• 한국간호교육학회지
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• 제28권4호
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• pp.411-420
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• 2022

Purpose: The purpose of this study was to identify the factors influencing nursing students' caring efficiency. Methods: This study included 212 nursing students from three University nursing departments in Busan metropolitan city. Data were collected from September 1 to September 29, 2021, using self-report questionnaires. For data analysis, descriptive statistics, independent t-test, one-way ANOVA, Pearson's correlation coefficient, and hierarchical multiple regression analyses were conducted with SPSS version 24.0. Results: Positive psychological capital and interpersonal competence on the relationship and character of nursing students were positively correlated with caring efficiency. The variables affecting the caring efficiency of the subjects were character (β=.60, p<.001), amount of participation in a character development program (5 times or more) (β=.16, p=.023), levels of stress (moderate β=.13, p=.037; low β=.15, p=.015), motivation to apply to a nursing program (β=.12, p=.024), and volunteer experience (β=.11, p=.038). The total explanatory power of the variables was 50.0% (F=14.69, p<.001). Conclusion: Character was one of the biggest influential factors on caring efficiency. In order to improve nursing students' caring efficiency, above all, it is necessary to make efforts to raise the level of individual character.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.

• 통합자연과학논문집
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• 제14권3호
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• pp.78-86
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• 2021

Personal space is an invisible space around people and plays an important role in human communication. Individual spaces are known to change according to gender and relationships in communication between people. This study examines whether such personal space exists between characters on a metabus. Gender participants took up short personal space, and female participants found that on the Metabus, they changed their personal space according to their intimacy or gender with other people's characters rather than their own. Male participants are thought to change their personal space according to their personality in case of reason in Metabus. From this, it is thought that the participant continues to have physicality, just as the meta-bus actually has a body. Female participants found that in Metabuses, as in the real world, gender between characters has a similar short personal space, rather than gender, and intimacy between characters. In the case of male participants, it was shown that the closeness between the characters was similar to that of the personal space, but the gender of other characters did not change the personal space. Future validation of personal space for metabus characters requires comparison of shapes of individual spaces and cultures, such as individual characteristics, such as introduction and extroversion of individual space, and experiments of gender. In this experiment, the number of female experiment participants is strongly required in future experiments, as compared with male experiment participants, and the need for various cultural experiments is also required.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• 한국지능시스템학회논문지
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• 제13권4호
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• pp.474-479
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• 2003

In this paper, we proposed the novel method for the recognition of English calling cards by using the projection method and the enhanced RBF (Radial Basis Function) network. The recognition of calling cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of calling cards, and the feature areas including character strings are separated from the calling card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method, the feature areas are split into the areas of individual characters. We also proposed the enhanced RBF network that organizes the middle layer effectively by using the enhanced ART1 neural network adjusting the vigilance threshold dynamically according to the homogeneity between patterns. In the recognition phase, the proposed neural network is applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the existing neural network based recognition.

• 복식문화연구
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• 제11권3호
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• pp.340-351
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• 2003

The purpose of this study was to examine the effect of clothing type and hair style on men’s impression formation. The experimental design was 4×2×2×2 (clothing type×hair style×perceiver’s age×perceiver’s role) factorial design with between-subjects design. The stimuli of color photographs of male in his 20's model and semantic differential scale were used. The data were obtained from questionnaires completed by 881 men and women in the metropolitan area of Seoul. The SPSS package was used for data analysis which includes factor analysis, t-test, and Cronbach’s a to measure the reliability. This study showed the following results. Four factors were derived to account for the dimensions of impression formation. These were dignity, activity, individual character, and social intercourse. Men evaluated individual character factor higher than women did. Dignity factor was evaluated higher by students, while social intercourse factor was evaluated higher by office workers. The clothing type of shirts/pants was evaluated to be more active and more sociable than of jacket/pants. Men wanted to exhibit natty image and women did elegant image through clothes.

• 한국정보통신학회논문지
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• 제20권6호
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• pp.1209-1214
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• 2016

자동차 번호판 인식은 대중적인 감시 기술 중의 한 종류로서, 주어진 비디오나 영상 내 광학문자 인식을 수반한다. 번호판 인식은 자동차 번호판 국부화, 번호판의 크기, 차원, 명암대비, 밝기를 조정하는 정규화, 개별문자를 얻어내는 문자 분할, 문자를 인식하는 광학 문자 인식, 번호판의 형태, 크기, 위치 들이 연도별, 지역별로 차이가 있는 번호판들의 데이터베이스를 비교하여 구문 분석을 하는 절차를 거친다. 본 논문에서는 EmguCV를 이용하여 구현한 번호판 감지를 수행하여 위치를 찾아내고, 오픈 소스 광학 문자 인식 엔진으로 잘 알려져 있는 테서렉트 OCR을 이용하여 번호판의 문자를 인식하는 자동 인식 프로그램을 구현하고 번호판의 촬영 각도, 크기, 밝기에 대한 성능평가 결과에 관해 기술하였다.

