• 한국가정과교육학회지
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• 제19권3호
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• pp.91-106
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• 2007

The purpose of this study was to provide the information for the development of gender-equality oriented content of Technology Home Economics subject. For this purpose, a total of 404 male and female middle high school students in Chuncheon city were sampled and asked to estimate the degree of practical use and preference for the 47 units of Technology Home Economics subject. Results were summarized as following : 1. The 47 units were classified into 4 groups on the basis of similarity in the degree of practical use and preference: 23 units estimated as 'better than average' by male and female students were classified into group 1; 4 units estimated as 'better than average' by female students but as 'less than average' by male students were classified into group 2; 10 units estimated as 'less than average' by male and female students were classified into group. 3; 10 units estimated as 'far less than average' by male and female students were classified into group 4. Most of the units in Home Economics area were classified Into group 1 or 2, but most of the units in Technology area were classified into group 3 or 4. 2. Gender difference was confirmed between male and female students' estimation of the degree of practical use and preference for the 47 units. In about three-quaters of the units in Home Economics area, female students' estimation of the degree of practical use and preference was higher than male students' estimation. In about half of the units in Technology area, male students' estimation of the degree of practical use and preference was higher than female students' estimation. However, possibility was detected in several units of Technology Home Economics subject that gender difference could be decreased.

• 한국음향학회지
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• 제26권2호
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• pp.75-79
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• 2007

본 논문은 SVM(Support Vector Machines)을 이용한 음성신호 기반의 효과적인 성별인식 시스템을 제안한다. 분별적 이진(binary) 패턴 분류기인 SVM은 특징 공간에서 비선형 경계를 찾아 분류하는 방법으로 우수한 성능을 보인다고 알려져 있다. 연구에서는 기존의 성별인식에서 널리 쓰이고 있는 MFCC(Mel Frequency Cepstral Coefficients)를 사용하여 SVM과 기존의 GMM(Gaussian Mixture Model) 알고리즘의 성별인식 성능을 비교하였고, 특히, 보다 향상된 SVM의 성별인식을 위해 MFCC와 Pitch를 이용한 결합 특징 벡터를 적용하였다. 실험결과 MFCC 파라미터를 사용했을 때 제안된 SVM이 GMM보다 우수한 성별인식 성능을 보였고, 제안된 결합 특징 벡터를 사용 했을 때 우수한 성능을 보였다.

• International Journal of Costume and Fashion
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• 제6권2호
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• pp.28-42
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• 2006

In present modern society, media contributes more to the constructing of personal identities than any other medium. Music video, a postmodernism branch among a variety of media, offers a complex experience of sounds combined with visual images. In particular. fashion in music video helps conveying contexts effectively and functions as a medium of immediate communication by visual effect. Considering the socio-cultural effects of music video. gender identity represented in fashion in it can be of great importance. Therefore, this study is geared to the reconsidering of gender identity represented through costumes in music video by analyzing fashions in it. Gender identity in socio-cultural category is classified as masculinity, femininity, and the third sex. By examining fashions based on the classification. this study will help to create new design concepts and to understand gender identity in fashion. The results of this study are as follows: First. masculinity in music video fashion was categorized into stereotyped masculinity, sexual masculinity. and metro sexual masculinity. Second, femininity in music video fashion was categorized into stereotyped femininity. sexual femininity, and contra sexual femininity. Third, the third sex in music video fashion was categorized into transvestism, masculinization of female, and feminization of male. This phenomenon is presented into music videos through females in male attire and males in female attire. Through this research, gender identity represented in fashion of music video was demonstrated, and the importance of the relationship between representation of identity through fashion and socio-cultural environment was reconfirmed.

• 말소리와 음성과학
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• 제15권2호
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• pp.43-51
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• 2023

본 논문에서는 음성의 발화 속도와 휴지 구간의 길이 그리고 화자의 연령과 성별에 기반한 방언 분류 접근 방법을 제안한다. 방언 분류는 음성 분석을 위한 중요한 기술 중 하나이다. 예를 들어 정확한 방언 분류 모델은 화자 인식 또는 음성 인식의 성능을 향상시킬 수 있는 잠재력을 가질 수 있다. 선행 연구에 따르면, Mel-Frequency Cepstral Coefficients(MFCC) 특징을 사용한 딥러닝 기반의 연구가 주류를 이루었다. 우리는 지역 간의 음향적 차이에 주목하여 그 차이를 바탕으로 추출한 특징을 사용하여 방언 분류를 진행하였다. 본 논문에서는 음성의 발화 속도, 휴지 구간의 길이 특성을 추출하여 사용하며 이와 함께 화자의 연령과 성별과 같은 메타데이터를 추가로 사용하는 새로운 접근 방법을 제안한다. 실험 결과 제안된 접근 방법이 더 높은 정확도를 보이는 것을 확인하였으며 특히 음성의 발화 속도 특성을 사용하는 것이 기존 MFCC만을 사용하는 방법보다 향상된 성능을 보여준다는 것을 확인할 수 있었다. MFCC 특성만을 사용한 방법과 비교했을 때 본 논문에서 제안한 특성들을 모두 사용하였을 때의 정확도는 91.02%에서 97.02%로 향상되었다.

