• Korean Journal of Adult Nursing
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• v.14 no.3
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• pp.359-367
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• 2002

Purpose: The purpose of this study was to measure meaning in life for a normal adult and to offer basic data for developing nursing strategies for improving the meaning in life of adults. Method: There were 351 subjects, from 20 to 60 years old, staying in Busan and KyungNam Province. The data was collected from August to October 2000. The instrument was designed by researchers. The data were analyzed with frequency, mean(${\pm}SD$), t test & ANOVA. Result: 1. The mean MIL for adult was 187.7(${\pm}18.1$). 2. The mean MIL of basic premise phase for discovery of meaning was 30.5, method phase for discovery of meaning was 111.1, and result phase for discovery of meaning was 46.4. 3. The levels of MIL, 56.4% of the subjects were in pursuit of meaning in life, 23.3% felt a loss of meaning in life. 4. There were significant differences in MIL according to the level of education(P=0.00), marital status(P=0.01) and monthly income (P=0.00). Conclusion: On the viewpoint of the above results, 23.3% of the subjects were in a level of existential vaccum which required keens Logotherapy. Therefore, it shows that developing nursing-logotherapy to improve the meaning in life for adults is demanded.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.5
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• pp.179-186
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• 2014

Current networks such as social network, web page link, traffic network are big data which have the large numbers of nodes and edges. Many applications such as social network services and navigation systems use these networks. Since big networks are not fit into the memory, existing in-memory based analysis techniques cannot provide high performance. Frontier-Expansion-Merge (FEM) framework for graph search operations using three corresponding operators in the relational database (RDB) context. FEM exploits an index table that stores pre-computed partial paths for efficient shortest path discovery. However, the index table of FEM has low hit ratio because the indices are determined by distances of indices rather than the possibility of containing a shortest path. In this paper, we propose an method that construct index table using high degree nodes having high hit ratio for efficient shortest path discovery. We experimentally verify that our index technique can support shortest path discovery efficiently in real-world datasets.

• Mass Spectrometry Letters
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• v.12 no.1
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• pp.16-20
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• 2021

We aimed to compare the content of ginsenosides and the pharmacokinetics after the oral administration of four different ginseng products at a dose of 1 g/kg in rats. The four different ginseng products were fresh ginseng extract, red ginseng extract, white ginseng extract, and saponin enriched white ginseng extract prepared from the radix of Panax ginseng C.A. Meyer. The ginsenoside concentrations in the ginseng product and the rat plasma samples were determined using a liquid chromatography-tandem mass spectrometry (LC-MS/MS). Eight or nine ginsenosides of the 15 tested ginsenosides were detected; however, the content and total ginsenosides varied depending on the preparation method. Moreover, the content of triglycosylated ginsenosides was higher than that of diglycosylated ginsenosides, and deglycosylated ginsenosides were not present in any preparation. After the single oral administrations of four different ginseng products in rats, only four ginsenosides, such as 20(S)-ginsenosides Rb1 (GRb1), GRb2, GRc, and GRd, were detected in the rat plasma samples among the 15 ginsenosides tested. The plasma concentrations of GRb1, GRb2, GRc, and GRd were different depends on the preparation method but pharmacokinetic features of the four ginseng products were similar. In conclusion, a good correlation between the area under the concentration curve and the content of GRb1, GRb2, and GRc, but not GRd, in the ginseng products was identified and it might be the result of their higher content and intestinal biotransformation of the ginseng product.

• Journal of Educational Research in Mathematics
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• v.8 no.1
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• pp.351-364
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• 1998

This thesis analyzes the nature of proof in the perspective on the philosophy of mathematics. such as absolutism, quasi-empiricism and social constructivism. And this thesis searches for the improvement of teaching proof in the light of the result of those analyses of the nature of proof. Though the analyses of the nature of proof in the perspective on the philosophy of mathematics, it is revealed that proof is a dynamic reasoning process unifying the way of analytical thought and the way of synthetical thought, and plays remarkably important roles such as justification, discovery and conviction. Hence we should teach proof as a dynamic reasoning process unifying the way of analytic thought and the way of synthetic thought, avoiding the mistake of dealing with proof as a unilaterally synthetic method. At the same time, we should make students have the needs of proof in a natural way by providing them with the contexts of both justification and discovery simultaneously. Finally, we should introduce the aspect of proof that can be represented as conviction, understanding, explanation and communication to school mathematics.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.101-103
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• 1999

