• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.63-70
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• 2019

These days, as Interest in Image recognition with deep learning is increasing, there has been a lot of research in image recognition using deep learning. In this study, we propose a system for classifying rocks through rock images of 18 types of rock(6 types of igneous, 6 types of metamorphic, 6 types of sedimentary rock) which are addressed in the high school curriculum, using CNN model based on Tensorflow, deep learning open source framework. As a result, we developed a classifier to distinguish rocks by learning the images of rocks and confirmed the classification performance of rock classifier. Finally, through the mobile application implemented, students can use the application as a learning tool in classroom or on-site experience.

• International Area Studies Review
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• v.13 no.1
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• pp.491-512
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• 2009

The study on waiting time has been conducted from the psychological perspective since Maister (1985). In using medical institutions, especially general hospitals, it is not avoidable to wait for a long time. As a result, the waiting time gives clients psychological stress, which causes medical consumers to be more dissatisfied and decreases their rate of revisit. Accordingly, it is very urgent to study on the waiting time for hospitals' marketing and better image-making. This study is intended to find out how hospitals image and clients revisit is influenced by waiting environment and consumers' attitude, variables of waiting time perceived in medical services. Based on this study, those medical institutions are required to improve their medical service and waiting environment. Accordingly, they should convert waiting time into more efficient and comfortable recess and provide better environment for physical service and effective human services. As those medical institutions have relatively worse image than other businesses, they should actively study on ways of better image-making in the future.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.243-246
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• 2019

본 논문에서는 감성과 힐링, 머신러닝이라는 주제를 바탕으로 딥러닝을 통한 사용자의 얼굴표정을 인식하고 그 얼굴표정을 기반으로 음악을 재생해주는 얼굴표정 기반의 음악재생 프로그램을 제안한다. 얼굴표정 기반 음악재생 프로그램은 딥러닝 기반의 음악 프로그램으로써, 이미지 인식 분야에서 뛰어난 성능을 보여주고 있는 CNN 모델을 기반으로 얼굴의 표정을 인식할 수 있도록 데이터 학습을 진행하였고, 학습된 모델을 이용하여 웹캠으로부터 사용자의 얼굴표정을 인식하는 것을 통해 사용자의 감정을 추측해낸다. 그 후, 해당 감정에 맞게 감정을 더 증폭시켜줄 수 있도록, 감정과 매칭되는 노래를 재생해주고, 이를 통해, 사용자의 감정이 힐링 및 완화될 수 있도록 도움을 준다.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.345-346
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• 2019

SNS의 다양한 역기능과 함께 중독문제가 사회적 문제로 대두되고 있는 가운데 이미지 기반의 인스타그램이 강세를 보인다. 이에 본 연구는 SNS중에서 이용도가 높은 인스타그램 사용제한 시 사용자의 감정에 미치는 영향과 대안 활동을 파악하기 위한 목적으로 수행되었다. 실험 방법은 인스타그램 1일 5회 이상 이용자 3명을 대상으로 7일간 앱 삭제 및 이용을 제한하고 매일 1인칭 관찰기법인 자기 일기 작성으로 감정변화와 대안 활동을 수집했다. 본 연구의 결과는 사용 빈도수가 높을수록 시간이 흘러도 부정적 감정이 감소하지 않았고 사용 빈도수가 낮을수록 부정적 감정이 점차 감소하였다. 대안 활동으로는 오프라인 활동보다는 온라인 활동이 많았고 여러 종류의 스마트폰 미디어 활동을 한 것으로 나타났다. 이 연구는 나아가 의존도에 따라 부정적 감정소강 소요 시간을 측정하는 연구로 발전될 것을 기대하며 이에 따라 SNS중독성 해결에 필요한 시간, 대안 활동 제시의 연구 초석이 되길 기대한다.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.450-452
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• 2020

기술의 발전과 함께 사용자에게 가까이 자리 잡은 소셜 네트워크 서비스는 이미지, 동영상, 텍스트 등 활용 가능한 데이터의 수를 폭발적으로 증가시켰다. 작성자의 감정을 포함하고 있는 텍스트 데이터는 시장 조사, 주가 예측 등 다양한 분야에서 이용할 수 있으며, 이로 인해 긍부정의 이진 분류가 아닌 다중 감정 분석의 필요성 또한 높아지고 있다. 본 논문에서는 딥러닝 기반 감정 분류에 심리학 이론의 기반 감정 모델을 활용한 결합 모델과 단일 모델을 비교한다. 학습을 위해 AI Hub에서 제공하는 데이터와 노래 가사 데이터를 복합적으로 사용하였으며, 결과에서는 대부분의 경우에 결합 모델이 높은 결과를 보였다.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.447-456
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• 2019

This study was to examine the physical environment of public golf course for golf popularization. More specifically, this study was try to analyze the effects of physical environment on customer emotional response, golf course's image and recommendation intention of public golf course. This study were analyzed by frequency analysis, exploratory factor analysis, reliability analysis, correlation and multiple regression analysis. The results were as follows. First, among the physical environmental variables of public golf course, facilities' convenience, cleanliness, and aesthetics had positive effects on customers' positive emotion. Second, among the physical environmental variables of public golf course, facilities' cleanliness had effects on customers' negative emotion. Third, physical environment of public golf course had positive effects on golf course's image. Fourth, physical environment of public golf course had positive effects on recommendation intention.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Journal of The Korean Association For Science Education
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• v.37 no.4
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• pp.705-717
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• 2017

The purpose of the study is to explore secondary science teachers' emotional display rules, types of emotional labor, science-specific emotional display rules and the episodes of emotional labor. For this purpose, the survey to measure emotional labor of science teachers (The Emotional Labor of Science Teaching Scale: TELSTS) was developed and the participants were 145 secondary science teachers in Korea. Results showed that first, secondary science teachers recognized the emotional display rules defined by their schools, especially, positive display rules. Second, secondary science teachers showed that they were carrying out emotional labor in order to keep their emotional display rules in check. The mean value of responses to deep acting was high. Also, there were statistically significant differences in emotional labor depending on whether they were full-time or part-time teachers and their teaching career. Third, as a result of analyzing the specificity of science teachers, it was mainly related to the objective and logical image of science, and experimental instruction. Seventy-four percent (74%) of responses were negative or neutral emotional display rules. Finally, implications for science education are discussed.

• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2023.04a
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• pp.61-62
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• 2023

• Journal of the Korean Society of Costume
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• v.59 no.10
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• pp.111-123
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• 2009

This study investigated the multifaced country image. The casual relationships among the multifaced country image, attitudes toward a country, and purchase emotion and purchase intention of fashion products with Korean images were also investigated. To conduct a quantitative analysis, we collected data from 296 Korean female consumers. SPSS 12.0 and AMOS 5.0 packages were used for statistical analysis. The results of this study as follows. To identify components of country image of Korea, exploratory and confirmatory factor analyses were conducted. This procedure produced five components such as culture, technique/product, ethical values, nationality, and space. Structural equational model was used to analyze the relationships among the country image, attitudes toward a country, purchase emotion, and purchase intention of fashion products with Korean image. The proposed model was verified.