• Proceedings of KHEA Conference
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• 2003.10a
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• pp.158-158
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• 2003

• 한국사회복지학회:학술대회논문집
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• 2007.10a
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• pp.327-332
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• 2007

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.171-174
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• 2019

군 내 복무부적응 용사들로 인한 병영사고는 대군 신뢰도 저하, 비전투력 손실로 인한 전투력 약화 등의 문제와 더불어 병영기피와 군에 대한 거부감까지 초래하고 있다. 군은 복무부적응 용사들을 사전에 식별하고 사고를 예방하기 위한 심리검사체계로 신인성검사를 자체 개발하여 적용하고 있지만, 이는 피검자의 의도적인 결과 왜곡이나 검사 결과에 대한 맹신 등의 문제점이 있다. 본 논문에서는 복무부적응 용사들의 행동 양식 및 증상들을 온톨로지로 설계하여 사고 우려자 식별을 위한 보조 도구로 활용할 수 있도록 온톨로지 구현 프로세스를 제안한다. 복무부적응 용사 식별을 위한 온톨로지 구현을 통해 사고 우려자를 식별하는데 객관적인 기준 제공 및 정보 공유로 사고 예방에 효과적인 역할을 할 것으로 기대된다.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.428-430
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• 2001

다수의 자율이동로봇으로 구성되는 다개체 시스템에서의 협조행동을 위해서 각 개체는 주변환경의 인식뿐만 아니라 환경변화에 적응할 수 있는 추론능력이 요구된다. 이에 본 논문에서는 강화학습을 이용하여 동적으로 변화하는 환경 하에서 개체들이 스스로 학습하고 대처할 수 있는 협조행동 방법을 제시한다. 제안한 방법을 먹이와 포식자 문제에 적용하여 포식자 로봇간의 협조행동을 구현하였다. 여러 대로 구성된 포식자 로봇은 회피가 목적인 먹이로봇을 추적하여 포획하는 것이 임무이며 포식자 로봇들 간의 협조행동을 위해 각 상태에 따른 최적의 행동방식을 찾는데 강화학습을 이용한다.

• Annual Conference of KIPS
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• 2012.11a
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• pp.1166-1169
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• 2012

본 논문은 사용자 행동인식을 위해 기존 PSO (Particle Swarm Optimization) 알고리즘의 경계선을 통한 데이터 분류에서 데이터의 수집환경에 의해 발생하는 문제를 벡터의 길이비교를 이용한 보정을 통해 보완한 알고리즘을 제안한다. 기존의 PSO 알고리즘은 데이터 분류를 위해서 데이터의 최소, 최대값을 이용하여 경계를 생성하고, 이를 이용하여 데이터를 분류하였다. 그러나 PSO를 이용하여 행동인식을 할 때 행동이 수집되는 환경에 따라서 경계에 포함되지 못해 행동이 분류되지 못하는 문제가 있다. 이러한 분류의 문제를 보완하기 위해 경계를 벗어난 데이터와 각 행동을 대표하는 데이터의 벡터 길이를 계산하고 최소길이를 비교하여 분류한다. 실험결과, 기존 PSO 방법에 비해 개선된 방법이 평균적으로 앉기 1%, 걷기 7%, 서기 7%의 개선된 결과를 얻었다.

• Korean Journal of Childcare and Education
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• v.9 no.5
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• pp.229-249
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• 2013

This study aims to analyze the effects of infants' attachment to their mother and child care teacher on their adaptation to child care. Data were collected in a purposive sample involving 100 infants and toddlers at age one and two, their mothers, and 36 teachers were analyzed with two-way ANOVA. The results showed that the effects of infants' attachment to their mother and teacher varied by subfactors of adaptation. 'Social Relationships' and 'Positive Emotion' were significantly different only among infants who established insecure attachment with their mother. Second, the level of 'Social Relationships' and 'Positive Emotion' were significantly high when teacher-infant attachment was secure among infants who established insecure attachment with their mother. Third, there were significant effects of mother-infant attachment on 'Aggressive Behavior' as infants who established insecure attachment with their mother showed a significantly higher level of 'Aggressive Behavior' than infants having secure attachment with their mother.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.41-50
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• 2020

The purpose of this study was to identify latent classes in school readiness which were measured by both parents and teachers, and to test the relationship with school adaption, academic abilities, problematic behaviors, self-esteem, and happiness. Latent Profile Analysis (LPA), MANOVA, and one-way ANOVA were conducted using the data of 1,154 1st to 3rd grade elementary school students, and the data was collected between the 7th year (2014) to the 10th year (2017) from the Korean Children and Youth Panel Study. The results of study were as follows. First, four school readiness groups were identified: the consistent low group, parent perceived high group, the teacher perceived high group, the consistent high group. Second, the consistent high group and the teacher perceived high group showed relatively better academic abilities and school adaption than the consistent low group. Furthermore, the consistent high group showed higher academic abilities, self-esteem, and happiness, but lower problematic behaviors than the consistent low group. The findings of this study could be utilized as preliminary data for understanding school readiness to promote successful school adaption and psychological well-being of children.

• Korean Journal of Culture and Social Issue
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• v.20 no.4
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• pp.281-305
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• 2014

This study investigated the relationship between older driver's safe driving behavior, driving mobility(amount of driving, assessment for driving-related physical ability, confidence for adaptation in driving situation, amount of social activity) and subjective well-being(life satisfaction, positive/negative affect). The data of these variables were collected by questionnaire method based on face-to-face interview. The results can be summarized as followings. First, the older driver who reported higher scores in the self-report safe driving behavior questionnaire tended to show higher level of subjective well-being and driving-based mobility. Second, all the sub-factors in the driving-based mobility questionnaire were positively related to life satisfaction, positive affect but negatively related to negative affect except amount of driving. Finally, it was found that both confidence for adaptation in driving situation and amount of social activity positively mediated the relationship between self-report safe driving behavior and life satisfaction and positive affect, but confidence for adaptation in driving situation negatively mediated the relationship between self-report safe driving behavior and negative affect, Implication and suggestion were discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.164-170
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• 2004

This paper proposes a Distributed Autonomous Robotic System(AIS) based on Artificial Immune System(AIS) and Distributed Genetic Algorithm(DGA). The behaviors of robots in the system are divided into global behaviors and local behaviors. The global behaviors are actions to search tasks in environment. These actions are composed of two types: dispersion and aggregation. AIS decides one among above two actions, which robot should select and act on in the global. The local behaviors are actions to execute searched tasks. The robots learn the cooperative actions in these behaviors by the DGA in the local. The proposed system is more adaptive than the existing system at the viewpoint that the robots learn and adapt the changing of tasks.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.699-704
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• 2004

This paper proposes a Distributed Autonomous Robotic System(DARS) based on an Artificial Immune System(AIS) and a Classifier System(CS). The behaviors of robots in the system are divided into global behaviors and local behaviors. The global behaviors are actions to search tasks in environment. These actions are composed of two types: aggregation and dispersion. AIS decides one among these two actions, which robot should select and act on in the global. The local behaviors are actions to execute searched tasks. The robots learn the cooperative actions in these behaviors by the CS in the local. The proposed system is more adaptive than the existing system at the viewpoint that the robots learn and adapt the changing of tasks.