• 동위원소뉴스
• /
• no.12 s.72
• /
• pp.4-6
• /
• 2002

• 동위원소뉴스
• /
• no.8 s.92
• /
• pp.7-9
• /
• 2004

• 동위원소뉴스
• /
• no.9 s.93
• /
• pp.8-10
• /
• 2004

• Korean Architects
• /
• no.4 s.134
• /
• pp.2-4
• /
• 1980

• Korean Journal of Childcare and Education
• /
• v.12 no.1
• /
• pp.225-244
• /
• 2016

This study seeks to analyze how children's moral emotions affect their social competence. Moral emotions focus on empathy and sympathy. The purpose of this research is to offer basic data for establishing both amoral and emotional educational program. The subjects of this research involve 182 children(either 4 or 5 year olds) that have lived in Korea. Analysis of the collected data have yielded some interesting results. First, it is found that children's empathy and sympathy are dependent on children's age and their fathers' educational level; as a result, increasing the age of the children and their fathers' educational level are found to increase empathy and sympathy. Secondly, both empathy and sympathy scores are found to have correlation to the scores of positive reciprocity, capability, and interpersonal relation on social competence. Sympathy scores increase with respect to the leadership scores of social competence. Lastly, children's empathy is a factor that affects positive reciprocity, capability, interpersonal relation, and participation on social competence. Their sympathy affects leadership on social competence. The results of this study suggest that strengthening the empathy and sympathy levels of children could partially enhance their social competence.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.5
• /
• pp.555-565
• /
• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Applied Biological Chemistry
• /
• v.38 no.3
• /
• pp.196-201
• /
• 1995

Chemo- and numerical taxonomic studies on the isolate SL-387 producing novel aminopeptidase M inhibitors MR-387A and B were carried out The genus of the isolate was determined as Streptomyces by cultural and morphological data and chemical indices. Forty one taxonomic unit characters were tested for determining the species of the isolate, and the data were analyzed numerically using a computer program as called TAXON. The isolate was best matched to Streptomyces neyagawaensis in the major cluster 18 of Streptomyces with $S_{SM}$ value of 75.67%. On the base of chemotaxonomic data and TAXON analysis, the isolate SL-387 was identified to be a member of Streptomyces neyagawaensis.

• Korean Journal of Clinical Laboratory Science
• /
• v.49 no.4
• /
• pp.407-412
• /
• 2017

The aim of this study was to shorten the time required for subculture and bacterial identification and obtain a simple and rapid identification method for new test methods for bloodstream infections. The following results were obtained using a mass spectrometer. In Vitek 2, 208 (81.8%) cases were well-identified and 45 isolates were not identified in blood cultures. Among 208 cases, 146 (57.5%) were Gram positive bacteria and 108 (42.5%) were Gram negative bacteria. In total, 233 were identified to the species level and 21 were identified to the genus level. The identification error was found to be Propionibacterium acnes as Clostridium bifermentans. The accuracy of Enterobacteriaceae, glucose non-fermentative bacilli (GNFB), and staphylococci were 81/83 (97.6%), 12/15 (80.0%), and 72/85 (84.7%), respectively. The concordance rate of Vitek 2 and Vitek MS by the direct method was 81.8% and 45 isolates were not identified. Most of the unidentified bacteria were Gram positive bacteria (N=37). The Gram positive bacteria were streptococci (14), coagulase-negative staphylococci (CNS) (11), enterococci (3), Staphylococcus aureus (2), Micrococcus spp. (2), Bacillus spp. (2) and Actinomyces odontolyticus, Finegoldia magna, and Peptostreptococcus spp. The results reporting time was reduced to 24~72 hours compared to the conventional method. The rate of identification of the aerobic and anaerobic cultures was similar, but the use of an anaerobic culture did not require a dissolution process, which could shorten the sample preparation time. These results suggest that the method of direct identification in blood cultures is very useful for the treatment of patients. In further studies, it might be necessary to further improve the method for identifying streptococci and CNS, which were lacking in accuracy in this study.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.11a
• /
• pp.467-470
• /
• 2005

퍼지모델은 주로 경험적 방법에 의해 추출되기 때문에 보다 구체적이고 체계적인 방법에 의한 동정 및 최적화 될 필요성이 요구된다. 따라서 본 논문에서는 퍼지 모델의 전반부 및 후반부의 구조 동정과 파라미터 동정에 있어서 최적의 구조 및 파라미터를 찾기 위해 유전자 알고리즘을 이용한다. 초기 퍼지 모델을 설계하기 위해 유전자 알고리즘을 이용하여 입력 변수의 수, 선택될 입력 변수, 멤버쉽함수의 수, 그리고 후반부 형태를 결정한다. 구축된 퍼지 모델은 유전자 알고리즘에 의해 세대를 거듭하면서 전반부 파라미터를 자동 동조함으로써 최적의 퍼지 모델을 설계한다. 또한 구조 동정 및 파라미터 동정을 동시에 시행함으로서 정보 입자 기반 퍼지 모델의 유전자적 최적화를 도모한다. 마지막으로 제안된 퍼지 모델은 표준 모델로서 널리 사용되는 수치적인 예를 통하여 평가한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.03a
• /
• pp.112-115
• /
• 1998

본 논문은 퍼지 추론 시스템 모델의 최적화를 제시한다. 비선형적이고 복잡한 실시스템의 특성을 해석하는 방법으로써 시스템의 정적 혹은 동적 특성을 묘사하기 위해 퍼지 모델이 사용된다. 그러나 퍼지 시스템의 동정은 경험적 방법에 의해 규칙을 추출하기 때문에, 보다 논리적이고 체계적인 방법에 의한 추출 방법의 고찰이 필요하다. 제안된 규칙베이스 퍼지모델은 GA 및 퍼지규칙의 이론을 이용한 시스템 구조와 파라미터 동정을 시향한다. 두형태의 퍼지모델 방법은 간략추론 및 선형추론에 의해 시행된다. 본 논문에서는 퍼지 추론 시스템의 전반부 파라미터 동정을 통해 퍼지 입력공간을 정의함으로써 비선형 시스템을 표현한다. 전반부 파라미터의 동정세는 유전자 알고리즘을 사용하고, 후번부는 표준가우스 소거법을 사용하여 동정한다. 최적화는 유전자 알고리즘에 기초한 자동-동조 방법이며, 학습 및 데이터의 성능결과의 상호 균형을 얻기 위한 하중값을 가진 성능지수가 제시된다.