period of the time, modern dance also faced a turning point as post-modern dance. After the Second World War, modern dance started to be evaluated as it reaches the limit, and at this juncture, headed by dancers including the Judson Dance Theatre. Acting as a dancer in a dance company of Merce Cunningham, Steve Paxton, one of founders of the Judson Dance Theatre, had a critical mind of the conditions of dance company with the social structure and the process that movement is made. This thinking is showed in early performances as an at tempt to realize everyday motion it self in performances. His early activity represented by a walking motion attracted attention as a simple motion that excludes all artful elements of existing dance performances and is possible to conduct by a person who is not a dancer. Although starting the use of everyday movement is regarded as an open characteristic of post-modern dance, advanced researches on this were rare, so this study started. In addition, studies related to Steve Paxton are skewed towards Contact Improvisation that he rose as an active practician. As the use of ordinary movement before he focused on Contact Improvisation, this study examines other attempts including Contact Improvisation as attempts after the beginning of his performances. Therefore, the study analyzes Satisfyin' Lover, Contact at 10th & 2nd and Bound that are performances of Steve Paxton, and based on this, draws everyday characteristics. In addition, related books, academic essays, dance articles and reviews are consulted to consider a concept related to everyday life and understand dance historical movement of post-modern dance. Paxton attracted attention because of his activity starting at critical approach of movement of existing modern dance. As walking of performers who are not dancers, a walking motion showed in Satisfyin' Lover gave esthetic meaning to everyday movement. After that, he was affected by Eastern ideas, so developed Contact Improvisation making a motion through energy of the natural laws. In addition, he had everyday things on his performances, and used a method to deliver various images by using mundane movement and impromptu gestures originating from relaxed body. Everyday movement of his performances represents change in awareness of performances of the art of dancing that are traditionally maintained including change of dance genre of an area. His activity with unprecedented attempt and experimentation should be highly evaluated as efforts to overcome the limit of modern dance.

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Studies on the Directivity of Gokjungkyeong(Kyung Overlapped with Gok) which was specified in Byeokgye-ri, Yangpyeong-gun and the Hwaseo Lee, Hang-ro's Management in Byeokwon Garden (양평 벽계리에 설정된 곡중경(曲中景)의 지향성과 화서(華西) 이항로(李恒老)의 벽원(蘗園) 경영)

  • Jung, Woo-Jin;Rho, Jae-Hyun
    • Journal of the Korean Institute of Traditional Landscape Architecture
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    • v.34 no.3
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    • pp.78-97
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    • 2016
  • The objectives of this study are to examine the context of the establishment of Suhoe Gugok, Byeokgye Gugok Vally, and Nosan Palkyung, which have been established in Seojong-myeon of Yangpyeong-gun, by literature review and site investigations, and to determine the sceneries of Byeokgye scenic site as enjoyed and managed during the period of Hwaseo Lee, Hang-ro(華西 李恒老). The results of the study are as follows. First, Byeokgye Gugok Vally(黃蘗九曲) and Nosan Palkyung(蘆山八景), which have been established after the period of Hwaseo and theorized to have been established around key scenic areas associated with Hwaseo's activities, the analysis results showed that they were collecting sceneries of modern times. The extensive overlap between Byeokgye Gugok Vally and concentrated scenic elements of Suhoe Gugok(水回九曲), and the artificial configuration from the end point of Suhoe Gugok to the beginning point of Nosan Palkyung, reveal the pattern of space conflict and hegemony between Byeokgyes of Suip-ri and Nomun-ri. This is likely to be caused by the conflict between the historicity of the group that enjoyed Byeokgye prior to Hwaso's period and the strong territoriality of the space filled with the image of Hwaseo. Second, Byeokgye Gugok Vally was the secondary spatial system created by selecting the most scenic sites in Suip-ri while expanding the area of Nosan Palkyung. After establishment of Byeokgye Gugok Vally, the spatial identity of the entire Byeokgyecheon area was effectively established. This was a "Hwaseo-oriented" move, including the complete exclusion of the scenic sites from the pre-Hwaseo period such as Cheongseo Gujang and Suhoe Gugok's Letters Carved on the Rock. Consequently, the entire Byeokgyecheon area was reorganized into a cultural scenic site with Heoseo's influence. Third, Fifth, creations of Gugok(九曲) to determine the lineage of the Hwaseo School from Juja(朱子) to Yulgok(栗谷) to Uam(尤庵) to Hwaseo is likely to be an opportunity of birth and external motivation of the establishment of new Gugok Palkyung. In other words, Nosan Palkyung and Byeokgye Gugok Vally are likely to have been created as a reaction to the change of the center of the Hwaseo School to Okgyedong, and with strategic orientation based on the motivation and needs such as creation of the connecting space between Mui Gugok, Gosan Gugok, and Okgye Gugok, and the elevation of Hwaseo's status. Fourth, from the Hwaseo's Li-centric point of view, all revered sites in Beokwon(蘗園) that he managed existed as the spatial creative work to experience the existence of "li" through the objects in the landscape and the boundary of the spirit of emptiness of the aesthetic self. This clearly shows how Byeokgye Gugok Vally or Nosan Palkyung must be defined, and furthermore, appreciated and approached, prior to discussing it as the space associated with Hwaseo. Fifth, Nosan Palkyung was composed of cultural scenic landscapes of Gokjungkyung(曲中景) with eight scenic sites where Hwaseo gave his teachings and spend time around, in the Byeokgye of Nomun-ri area of Byeokgye Gugok Vally. The sceneries is, however, collected by depending on Hwaseo's Letters Carved on the Rock and poetry. Consequently, an inner exuberance of Nosan Palkyung is satisfied beside Byeokgye Gugok Vally, but its conceptual adequacy leaves room for questions.

Ensemble Learning with Support Vector Machines for Bond Rating (회사채 신용등급 예측을 위한 SVM 앙상블학습)

  • Kim, Myoung-Jong
    • 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.