• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.78-91
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• 2022

The importance of emotional intelligence education in the development of children's augmented reality education content is overlooked. Therefore, in-depth research is needed to develop children's emotional intelligence. This study was conducted through theoretical consideration and case analysis. The proposal of this paper is that the augmented reality type for children aged 2 to 7 is suitable for indoor activities with marking recognition technology. To promote an understanding of emotions, a large screen is selected, and emoticon dolls or emoticon books are recommended for learning content. Children aged 7 to 11 are suitable for indoor activities of non-marker recognition technology, and can induce emotional control and emotional recognition through active manipulation. For the learning content, "3D art teaching content" and "Online Classic Musical" are recommended. Children after the age of 11 are suitable for non-marker recognition technology outdoor activities and improve each element of emotional intelligence through interaction with nature and society. For the learning content, 'Forest Play Activity through Art' and 'EQ Theater Play' are recommended. Through this paper, we intend to promote the development of children's augmented reality emotional intelligence education.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.204-208
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• 2007

대화시스템은 사용자가 자연언어를 사용하여 해당 시스템과 필요한 정보를 주고받는 목적 지향적 에이전트로서 활용되어 왔다. 이러한 대화형 에이전트는 사용자의 입력으로부터 필요한 정보를 정확하게 추출함으로써 이후 처리단계에서의 결과를 향상시킬 수 있다. 본 논문에서는 항공여행관련 대화에서 발생하는 예약정보들 중에서 경유정보, 특히 경유하는 시간 및 날짜에 대한 정보를 효과적으로 추출하는 방법에 대해서 다룬다. 출발 도착정보와 경유정보를 계층적으로 분류하고, 현재 발화되고 있는 문장보다 선행되고 있는 문장들의 예약정보들을 문맥정보로 사용하여 현재 문장에서 추출하고자 하는 정보들을 학습하고 평가하였다. 이를 통해서 얻어진 결과는 출발.도착 및 경유정보를 동시에 고려했을 때보다 효과적인 학습 성능을 보였으며 실제로 시간정보에 대해서는 81.5%, 날짜정보에 대해서는 92.0%의 정확도를 보였다.

• Journal of The Korean Association For Science Education
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• v.32 no.5
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• pp.954-973
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• 2012

To apply "ethics of care" into science education, this study summarized previous studies of care, ethics of care, and caring education. Through a wide range of literature review, we proposed science learning model with ethics of care. This model has steps of 'being in a context of issue, perception of issue-related value, choosing value with ethics of care, feeling empathy to caring subject, experiencing care, and verifying the effectiveness of caring, which are reflected characteristics of ethics of care, contextual, connected, and practical. It is expected that students will be able to solve science-related issues while keeping in mind consideration for nature as a caring subject.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.238-245
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• 2013

Gesture recognition has been widely one of the research areas for natural user interface. This paper presents a novel gesture recognition method using tree classification and multiclass SVM(Support Vector Machine). In the learning step, 3D trajectory of human gesture obtained by a Kinect sensor is classified into the tree nodes according to their distributions. The gestures are resampled and we obtain the histogram of the chain code from the normalized data. Then multiclass SVM is applied to the classified gestures in the node. The input gesture classified using the constructed tree is recognized with multiclass SVM.

• Journal of Digital Convergence
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• v.9 no.6
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• pp.411-419
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• 2011

Recently concept of self-leadership that leads one's own activities toward right direction through self-control or self-management has been being focused on practices as well as academia. This study is to investigate the influence between self-leadership strategies and learning performance at IT classes mediated by attitude of attendance focused on the social science students at an university. This study performed the comparative study to investigate whether differences among Korean(64 samples) and Chinese(31 samples) students is or not Research results can give us right direction of task-taking attitudes in firms or learning attitudes in teaching organization and implications to human resource managers who are in charge of improving learning performance or productivity.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.26-26
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• 2017

우리나라는 하천 및 호수 등 지표수에 대한 수자원 의존도가 매우 높다. 지표수는 태양광에 노출되어 있고, 기온의 영향을 직접 받기 때문에 기후변화에 대해 매우 민감한 수체이다. 기후변화로 인한 이상 저온, 이상 고온, 홍수, 가뭄 등의 자연 현상은 하천, 호수의 물리화학적 및 생태학적 특성을 변화(교란)시키고 있다. 이러한 기상현상에 변동되는 수질특성을 고려하여 기상청 확률기상예보를 구축된 인공신경망 예측모형의 입력인자로 적용하여 수질예보시스템을 개발하고자 하였다. 모형구축은 실제 일어난 기상관측자료와 요인분석을 통해 분류한 수질인자를 반영하여 단위유역별 수질예측을 위한 ANN학습을 실시하였다. 각 단위유역마다 기상요인의 공간적 세밀화 적용을 위해 각각 남강A, 남강B는 산청기상대, 남강C, 남강D는 진주기상대, 남강E는 의령기상대 자료를 이용하였으며, 수질항목은 DO, BOD, COD, TOC, T-P, SS 총 6개로 단위유역 5개에서 총 30개 예측모형 구축을 위한 자료를 수집하였다. 학습된 인공신경망 예측모형에 기상청 확률예보 값을 입력인자로 사용하여 모형평가를 실시하였다. 5개 단위유역 중 상대적으로 유역관리의 시급성을 고려하여 남강댐 하류 단위유역인 남강D, 남강E 인공신경망 모형의 입력자료로 적용하여 평가하였다.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.209-213
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• 2023

