• The Journal of the Petrological Society of Korea
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• v.22 no.3
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• pp.219-233
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• 2013

Spherulitic rhyolite occur as part of ring dyke which showing a vertical flowage of $60^{\circ}{\sim}90^{\circ}$, of the Jangsan cauldron was studied. The spherulites range in diameter from a few millimeters to 2.8 centimeters or more, and average 5~10 millimeters. It belongs to radiated simple spherulite type. They consist of a core of moderate brown dense material encased by a thin crust, a few millimeters thick at most of white grey material. The spherulites frequently have a radiating fibrous structure, which are thought to have formed as a consequence of rapid mineral growth caused by very fast cooling of the dykes in shallow depth near the surface. EPMA examination of the concentric-zoned core of spherulites show that they are mainly composed of cryptocrystalline-fibrous intergrowth of silica minerals and alkali feldspars which have $SiO_2$ 82% or more, $Al_2O_3$ 7~10%, $Na_2O+K_2O$ less than 8%. The feldspar compositions of the spherulites lie essentially within the sanidine field. XRD examination show that spherulites are mainly composed of quartz, sanidine, albite with minor mica, kaolinite and chlorite. According to X-ray mapping, the spherulites are enriched in $SiO_2$ in the core and partly enriched $Na_2O$ or $K_2O$, $Al_2O_3$ in the shell that reflect in compositional zoning with increasing spherulitic devitrification. The feathery and non-equant crystal shapes of spherulites from rhyolite dyke of Jangsan cauldron suggest that they may have formed during the rapid cooling of dyke under the static state, or faster velocity of devitrification from glassy materials than movement velocity of the magma intrusion. The spherulitic rhyolite originated from high-silica(75.4~75.7 wt.%) rhyolite magma.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of Korean Philosophical Society
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• no.123
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• pp.137-185
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• 2018

This article aims to deconstruct the mechanism of male domination that constantly reproduces the hegemonic class of men. In order to overcome misogyny, we should no longer deny the ontological dimension of the reality of women's oppressions and the pre-eminence of the material condition of women's existence. In addition, the possibility of the category of women as a modality of resistance should be taken into consideration. First, I will highlight the correlation between penis and phallus according to which the phallus refers to the penis which is malleable and fragile and which disappears without being castrated by the external factor. From here we could deduce the fragility and imperfection, the non-absoluteness of the phallic order. Secondly, I will analyze the mechanism of penis-narcissism, which is the modality of the constitution of the individual identity of man. The penis is not only a physiological organ, but a site of self-estimation and the validity of the succession of power and authority of the father's law. With this penis-narcissism, man is constituted as a hegemonic body that can let itself go without worrying about the reactions of others. Thirdly, I will focus on the mechanism of the penis-cartel which is the modality of the formation of the collective identity. The penis-cartel is reinforced by the mutual affirmation of the superiority of men among themselves, but also by the permission and the tacit agreement of their absurdity and lack of rationality and corruption. Because the privilege of men is not monopolized by a small part of the elite, but is consciously and unconsciously shared by all men who are part of the hegemonic and collective category. In order to deconstruct the penis-narcissism and the penis-cartel, it is necessary to demonstrate that the penis is not a self-sufficient body, nor a closed and impermeable body, but that it is a porous body where the organ serves both ejaculation and urinary ejection. The penis is a porous body that is at once the site of sublimity and degradation, purity and impurity. In addition, the penis is no longer an all-powerful and aggressive organ, but it is a malleable and fluid flesh that constantly changes its shape. Linked to a phallus-organ that is the notion of Jacques-Alain Miller, it is a site of deficiency and vulnerability that is not the axis of the penis-cartel. It is through the notion of the double porosity of the penis and the phenomenology of the flesh of the penis, I try to provide the modality of undoing the reproductive mechanism of predatory masculinity. Because this would be an effective strategy to overcome misogyny.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of Life Science
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• v.26 no.11
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• pp.1313-1319
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• 2016