Decentralized 정보는 여러 도메인에 대한 heterogeneous한 독립적인 정보가 자율적으로 존재하며 이들 정보간의 관계성의 고려한 전체에 대한 global view가 존재하지 않기 때문에 inter-domain에 대한 마이닝을 수행하는데 어려움이 있다. 본 연구에서는 intra-domain knowledge discovery, intra 및 inter-domain problem solving method라는 접근방법으로, decentralized 데이터 환경에서 문제 해결에 필요한 정보 추출을 위한 데이터 tailoring과 분산 데이터에 대한 목표-지향 데이터마이닝(goal-oriented data-mining)을 통해 문제 해결을 위해 필요한 지식을 생성하고 이들 간의 관련 정보를 탐색하여 문제를 해결하는 프레임워크를 제안한다. 특히, 생성된 지식간의 협동 문제 처리를 멀티 에이전트 패러다임을 이용하기로 한다. 제안 프레임워크는 산재되어 있는 데이터들로부터 문제 해결에 유용한 지식 차원의 정보를 추출해내고 생성된 지식을 바탕으로 각 도메인 정보에 대한 개별적인 사용뿐 만 아니라 서로 cooperation을 통한 문제 해결을 지원함으로써, 개방된 분산 환경하에 decentralized 되어 있는 여러 도메인 정보를 보다 효율적으로 활용할 수 있는 새로운 형태의 문제 해결 방법이라고 할 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2029-2032
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• 2004

The quantity of data were rapidly increased recently and caused the data overwhelming. This led to be difficult in searching the required data. The method of eliminating redundant data was needed. One of the efficient methods was Knowledge Discovery in Database (KDD). Generally data can be separate into 2 cases, continuous data and discrete data. This paper describes algorithm that transforms continuous attributes into discrete ones. We present an Improved Class Attribute Interdependence Maximization (ICAIM), which designed to work with supervised data, for discretized process. The algorithm does not require user to predefine the number of intervals. ICAIM improved CAIM by using significant test to determine which interval should be merged to one interval. Our goal is to generate a minimal number of discrete intervals and improve accuracy for classified class. We used iris plant dataset (IRIS) to test this algorithm compare with CAIM algorithm.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2561-2564
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• 2003

Data mining is an effective method of the discovery of useful information such as rules and previously unknown patterns existing in large databases. The discovery of association rules is an important data mining problem. We have developed a new parallel mining called Distributed Frequent Pattern Tree (abbreviated by DFPT) algorithm on a distributed shared nothing parallel system to detect association rules. DFPT algorithm is devised for parallel execution of the FP-growth algorithm. It needs only two full disk data scanning of the database by eliminating the need for generating the candidate items. We have achieved good workload balancing throughout the mining process by distributing the work equally to all processors. We implemented the algorithm on a PC cluster system, and observed that the algorithm outperformed the Improved Count Distribution scheme.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1994.04a
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• pp.41-49
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• 1994

The most common conventional method of discovering a drug involves a massive screening of a large number of compounds in chemical libraries or in the extracts from natural sources such as plants or microbial broths followed by chemical modification of one or more active compounds to improve their properties as a drug. When the three-dimensional structure of the target molecule for which the drug is searched is known the drug discovery process can be significantly simplified, This is especially true when the three-dimensional structure of a complex between the target and a lead compound is known. In this lecture our experience on the structure-based drug design for human CDK2(cyclin-dependent protein kinase 2) will be discussed with special emphasis on the strength and weakness of this approach of drug discovery. The regulation of the activity of CDK2 plays an important role in the cell proliferation of normal and cancer cells.

• International Journal of Contents
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• v.12 no.4
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• pp.8-16
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• 2016

This research proposes the composition logic of an Active Learning Environment (ALE), to enable discovery by learning through experience, whilst increasing knowledge about modern architectural heritage. Linking information to the historical heritage using Information and Communication Technology (ICT) helps to overcome the limits of previous learning methods, by providing rich learning resources on site. Existing field trips of cultural heritages are created to impart limited experience content from web resources, or receive content at a specific place through humanities Geographic Information System (GIS). Therefore, on the basis of the blended space theory, an augmented space experience method for overcoming these shortages was composed. An ALE space framework is proposed to enable discovery through learning in an expanded space. The operation of ALE space is needed to create full coordination, such as a Content Management System (CMS). It involves a relation network to provide knowledge to the rule engine of the CMS. The application is represented with the Deoksugung Palace Seokjojeon hall example, by describing a user experience scenario.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.155-159
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• 2003

In order to compansate the problem of fragmentating data and disdaining small group of data in decision trees, a descriptive rule set discovery method is suggested. The principle of association rule finding algorithm is presented and a modified association nile finding algorithm for efficiency is applied to target database which has condition and decision attributes to see the effect of modification.