• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.80-85
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• 2016

Pat Metheny debuted in 1976 and attracted the public beyond his genre in jazz by committing experimental and various music. His 50 albums have been recognized with musicality and artistry by winning 20 Grammy Awards and being awarded a Gold Disc 3 times. Therefore, his music cannot be expressed merely with the single word 'jazz'. In basic harmonics, there are distinct chords between major chord and minor chord. For example, Ionian Scale and Lydian Scale are used in major chord and Dorian Scale, Phrygian Scale, and Aeolian Scale are used in minor chord. It is also common to use m7b5 chord in Locrian Scale. However, after analyzing Pat Metheny's scale, he made Lick using Dorian Scale in major chord or using Lydian Scale in minor chord. In Dominant chord that can use various six scales, he often used Dorian Scale or Lydian Scale surprisingly. In some measures, he made Lick using Ionian Scale and Lydian Scale. In this case, since the whole atmosphere of that measure simultaneously expresses both the bright feeling of major and the lonely feeling of minor, it arouses a very special atmosphere. In addition, he brought 12-measure pattern and made Lick not using blues scale but often using Ionian Scale, Lydian Scale, and Mixo-Lydian Scale.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.431-440
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• 2017

The 2016 Gyeongju Earthquake ($M_L$ 5.8) (occurred on September 12, 2016) and the 2017 Pohang Earthquake ($M_L$ 5.4) (occurred on November 15, 2017) caused unprecedented damages in South Korea. It is necessary to establish basic data related to earthquake-induced mountainous sediment-related disasters over worldwide. In this study, we analyzed previous international studies on the earthquake-induced mountainous sediment-related disasters, then classified research areas according to research themes using text-mining and co-word analysis in VOSviewer program, and finally examined spatio-temporal research trends by research area. The result showed that the related-researches have been rapidly increased since 2005, which seems to be affected by recent large-scale earthquakes occurred in China, Taiwan and Japan. In addition, the research area related to mountainous sediment-related disasters induced by earthquakes was classified into four subjects: (i) mechanisms of disaster occurrence; (ii) rainfall parameters controlling disaster occurrence; (iii) prediction of potential disaster area using aerial and satellite photographs; and (iv) disaster risk mapping through the modeling of disaster occurrence. These research areas are considered to have a strong correlation with each other. On the threshold year (i.e., 2012-2013), when cumulative number of research papers was reached 50% of total research papers published since 1987, proportions per unit year of all research areas should increase. Especially, the proportion of the research areas related to prediction of potential disaster area using aerial and satellite photographs is highly increased compared to other three research areas. These trends are responsible for the rapidly increasing research papers with study sites in China, and the research papers examined in Taiwan, Japan, and the United States have also contributed to increases in all research areas. The results are could be used as basic data to present future research direction related to mountainous sediment-related disasters induced by earthquakes in South Korea.

• (The) Research of the performance art and culture
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• no.23
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• pp.37-61
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• 2011

The Noh(能) performance is a traditional drama that represents Japan. The Noh performance was approved in the background of religious thought such as Shintoism(神道), Buddhisms(佛敎), and Syugendo(修驗道). Especially, the influence from Shugendo is large. Shugendo was active in the Middle Ages. Especially, the influence from Shugendo is large. Shugendo was active in the Middle Ages. The Noh was approved while receiving a large influence from Shugendo. It can know the feature of the Shugen(修驗) culture in the Middle Ages through the consideration of . Moreover, the appearance of the training of 'Yamabusi(山伏)' can be seen. "Yamabusi" has not been paid to attention up to now in the research of . And, the focus was appropriated to Yamabusi and it researched in this text. Moreover, the problem of "Chigo(稚子)" is thought through . "Chigo culture" was general in the Middle Ages. It is thought that "Chigo culture" is reflected in . is an Noh performance for the boy named 'Wakamatsu' to enter the mountain and to train. It is because mother's sickness was cured. However, the boy gets sick while it is training. It was dropped to the valley according to the law of Shugendo, and it died. However, it revives by the Yamabusi's prayers. 'Taniko' is to drop to the valley and to bury it when the Yamabusi gets sick while lived. The title of the Noh originated in here. has elements of history, content of training of Shugendo, "Filial piety", and the Chigo culture, etc. These are features of the culture in the Middle Ages. It is not only a sad content though this is a content of the cruel remainder. It is because of the revival though waited rapidly at the end. As for the difficulty of training is drawn in the round, and the appearance of the training at that time is understood well. The essence of Shugendo is to train in the mountain. Supernatural power can be obtained through training. Moreover, it was thought that it was able to be newly reborn through training. The leading part of Shugendo is an Yamabusi. The Yamabusi took an active part in not only the mountain but also the village. The Yamabusi is ordinary people's lives and because the relation is deep, an important factor it knows the folk customs of Japan. The word 'Chigo' is not written in . However, a spectator at that time is 'Chigo' Wakamatsu and is already sure to have understood 'Chigo'. Because everyone knew the Chigo culture in the Middle Ages. A religion at that time and knowledge of the society are necessary to understand the play of Nho well.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• The Korean Journal of Nuclear Medicine
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• v.34 no.1
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• pp.10-21
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• 2000

Purpose: Episodic memory is described as an 'autobiographical' memory responsible for storing a record of the events in our lives. We performed functional brain activation study using ${H_2}^{15}O$ PET to reveal the neural basis of the encoding and the retrieval of episodic memory in human normal volunteers. Materials and Methods: Four repeated ${H_2}^{15}O$ PET scans with two reference and two activation tasks were performed on 6 normal volunteers to activate brain areas engaged in encoding and retrieval with verbal materials. Images from the same subject were spatially registered and normalized using linear and nonlinear transformation. Using the means and variances for every condition which were adjusted with analysis of covariance, t-statistic analysis were performed voxel-wise. Results: Encoding of episodic memory activated the opercular and triangular parts of left inferior frontal gyrus, right prefrontal cortex, medial frontal area, cingulate gyrus, posterior middle and inferior temporal gyri, and cerebellum, and both primary visual and visual association areas. Retrieval of episodic memory activated the triangular part of left inferior frontal gyrus and inferior temporal gyrus, right prefrontal cortex and medial temporal area, and both cerebellum and primary visual and visual association areas. The activations in the opercular part of left inferior frontal gyrus and the right prefrontal cortex meant the essential role of these areas in the encoding and retrieval of episodic memory. Conclusion: We could localize the neural basis of the encoding and retrieval of episodic memory using ${H_2}^{15}O$ PET, which was partly consistent with the hypothesis of hemispheric encoding/retrieval asymmetry.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.