• 한국심리학회지:법
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• 제12권2호
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• pp.171-197
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• 2021

본 연구는 일반인이 정당방위를 어떻게 판단하고 있으며 판단시 영향을 줄 수 있는 요인들은 어떤 것이 있는지 실증적으로 고찰하고자 하였다. 정당방위 판단에 영향을 주는 요인을 정당방위 유형, 정당방위에 대한 기사, 판단자 개인 특성인 폭력 허용도, 법적 태도로 나누어 만 20세 이상의 성인 남녀 총 651명을 대상으로 연구자료를 수집 및 분석하였다. 참가자들은 정당방위가 주장되는 상황을 유형화하여 작성된 세 유형 중 하나의 유형에 할당되고, 각 유형에 해당하는 정당방위 관련 기사와 시나리오를 제공받은 후 정당방위 판단을 하였다. 또한, 개인적 요소인 법에 대해 가지는 태도, 폭력 허용도가 측정된 후 정당방위 판단에 미치는 영향을 분석하였다. 연구 결과, 자신을 위한 정당방위 유형에서는 정당방위를 인정하는 비율이 가장 높았으나, 국가기관에 대항하는 정당방위 유형에서는 정당방위를 불인정하는 비율이 훨씬 높은 반대 양상을 나타내었다. 또한, 정당방위에 대한 기사 중 정당방위가 잘 인정되지 않는다는 부정적인 기사가 정당방위 판단에 영향을 미치는 것으로 나타났다. 더불어 참가자 개인의 폭력 허용도, 법적 태도가 정당방위 판단에 영향을 미칠 수 있음을 확인하였다. 본 연구를 통해 확인한 일반인들의 정당방위 판단 과정과 정당방위 판단에 영향을 줄 수 있는 제반 요소들은 실제 배심 재판에서의 편향적인 판단을 방지하기 위한 고려 요소가 될 수 있을 것이다. 마지막으로 본 연구의 의의와 한계점, 후속 연구를 제언하였다.

• Child Health Nursing Research
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• 제14권1호
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• pp.61-70
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• 2008

Purpose: The purpose of this study was to investigate the relationship between high school students' self-differentiation, family function and their level of stress. Method: A questionnaire which consisted of questions on general characteristics of the high school students, and 36 questions on self-differentiation, 17 questions on family function, and 37 questions on level of stress was used to collect the data. Participants were 201 second grade high school students from Bucheon City. Descriptive statistics, T-test, ANOVA, correlation and multiple regression were used with SPSS 10.0 to analyze the data. Results: The mean scores for self-differentiation, family function, and levels of stress were 3.27, 3.39, and 2.61 respectively. The relationship between self-differentiation and level of stress revealed a significant negative correlation. The relationship between self-differentiation and family function showed a significant positive correlation. The relationship between family function and stress level showed a significant negative correlation. Conclusion: The results of the study show that variation in level of stress was related to family regression, recognition/emotional function, family projection, role recognition and emotional support and emotional cutoff which together explained 40.9% of the variance in level of stress.

• 대한전자공학회논문지TC
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• 제47권1호
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• pp.69-76
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• 2010

FTTH(Fiber To The Home)가입자 망에 있어 패킷단위의 정보처리를 위한 3 비트, 4 비트의 전광학 헤더인식 과정을 설명하고 실험을 성공적으로 수행하였다. 패킷의 목적지를 나타내는 3, 4 비트 헤더정보에 따른 하나의 신호가 각각 8개, 16개의 타임슬롯 상의 지정된 한 곳에 나열되었다. RN(remote node)에서의 self-routing을 위해 제안된 헤더인식 기술은 TDM 방식의 원리를 응용하여 스위치와 지연라인만을 사용함으로써 매우 간단한 회로를 구성하여 높은 신뢰도와 낮은 비용으로 가입자 망을 구축할 수 있다. 또 RN으로부터 각 가입자의 독립 전송선로를 확립함으로써 TDM-PON 방식의 최대 취약점이었던 광 신호의 감쇄와 보안성의 문제를 해결할 수 있다.

