• Title/Summary/Keyword: 동물문

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Elementary Students' Perceptions of Role and Epistemic Authority in the Activity about 'Making a Pet Poster' ('애완동물 안내문 만들기' 수업에서 나타나는 초등학생들의 역할 인식과 인식적 권위)

  • Kang, Eunhee;Kim, Heui-Baik
    • Journal of The Korean Association For Science Education
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    • v.37 no.4
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    • pp.587-597
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    • 2017

  • If we, as educators, want to put students at the center of learning in science classroom, we must let students express their voices and exercise authority. To do this, we developed a classroom activity about 'Making a pet poster', and then we explored how elementary school students perceived their roles and expressed their authority during this activity. Fourth grade students from an elementary school in the city of Seoul participated in the activity, which was videotaped and recorded. We found that students expressed their epistemic authority differently in small group activities and in whole group discussions. In small group activities, they desired to show their authority as "pet experts" by using and selecting various resources from their everyday lives and transforming those resources into suitable forms in public spaces. Meanwhile, in whole group discussions, participants were classified as either presenters or audience members to verify their authority in regard to the pet poster activity; presenters desired to achieve recognition as "pet experts," and audience members assessed the presenters as "testers." In addition, they expressed authority as teachers by leading the whole group discussions. Based on these findings, this paper suggests the implications for new educational strategies to foster a student-centered learning environment.

  • https://doi.org/10.14697/jkase.2017.37.4.587 인용 PDF KSCI

Design of Pet Behavior Classification Method Based On DeepLabCut and Mask R-CNN (DeepLabCut과 Mask R-CNN 기반 반려동물 행동 분류 설계)

  • Kwon, Juyeong;Shin, Minchan;Moon, Nammee
    • Proceedings of the Korea Information Processing Society Conference
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    • 2021.11a
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    • pp.927-929
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    • 2021

  • 최근 펫팸족(Pet-Family)과 같이 반려동물을 가족처럼 생각하는 가구가 증가하면서 반려동물 시장이 크게 성장하고 있다. 이러한 이유로 본 논문에서는 반려동물의 객체 식별을 통한 객체 분할과 신체 좌표추정에 기반을 둔 반려동물의 행동 분류 방법을 제안한다. 이 방법은 CCTV를 통해 반려동물 영상 데이터를 수집한다. 수집된 영상 데이터는 반려동물의 인스턴스 분할을 위해 Mask R-CNN(Region Convolutional Neural Networks) 모델을 적용하고, DeepLabCut 모델을 통해 추정된 신체 좌푯값을 도출한다. 이 결과로 도출된 영상 데이터와 추정된 신체 좌표 값은 CNN(Convolutional Neural Networks)-LSTM(Long Short-Term Memory) 모델을 적용하여 행동을 분류한다. 본 모델을 바탕으로 행동을 분석 및 분류하여, 반려동물의 위험 상황과 돌발 행동에 대한 올바른 대처를 제공할 수 있는 기반을 제공할 것이라 기대한다.

  • https://doi.org/10.3745/PKIPS.y2021m11a.927 인용 PDF