• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.681-694
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• 2018

This paper studied the effects of risk cognition of personal information and self-expression information on conation of privacy protection. In the first study, 88 college students who volunteered for this research were surveyed about risk cognition of personal information and conation to protect it. In the second study, after an information-seeking expert collected and organized the self-expression information that 88 volunteers had expressed on SNS, and then showed the organized self-expression information to 88 volunteers, and then 88 volunteers were surveyed about risk cognition of self-expression information and conation to protect it. As results of the first data analysis, the risk cognition of personal information had the greatest influence on non-disclosure of personal information, followed by reduction of the disclosure scope and law institutionalization requirement. As results of the second data analysis, SNS users openly expressed their opinion or life-style, but when they realized that self-expression information can be accumulated and become sensitive information, they had conation to protect their self-expression information such as non-disclosure, reduction of disclosure scope, and law institutionalization requirement. The implication of this study is that we have overcome the limitations of existing researches that can not explain information protection behavior on SNS.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.649-658
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• 2005

Since the Network based attack Is extensive in the real state of damage, It is very important to detect intrusion quickly at the beginning. But the intrusion detection using supervised learning needs either the preprocessing enormous data or the manager's analysis. Also it has two difficulties to detect abnormal traffic that the manager's analysis might be incorrect and would miss the real time detection. In this paper, we propose a traffic attributes correlation analysis mechanism based on self-organizing maps(SOM) for the real-time intrusion detection. The proposed mechanism has three steps. First, with unsupervised learning build a map cluster composed of similar traffic. Second, label each map cluster to divide the map into normal traffic and abnormal traffic. In this step there is a rule which is created through the correlation analysis with SOM. At last, the mechanism would the process real-time detecting and updating gradually. During a lot of experiments the proposed mechanism has good performance in real-time intrusion to combine of unsupervised learning and supervised learning than that of supervised learning.

• Journal of The Korean Association For Science Education
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• v.31 no.6
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• pp.958-969
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• 2011

The purpose of this study is to investigate relationships among scientific self-efficacy, achievement in science and cognitive self-regulation learning strategy. The subjects were composed of 158 elementary school students. Data of students' self-efficacy related to science and cognitive self-regulation learning strategy measured by questionnaire were analyzed. Science achievement scores were also collected. The results indicated that self-efficacy and cognitive self-regulation learning strategy predicted science scores. The findings showed that cognitive selfregulation learning strategy mediated the relation between self-efficacy and achievement in science. Memory learning strategy, considered a cognitive self-regulation learning strategy, did not mediate the relation between self-efficacy and science scores. The implications of science education to develop students' science achievement in the classroom and the suggestions for future researchers are discussed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.260-267
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• 2005

자기조직화지도(Self Organizing Map, SOM)는 비지도 신경망으로서 고차원의 입력공간을 위상적관계를 유지시키면서 저차원으로 사영 시킬 수 있는 특징을 갖고 있다. SOM은 패턴인 식과 자료압축/재생 등 여러 분야에서 유용하게 활용될 수 있으며 특히 고차원 자료의 시각화 방법으로 많은 관심을 받고 있다. 본 연구에서는 SOM의 quantization error를 줄이기 위한 목적으로 인공데이터를 생성시켜 학습에 이용하는 방법을 제시한다. 이는 특히 데이터가 부족한 상황에서 SOM을 학습시켜야 할 때 유용하게 적용될 수 있을 것으로 기대된다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.180-188
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• 1995

A modified Kohonen's self-organizing feature map (SOFM) algorithm which has binary reinforcement function and a constant learning rate is proposed. In contrast to the time-varing adaptaion gain of the original Kohonen's SOFM algorithm, the proposed algorithm uses a constant adaptation gain, and adds a binary reinforcement function in order to compensate for the lowered learning ability of SOFM due to the constant learning rate. Since the proposed algorithm does not have the complicated multiplication, it's digital hardware implementation is much easier than that of the original SOFM.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.382.1-382
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• 2002

This paper presents a new approach integrated Case-Based Reasoning with Self- Organization Map(SOM) in diagnosis systems. The causes of faults are obtained by case-base trained from SOM. When the vibration problem of rotating machinery occurs, this provides an exact diagnosis method that shows the fault cause of vibration problem. In order to verify the performance of algorithm, we applied it to diagnose the fault cause of the electric motor.

• 월간 기계설비
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• no.11 s.208
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• pp.107-110
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• 2007

인간의 성공과 행복은 스스로 어떻게 하느냐에 달려있다. 끈기와 집념, 성실과 근면, 정직과 선의, 박애와 상조, 시간관리와 끊임없는 자기계발 등. 다시 말해 스스로 행복해지고 성공할 수 있다는 신념이 인간의 행복과 성공을 만든다. 어느 시대를 막론하고 사람들 중 일부는 국가나 제도의 힘을 빌려 행복과 성공을 보장 받으려 할 뿐 스스로 성취하려 하지 않는 경향이 있다. 지난 4월호부터 이런 낡은 정신에서 벗어나 보다 고양된 자세를 통해 삶을 깊이 있게 다듬어 나간 사람들의 이야기를 싣는다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.250-251
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• 2015

본 논문에서는 자기 조직화 기능을 갖는 Kohonen의 SOM(Self organization map) 신경회로망과 주어지는 데이터에 따라 초기의 클러스터 개수를 설정하여 처리하는 수정된 K-Means 알고리즘을 결합한 Hybrid Kohonen Network 를 제안한다. 또한, 실제의 항공영상에 적용하여 고전적인 K-Means 알고리즘 및 고전적인 SOM 알고리즘보다 우수함을 보인다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.465-474
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• 2001

미래의 시스템은 보다 동적이고 복잡한 환경에서 작동될 것으로 예측된다. 이러한 환경에서는 학습, 적응, 진화, 퍼지, 추론, 계획, 보안, 자기 조직화, 감성 등 소위 지능적 능력들이 필수적으로 요청된다. 본 논문에서는 생명체 패러다임 SAL(System As a Life)을 제안한다. SAL은 생명체 고유의 창발적 속성에 기반을 둔 시스템 설계 방법론으로 객체 패러다임을 확장한 구조를 갖는다. SAL 기반으로 시스템을 설계할 경우 상기의 지능적 능력들이 자연스럽게 구현될 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.283-284
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• 2009

본 논문은 단일 카메라 환경에서 손 형상을 입력정보로 사용하여 손 영역만을 분할한 후 자기 조직화 특징 지도(SOFM: Self Organized Feature Map) 신경망 알고리즘을 이용하여 손 형상을 인식함으로서 수화인식을 위한 보다 안정적이며 강인한 인식 시스템을 구현하고자 한다.