• 로봇학회논문지
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• 제3권3호
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• pp.165-175
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• 2008

For an advanced intelligent service, the need of HRI technology has recently been increasing and the technology has been also improved. However, HRI components have been evaluated under stable and controlled laboratory environments and there are no evaluation results of performance in real environments. Therefore, robot service providers and users have not been getting sufficient information on the level of current HRI technology. In this paper, we provide the evaluation results of the performance of the HRI components on the robot platforms providing actual services in pilot service sites. For the evaluation, we select face detection component, speaker gender classification component and sound localization component as representative HRI components closing to the commercialization. The goal of this paper is to provide valuable information and reference performance on appling the HRI components to real robot environments.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2011년도 하계학술대회
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• pp.552-555
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• 2011

최근 스마트폰, 태블릿 PC와 같은 모바일 스마트 디바이스(mobile smart devices)와 더불어 스마트 TV에 대한 관심이 크게 증가하면서 사용자들의 컨텐츠와 기능에 대한 요구 또한 다양해지고 있다. 스마트 TV가 컨텐츠와 기능적 측면에서 사용자의 편의와 재미, 그리고 유익함을 동시에 만족시키기 위해서는 더욱 지능화된 기능을 탑재할 필요가 있다. 일반적으로 남녀에 따라 TV를 시청하는 경향이 다르기 때문에 현재 TV를 시청하는 사용자의 성별분류(gender classification)를 통해 성별에 따른 따른 채널이나 광고, 응용 프로그램을 달리 제공하는 성별 기반의 스마트 TV 응용을 개발할 수 있게 된다. 본 논문에서는 스마트 TV 응용에 적합한 BGOLAM 기반의 성별분류 방법에 대해 제안하고, 실험을 통해 제안하는 방법의 적절성을 보인다.

• 수산해양교육연구
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• 제25권1호
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• pp.246-259
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• 2013

The principal purpose of this study is to analyze school organizational culture in secondary school in Busan. This study measures background variables such as gender, teaching experience, classification of school, grade of school, and scale of school. The results of the study are as follows : First, to see the difference on the perception of organizational culture depending on gender, female teachers have a stronger sense of professionalism, community spirit and consideration than male teachers. Second, to see the difference on the perception of organizational culture in terms of teaching experience, teachers who have more than 21 years of teaching experience have a more positive perception on decision-making and consideration than those who have 11~20 years of teaching experience. Third, to see the difference on the perception of organizational culture according to classification of school, public schools have a more positive perception on every item such as professionalism, decision-making, community spirit, and consideration than private school. Fourth, to see the difference on the perception of organizational culture in terms of classification of schools, secondary schools have a more positive perception on professionalism and community spirit than high schools. Lastly, as it is seen in the difference on the perception of organizational culture depending on scale of school, schools which have 13~35 classes have a more positive perception on professionalism than others.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 춘계학술발표대회
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• pp.483-486
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• 2020

Age estimation and gender classification for human is a classic problem in computer vision. Almost research focus just only one task and the models are too heavy to run on low-cost system. In our research, we aim to apply multitasking learning to perform both task on a lightweight model which can achieve good precision on embedded system in the real time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권7호
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• pp.1705-1720
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• 2013

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.

• The Journal of Korean Physical Therapy
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• 제26권5호
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• pp.315-323
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• 2014

Purpose: This study was conducted in order to investigate the effect of motor ability on mastery motivation in children with cerebral palsy. Methods: Sixty children with cerebral palsy (5~12 years) and their parents participated in the study. Data on general characteristics and disability condition, Gross Motor Functional Classification System, Manual Ability Classification System, and The Dimensions of Mastery questionnaire were collected for this study. Independent t-test, and ANOVA were used for analysis of the effect of The Dimensions of Mastery questionnaire according to general and disability condition, Gross Motor Functional Classification System, and Manual Ability Classification System. Linear regression analysis was performed to determine the effects of Gross Motor Functional Classification System and Manual Ability Classification System on The Dimensions of Mastery questionnaire. SPSS win. 22.0 was used and Tukey was used for post hoc analysis, level of statistical significance was less than 0.05. Results: The Dimensions of Mastery questionnaire score showed statistically significant difference according to gender, region, type, disability rating, Gross Motor Functional Classification System, and Manual Ability Classification System (p<0.05). Gross Motor Functional Classification System and Manual Ability Classification System were the effect factor on The Dimensions of Mastery questionnaire significantly (p<0.05). Conclusion: These results suggest that motor ability of children with cerebral palsy was an important factor having an effect on The Dimensions of Mastery questionnaire.