Cancer is a complex disease heterogeneously composed of various types of cells including cancer stem-like cells responsible for relapse and chemoresistance in the tumor microenvironment. The conventional two-dimensional cell culture-based platform has critical limitations for representing the heterogeneity of cancer cells in the three-dimensional tumor niche in vivo. To overcome this insufficiency, three-dimensional cell culture methods in a scaffold-dependent or -free physical environment have been developed. In this study, we improved and simplified the HCT-8 colon cancer cell-based spheroid culture protocol and evaluated the relationship between cancer stemness and responses of chemosensitivity to 5- Fluorouracil (5-FU), a representative anticancer agent against colon cancer. Supplementation with defined growth factors in the medium and the culture dish of the regular surface with low attachment were required for the formation of constant-sized spheroids containing $CD44^+$ and $CD133^+$ colon cancer stem cells. The chemo-sensitivities of $CD44^+$ cancer stem cells in the spheroids were much lower than those of $CD44^-$ non-stem-like cancer cells, indicating that the chemoresistance to 5-FU is due to the stemness of colon cancer cells. Taken together, the inflammation and oncogenic gut environment-sensitive HCT-8 cell-based colon cancer spheroid culture and comparative evaluation using the simplified model would be an efficient and applicable way to estimate colon cancer stemness and pharmaceutical response to anticancer drugs in the realistic tumor niche.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1149-1154
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• 2003

Due to various requirements for the user access control to large databases in the hospitals and the banks, database security has been emphasized. There are many security models for database systems using wide variety of policy-based access control methods. However, they are not functionally enough to meet the requirements for the complicated and various types of access control. In this paper, we propose a database security system that can individually control user access to data groups of various sites and is suitable for the situation where the user's access privilege to arbitrary data is changed frequently. Data group(s) in different sixes d is defined by the table name(s), attribute(s) and/or record key(s), and the access privilege is defined by security levels, roles and polices. The proposed system operates in two phases. The first phase is composed of a modified MAC (Mandatory Access Control) model and RBAC (Role-Based Access Control) model. A user can access any data that has lower or equal security levels, and that is accessible by the roles to which the user is assigned. All types of access mode are controlled in this phase. In the second phase, a modified DAC(Discretionary Access Control) model is applied to re-control the 'read' mode by filtering out the non-accessible data from the result obtained at the first phase. For this purpose, we also defined the user group s that can be characterized by security levels, roles or any partition of users. The policies represented in the form of Block(s, d, r) were also defined and used to control access to any data or data group(s) that is not permitted in 'read ' mode. With this proposed security system, more complicated 'read' access to various data sizes for individual users can be flexibly controlled, while other access mode can be controlled as usual. An implementation example for a database system that manages specimen and clinical information is presented.

• Korean Journal of Psychosomatic Medicine
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• v.27 no.2
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• pp.101-110
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• 2019

Objectives : It is not clear which clinical variables are most closely associated with delirium in the Intensive Care Unit (ICU). By comparing clinical data of ICU delirium and non-delirium patients, we sought to identify variables that most effectively differentiate delirium from non-delirium. Methods : Medical records of 6,386 ICU patients were reviewed. Random Subset Feature Selection and Principal Component Analysis were utilized to select a set of clinical variables with the highest discriminatory capacity. Statistical analyses were employed to determine the separation capacity of two models-one using just the selected few clinical variables and the other using all clinical variables associated with delirium. Results : There was a significant difference between delirium and non-delirium individuals across 32 clinical variables. Richmond Agitation Sedation Scale (RASS), urinary catheterization, vascular catheterization, Hamilton Anxiety Rating Scale (HAM-A), Blood urea nitrogen, and Acute Physiology and Chronic Health Examination II most effectively differentiated delirium from non-delirium. Multivariable logistic regression analysis showed that, with the exception of vascular catheterization, these clinical variables were independent risk factors associated with delirium. Separation capacity of the logistic regression model using just 6 clinical variables was measured with Receiver Operating Characteristic curve, with Area Under the Curve (AUC) of 0.818. Same analyses were performed using all 32 clinical variables;the AUC was 0.881, denoting a very high separation capacity. Conclusions : The six aforementioned variables most effectively separate delirium from non-delirium. This highlights the importance of close monitoring of patients who received invasive medical procedures and were rated with very low RASS and HAM-A scores.

• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.51-56
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• 2003

The structuring of Web information supports a strong user side viewpoint that a user wants his/her own needs on snooping a specific Web site. Not only the depth first algorithm or the breadth-first algorithm, but also the Web information is abstracted to a hierarchical structure. A prototype system is suggested in order to visualize and to represent a semantic significance. As a motivating example, the Web test site is suggested and analyzed with respect to several keywords. As a future research, the Web site model should be extended to the whole WWW and an accurate assessment function needs to be devised by which several suggested models should be evaluated.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.