최근 자연어 이해 분야에서 대규모 언어모델 기반으로 프롬프트를 활용하여 모델과 상호작용하는 방법이 널리 연구되고 있으며, 특히 상담 분야에서 언어모델을 활용한다면 내담자와의 자연스러운 대화를 주도할 수 있는 대화생성 모델로 확장이 가능하다. 내담자의 상황에 따라 개인화된 상담대화를 진행하는 모델을 학습시키려면 동일한 내담자에 대한 과거 및 차기 상담대화가 필요하지만, 기존의 데이터셋은 대체로 단일 대화세션으로 구축되어 있다. 본 논문에서는 언어모델을 활용하여 단일 대화세션으로 구축된 기존 상담대화 데이터셋을 확장하여 연속된 대화세션 구성의 학습데이터를 확보할 수 있는 프롬프트 기반 데이터 증강 기법을 제안한다. 제안 기법은 기존 대화내용을 반영한 요약질문 생성단계와 대화맥락을 유지한 차기 상담대화 생성 단계로 구성되며, 프롬프트 엔지니어링을 통해 상담 분야의 데이터셋을 확장하고 사용자 평가를 통해 제안 기법의 데이터 증강이 품질에 미치는 영향을 확인한다.

• Korean Journal of Ecology and Environment
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• v.56 no.4
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• pp.406-413
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• 2023

Captive breeding and reintroduction are crucial strategies for conserving endangered species populations. However, fish raised in predator-free environments, show a lack of recognition of predationrelated stimuli such as chemical and visual signals. It is critical to recognize chemical signals from injured conspecifics, also known as alarm signals, and the order or shape of predators to indicate the spread of predation risk in the habitat. We conducted a laboratory experiment to determine and adjust the optimal exposure period to induce appropriate anti-predator behavior response to different types of stimuli (Chemical, Visual and Chemical+Visual) for the endangered species Microphysogobio rapidus. Our results demonstrate that predator avoidance behavior varies depending on the types of stimuli and the duration of predation risk exposure. First, the results showed captive-breed M. rapidus show lack of response against conspecific alarm signal (Chemical cue) before the predation risk exposure period and tend to increase response over predation risk exposure time. Second, response to predator (visual cue) tend to peak at 48 hours cumulative exposure, but show dramatic decrease after 72 hours cumulative exposure. Finally, response to the mixed cue (Chemical+visual) tend to peak prior to the predation risk exposure period and show reduced response during subsequent exposure periods. This experiment confirms the lack of responsiveness to conspecific alarm signals in captive-bred M. rapidus and the need for an optimal nature behavior enhancement program prior to release of endangered species. Furthermore, responsiveness to predator visual signal peak at 48 hours cumulative exposure, suggest an optimal predation risk exposure period of up to 48 hours.

• Journal of Korean Home Economics Education Association
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• v.16 no.2
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• pp.73-84
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• 2004

The Purpose of this study was to investigate the relationship between elementary and middle school students’ depression and their mothers’ parenting stress. The subject were 659 elementary and middle school students and their mothers. In the study, elementary and middle school students depression appeared significant difference to their sex/grade. Parenting stress related to learning expectation appeared significant difference to elementary and middle school students’ sex/grade. Elementary and middle school students depression appeared significant difference to Parenting stress. And as for correlating parenting stress to elementary and middle school students’ depression, the significance appeared in these factors.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.155-164
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• 2021

Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Nevertheless, barks show a complex shape with a large variation depending on the environment, and there are insufficient materials that can be utilized to train algorithms. Here, in addition to the previously published bark image dataset, BarkNet v.1.0, images of barks were collected, and a dataset consisting of 53 tree species that can be easily observed in Korea was presented. A convolutional neural network (CNN) was trained and tested on the dataset, and the factors that interfere with the model's performance were identified. For CNN architecture, VGG-16 and 19 were utilized. As a result, VGG-16 achieved 90.41% and VGG-19 achieved 92.62% accuracy. When tested on new tree images that do not exist in the original dataset but belong to the same genus or family, it was confirmed that more than 80% of cases were successfully identified as the same genus or family. Meanwhile, it was found that the model tended to misclassify when there were distracting features in the image, including leaves, mosses, and knots. In these cases, we propose that random cropping and classification by majority votes are valid for improving possible errors in training and inferences.