• Korean Journal of School Psychology
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• v.16 no.2
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• pp.89-110
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• 2019

This study is to verify relative influence of individual, parent, peer, teacher-related variables as protective factors and risk factors of academic hatred. Surveys were conducted with 1,015 (women, 57.3%) high school third grade students across eight schools where are located in Seoul, Incheon, and Geyonggi province. Correlation analysis and hierarchical multiple regression analysis were performed. The findings are summarized as follows. Teacher's academic pressure did not have significant correlation with student's basic psychological needs, teacher's autonomy support, teacher's support, and peer support whereas other variables showed significant correlation each others. The hierarchical multiple regression analysis indicated that student's individual competence and autonomy, parent's academic support, and teacher's emotional support work as protective factors and that parent's academic pressure functions as a risk factor. The effects of peer support disappeared when teacher-related factors were included. In addition, the effects of teacher's autonomy support disappeared, while the effects of teacher's support strengthened when learner's basic psychological needs were input. This study is meaningful in that it clarified academic hatred which had not been studied in other research and that it provided theoretical foundation for subsequent studies on academic hatred by examining relative influence of related variables. Lastly, it presented its limitation, implications on intervening strategies in school counseling, and suggestions for later studies.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.381-384
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• 2005

현존하는 정보처리 시스템 중에서 가장 뛰어난 성능을 지니고 있는 것은 인간의 두뇌라고 할 수 있다. 두뇌의 정보처리 메커니즘을 보다 정확하게 구현할 수 있는 시스템은 입력에 대한 정확한 인지 능력, 상황 판단 능력, 학습 및 추론 능력, 출력의 결정 능력 등의 성능 구현은 물론이며, 감정과 비교될 수 있는 시스템의 상태를 평가하여 판단 및 결정에 적용함으로써 매우 뛰어난 지능형 시스템이 쥘 수 있다. 이러한 뇌 정보처리 시스템의 구현에 앞서 본 논문에서는 생물학적인 대뇌 피질의 구조를 살피고 정보의 처리 영역을 고찰하고 정보의 흐름을 소개하였으며 이를 바탕으로 뇌 정보처리 메커니즘을 공학적인 측면에서 해석해 보았다. 특히, 뇌 영역의 기능 및 구조적인 특징, 정보의 처리과정 등을 공학적으로 해석하였으며 이는 뇌의 기능을 모방한 공학적인 모델을 구현하는데 있어서 기초가 될 것이다.

• Hwankyungkyoyuk
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• v.24 no.2
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• pp.99-111
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• 2011

We developed animation-based environmental teaching program using empathic learning strategy in order to motivate elementary school students' willingness to practice environmental-friendly behavior and applied it to elementary school students. The subjects were 56 fourth graders, who were divided into the experimental and controlled groups. Experimental group was taught with animation programs using the empathic learning strategy and controlled group took typical classes. There was significant difference between experimental and controlled groups(p<.05) in terms of environmental practice willingness. When examining students' environmental-friendly recognition, this study showed positive result that they were more interested and motivated in the learning process. Thus, it concluded that the environmental animation program using empathic learning strategy was an effective teaching method. It showed that it was more effective to improve environmental knowledge and motivates students to actively participate in instructions and to concentrate on educating materials.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.1057-1062
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• 2007

얼굴표정 인식은 심리학 연구, 얼굴 애니메이션 합성, 로봇공학, HCI(Human Computer Interaction) 등 다양한 분야에서 중요성이 증가하고 있다. 얼굴표정은 사람의 감정 표현, 관심의 정도와 같은 사회적 상호작용에 있어서 중요한 정보를 제공한다. 얼굴표정 인식은 크게 정지영상을 이용한 방법과 동영상을 이용한 방법으로 나눌 수 있다. 정지영상을 이용할 경우에는 처리량이 적어 속도가 빠르다는 장점이 있지만 얼굴의 변화가 클 경우 매칭, 정합에 의한 인식이 어렵다는 단점이 있다. 동영상을 이용한 얼굴표정 인식 방법은 신경망, Optical Flow, HMM(Hidden Markov Models) 등의 방법을 이용하여 사용자의 표정 변화를 연속적으로 처리할 수 있어 실시간으로 컴퓨터와의 상호작용에 유용하다. 그러나 정지영상에 비해 처리량이 많고 학습이나 데이터베이스 구축을 위한 많은 데이터가 필요하다는 단점이 있다. 본 논문에서 제안하는 실시간 얼굴표정 인식 시스템은 얼굴영역 검출, 얼굴 특징 검출, 얼굴표정 분류, 아바타 제어의 네 가지 과정으로 구성된다. 웹캠을 통하여 입력된 얼굴영상에 대하여 정확한 얼굴영역을 검출하기 위하여 히스토그램 평활화와 참조 화이트(Reference White) 기법을 적용, HT 컬러모델과 PCA(Principle Component Analysis) 변환을 이용하여 얼굴영역을 검출한다. 검출된 얼굴영역에서 얼굴의 기하학적 정보를 이용하여 얼굴의 특징요소의 후보영역을 결정하고 각 특징점들에 대한 템플릿 매칭과 에지를 검출하여 얼굴표정 인식에 필요한 특징을 추출한다. 각각의 검출된 특징점들에 대하여 Optical Flow알고리즘을 적용한 움직임 정보로부터 특징 벡터를 획득한다. 이렇게 획득한 특징 벡터를 SVM(Support Vector Machine)을 이용하여 얼굴표정을 분류하였으며 추출된 얼굴의 특징에 의하여 인식된 얼굴표정을 아바타로 표현하였다.