• International journal of advanced smart convergence
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• 제8권4호
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• 전자공학회논문지B
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• 제33B권2호
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• pp.168-177
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• 1996

In this paper, we propose an on-line recognition system of chinese characters using an adaptive resonance theory-1(ART-1) neural network. Strokes, primitive components of chinese characters are usually warped into a cursive form and classifying them is very difficult. To deal with such cursive strokes, we use an ART-1 neural network that has the following advantages: (1) it automatically assembles similar patterns together to form classes in a self-organized manner: (2) it directly accesses the recognition codes corresponding to binary input patterns after self-stabilizing; (3) it doesn't tends to get trapped in local minima, or globally incorrect solutions. A database for character recognition also dynamically constructed with generalized character lists, and a new character can be included simply by adding a new sequence to the list. Character recognition is achieved by traversing the chinese datbase with a sequence of recognized strokes and positional relations between the strokes. To verify the performance of the system. We tested it for 1800 daily-used basic chinese second per character. This results suggest that the proposed system is pertinent to be put into practical use.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.61-66
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• 2022

Object classification is one of the main fields in neural networks and has attracted the interest of many researchers. Although there have been vast advancements in this area, still there are many challenges that are faced even in the current era due to its inefficiency in handling large data, linguistic and dimensional complexities. Powerful hardware and software approaches in Neural Networks such as Deep Neural Networks present efficient mechanisms and contribute a lot to the field of object recognition as well as to handle time series classification. Due to the high rate of accuracy in terms of prediction rate, a neural network is often preferred in applications that require identification, segmentation, and detection based on features. Neural networks self-learning ability has revolutionized computing power and has its application in numerous fields such as powering unmanned self-driving vehicles, speech recognition, etc. In this paper, the experiment is conducted to implement a neural approach to identify numbers in different formats without human intervention. Measures are taken to improve the efficiency of the machines to classify and identify numbers. Experimental results show the importance of having training sets to achieve better recognition accuracy.

• 복식문화연구
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• 제31권6호
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• pp.705-719
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• 2023

The study aimed to investigate the factors influencing genderless fashion preferences. The questionnaires were collected from men and women participants aged 20 to 49 living in Seoul City and Gyeonggi Province. Data analysis involved factor analysis, t-test, ANOVA, and regression analysis. The findings revealed that genderless fashion preference comprised four factors, namely individuality pursuit, deviation from norms, fashion pursuit, and social recognition pursuit. Self-esteem encompassed two factors positive self-esteem, and negative self-esteem. while sociocultural attitude toward appearance consisted internalization, and awareness. Second, positive self-esteem significantly influenced individuality pursuit and deviation from norms in genderless preference factors. Third, sociocultural attitude toward appearance had a significant effect on genderless fashion preference, with awareness particularly exerting a significant effect on individuality pursuit, fashion pursuit, and social recognition pursuit. Fourth, genderless fashion preferences exhibited differences based on gender role identity in factors such as individual pursuit, norm avoidance, and trend pursuit. Lastly, demographic characteristics such as age, education, occupation, and monthly income revealed significant differences in genderless fashion preferences. From the results of the study, it was found that consumers perceived individuality pursuit, fashion pursuit, and social recognition pursuit as important influencing factors of genderless fashion preferences. In addition, it is necessary to create an independent brand identity by developing various items to express consumers' individuality, differentiated brand concepts from other brands, and store displays.

• 디지털융복합연구
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• 제11권8호
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• pp.143-157
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• 2013