• 한국식품영양학회지
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• 제27권6호
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• pp.979-989
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• 2014

The purpose of this study was to investigate the association between dietary behavior and temperament & character in preschool children, and to offer basic data that can be applied for nutrition education and counseling. A total of 211 parents of preschool children aged 3~5 years performed the Korean version of Preschool Temperament and Character Inventory (K-psTCI), a questionnaire based on Cloninger's seven-factor model of personality, along with a questionnaire about the dietary behaviors of their children. K-psTCI represented seven factors such as harm avoidance (HA), novelty seeking (NS), reward dependence (RD), persistence (P), self-directedness (SD), cooperativeness (CO), and self-transcendence (ST). The subjects were divided into either the high rank group or low rank group based on the mean score of each factor. The high rank group of HA showed significantly less physical activity and less appetite than the low rank group of HA. The children in the high rank of NS were more likely to have picky eating and a late night snack. The children in the low rank of SD or CO were more likely to have undesirable dietary behaviors, such as picky eating, too much snacking, and lower appetite than those in the high rank of SD or CO. In conclusion, individual temperament & character in preschool children may be associated with their dietary behavior, and understanding temperament & character in children may be important facts to screen and to develop an effective nutrition education program for children.

• 한국인터넷방송통신학회논문지
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• 제23권1호
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• pp.1-6
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• 2023

언캐니 밸리는 일본의 로봇 공학자 모리 마사히로가 1970년 제안한 이론으로 로봇이 완전히 갖추어지지 않은 상태에서 외형이 인간과 매우 흡사해질 때 호감도가 급격하게 떨어지는 지점을 가리킨다. 본 연구에서는 기존 문헌들의 이론과 선행 연구된 관련 논문 그리고 실험 자료를 분석하여 캐릭터 디자인에 영향을 미치는 언캐니 밸리 효과에 대해 고찰하였으며 본 연구의 목적은 로봇 분야에서 연구된 언캐니 밸리 효과가 언리얼 엔진의 마켓플레이스에서 유통되고 있는 가상 캐릭터의 모델링에도 적용되는지 분석하기 위함이다. 이를 위해 언리얼 엔진의 마켓플레이스에서 인간과의 유사도를 기준으로 15가지 캐릭터를 선정하여 인간과의 유사도와 호감도에 대해 조사 및 분석하였다. 실험 결과 가상 캐릭터 모델링에도 언캐니 밸리 효과가 유사하게 적용되고 있음을 확인하였고 이를 통해 언리얼 엔진을 사용하는 인디 게임 개발자나 개인 개발자의 캐릭터 활용을 위한 가이드라인을 제시하고자 하였다. 또한 결과 분석을 통해 캐릭터 디자인 시 추구해야 할 방향에 대해 살펴보고자 하였다.

• 한국철학논집
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• 제38호
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• pp.7-38
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• 2013

노자 철학에서 우리는 유위적 개인과 무위적 개인이라는 두 가지 유형의 개인을 유추할 수 있다. 유위적 개인은 확장성을 그 특징으로 하는데, 이들의 확장성은 곧 타인의 권리를 침해하는 공격성으로 나타나고, 이것이 문화적으로 고착된 사회가 허위적 사회이다. 무위적 개인은 허위적 권력과 권위를 버리고 자신의 참모습과 생명에 집중하며, 나아가 자발적으로 자신의 권리를 제한한다. 이들의 이러한 행위 방식은 상대방에게 자율공간을 마련해 줌으로써 상대방 스스로 자신의 생명력을 충실히 할 수 있게 한다. 이러한 무위적 개인들의 관계로 형성된 것이 현동 사회이다. 노자의 현동 사회는 개인권을 제한하지만 오히려 개인의 자율, 생명, 행복을 보장하고, 공동선을 세우지 않지만 오히려 공동선이 끊임없이 생성되는 개인-공동체 모형을 제시한다. 이는 개인권을 보장하는 동시에 공동선도 함께 실현하려는 시도와 다른 관점이다.