• School Mathematics
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• v.19 no.4
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• pp.775-793
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• 2017

In this study, instruments were developed to measure of mathematics attitudes by conceptualization of epistemological beliefs as a cognitive dimension, mindfulness as a conative dimension, affect as an affective dimension. Perry's epistemological development scheme and Langer's mindfulness theory was noticed as a theoretical approach. Exploratory factor and confirmatory factor analyses, and a reliability test were assessed. This article suggest a new framework for analysing attitudes toward mathematics and changes in attitudes toward mathematics.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.354-357
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• 2019

최근 맞벌이 가정이 많아지면서 베이비 시터를 고용해 영아를 양육하는 경우가 많아지고 있는 추세이다. 본 논문에서는 영유아 상태분석에 따른 인공지능 베이비시터 시스템에 대하여 기술하였다. 보다 상세하게는 얼굴인식을 위한 Opencv 영상처리 기법, MS(azure)API 를 이용한 머신러닝 기반의 감정분석과 악취 센서(MQ-135 Sensor)를 이용하여 영유아의 상태를 파악한다. 파악한 영유아의 상태를 바탕으로 스스로 학습하여 요람을 제어하고 어플리케이션을 통해 원격제어를 할 수 있도록 제작한 스마트 베이비시터 시스템에 관한 것이다. 이에 따라 양육에 대한 부담감이 줄어들 것으로 기대하고 양육에 대한 부담감을 조금이나마 경감 시켜 주어 저출산과 양육 지출 비용 절약으로 사회적 측면, 경제적 측면 모두에 기여할 것을 기대한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.693-700
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• 2023

Sentiment analysis, a branch of natural language processing that classifies and identifies subjective opinions and emotions in text documents as positive or negative, can be used for various promotions and services through customer preference analysis. To this end, recent research has been conducted utilizing various techniques in machine learning and deep learning. In this study, we propose an optimal language model by comparing the accuracy of sentiment analysis for movie, product, and game reviews using existing RNN-based models and recent Transformer-based language models. In our experiments, LMKorBERT and GPT3 showed relatively good accuracy among the models pre-trained on the Korean corpus.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.147-149
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• 2021

Recently, the rapid growth of the media market and the expectations of users have been increasing. In this research, tags are extracted through media-derived audio and classified into specific categories using artificial intelligence. This category is a type of emotion including joy, anger, sadness, love, hatred, desire, etc. We use JupyterNotebook to conduct the corresponding study, analyze voice data using the LiBROSA library within JupyterNotebook, and use Neural Network using keras and layer models.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.706-708
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• 2023

In this study, we embark on a journey to uncover the essence of emotions by exploring the depths of transfer learning on three pre-trained transformer models. Our quest to classify five emotions culminates in discovering the KLUE (Korean Language Understanding Evaluation)-BERT (Bidirectional Encoder Representations from Transformers) model, which is the most exceptional among its peers. Our analysis of F1 scores attests to its superior learning and generalization abilities on the experimental data. To delve deeper into the mystery behind its success, we employ the powerful SHAP (Shapley Additive Explanations) method to unravel the intricacies of the KLUE-BERT model. The findings of our investigation are presented with a mesmerizing text plot visualization, which serves as a window into the model's soul. This approach enables us to grasp the impact of individual tokens on emotion classification and provides irrefutable, visually appealing evidence to support the predictions of the KLUE-BERT model.

• Journal of Engineering Education Research
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• v.9 no.2
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• pp.5-20
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• 2006

This study is a part of the research on measuring and developing the creativity of college students, especially of engineering college students. This study was done in the class of imaginative design engineering which was a creativity training program integrated with engineering majors, including idea generation skills, creative problem solving, patent applications, design, manufacturing and marketing. Participants in this program were 75 freshman strudetns in engineering college. The achievements in this program were measured by the figural form of TTCT(Torrance Tests of Creative Thinking), as well as MBTI(Myers-Briggs Type Indicator). The results are as follows. First, TTCT scores of the participants in imaginetive design engineering increased significantly. Second, type indicator scores of MBTI varied significantly, to the directions of extrovert, feeling, and perception. Therefore, according to the results of this study, we can conclude the imaginative design engineering class had significant positive effects on the creativity of the engineering students.