본 논문은 창업과 관련하여 선행연구가 많이 이루어져 왔던 개인특성 접근법 보다는 환경론적 접근법에 의한 이론적 기반을 토대로 하여 창업의지에 미치는 영향을 규명하고자 하였다. 특히 한국인들에게 높게 나타나고 있는 사업실패부담감이 창업환경과 창업의지 간 부정적 영향을 미치는 매개변수 역할을 할 것인가에 대한 가설을 검증하였다. 이를 위해 잠재창업자인 대학생 및 일반인들을 대상으로 설문조사를 실시하였으며 총 335명의 응답을 표본을 대상으로 검증하였다. 연구결과에 의하면, 첫째로, 객관적 창업환경과 잠재 창업가의 자신감 혹은 자기효능감(self-efficacy)으로 정의되는 주관적 환경인식이 창업의지에 유의한 영향력을 미치는 것으로 나타났다. 둘째, 객관적 창업환경 및 주관적 환경인식과 창업의지 간의 관계에서 사업실패부담감이 매개변수 역할을 할 것이라는 가설에서 유의한 결과를 얻었다. 셋째로, 본 논문에서 객관적 창업환경의 설명변수로 채택한 보육제도 여건도 향후 창업의지 결정에 중요한 요인이 될 수 있다는 점이다.

• 정보처리학회논문지B
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• 제13B권4호
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• pp.439-448
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• 2006

Kohonen SOM(Self-Organizing Map)이나 MLP(Multi-Layer Perceptron), SVM(Support Vector Machine)과 같은 기존의 인식 및 클러스터링 알고리즘들은 새로운 입력 패턴에 대한 적응성이 떨어지고 학습 패턴 자체의 복잡도에 대한 학습률의 의존도가 크게 나타나는 등 여러 가지 단점이 있다. 이러한 학습 알고리즘의 단점은 문제의 학습 패턴자체의 특성을 잃지 않고 문제의 복잡도를 낮출 수 있다면 보완할 수 있다. 패턴 자체의 특성을 유지하며 복잡도를 낮추는 방법론은 여러 가지가 있으며, 본 논문에서는 커널 공간 해석 기법을 접근 방법으로 한다. 본 논문에서 제안하는 kSOM(kernel based SOM)은 원 공간의 데이터가 갖는 복잡도를 무한대에 가까운 초 고차원의 공간으로 대응시킴으로써 데이터의 분포가 원 공간의 분포에 비해 상대적으로 성긴(spase) 구조적 특정을 지니게 하여 클러스터링 및 인식률의 상승을 보장하는 메커니즘 을 제안한다. 클러스터링 및 인식률의 산출은 본 논문에서 제안한 새로운 유사성 탐색 및 갱신 기법에 근거하여 수행한다. CEDAR DB를 이용한 필기체 문자 클러스터링 및 인식 실험을 통해 기존의 SOM과 본 논문에서 제안한 kSOM과 성능을 비교한다.

• 대한지역사회영양학회지
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• 제12권3호
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• pp.247-258
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• 2007

This study was conducted to investigate weight control status and associated factors among health-related major female college students. The subjects consisted of 41 nutrition and 78 physical exercise major female college students. Nutrient intakes, biochemical index, nutrition knowledge (recognition and accuracy), interest of weight control, body satisfaction, self-recognition of health condition, self-evaluated body weight were studied. About 73% of nutrition and 79% of physical exercise major female students were in the normal range of BMI ($18.5{\sim}23$) and 2% of nutrition and 1% of physical exercise major female students were underweight, 10% of nutrition and 6% of physical exercise major female students were obese. There were no significant differences in height and weight by major but %body fat and WHR in physical exercise majors were significantly lower than nutrition major students (respectively p<0.01, p<0.05). Overall, nutrition intakes of subjects were not shown to be appropriate, especially Ca/P of subjects was shown $0.54{\sim}0.64$, fat% out of energy of subjects was shown $24.7{\sim}29.0$ and Na intake was shown above 2000mg. Recognition and accuracy of nutrition were higher than those of physical exercise majors (p<0.001). There were no significant differences in self-recognition of health condition, self-evaluated body weight, satisfaction of body shape by major and weight control attempt. But interest of weight control of attempter was higher than that of no-attempter in nutrition (p<0.05) and physical exercise major students (p<0.01). Significantly negative correlation was found in satisfaction of body and BMI, body fat mass, %bodyfat, WHR. And significantly positive correlation was found in interest of weight control and BMI, %bodyfat, WHR. It was noticeable to see that interest of weight control was positively correlated to accuracy and accuracy was negatively correlated to blood cholesterol level. Therefore, proper nutrition education for female college students is needed in order to improve their weight control-related health.