• 한국초등과학교육학회지:초등과학교육
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• 제39권1호
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• pp.145-153
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• 2020

Students' engagement is very important for effect science learning. Multidimensional approaches on students' engagement defines engagement in three ways which includes cognitive, behavioral, and cognitive engagement. Based on the multidimensional approaches on students' engagement, this study identified latent groups of elementary students characterized by patterns of cognitive, behavioral, and emotional engagement in science classes. This study also compared students' perceptions of their engagement in general science classes and small-group activities by the latent groups. 377 elementary students were involved in this study. 5-scale Likert survey were used in order to investigate students' engagement in science classes. Latent class analysis using Mplus program identified 3 latent groups of students engagement in science classes: Highly engaged, moderately engaged, and minimally engaged in three ways of engagement. The mean scores of cognitive, behavioral, and emotional engagement were significantly different by three latent groups. In addition, there were significant difference in students perceptions on participating experiments activities and carefully listening of teacher among latent groups. However, there was no significant difference in students' perceptions on their actions during small-group activities. Educational implications were discussed.

• 정보교육학회논문지
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• 제6권1호
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• pp.64-74
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• 2002

본 연구에서는 외국어 학습에서 상황인지(Situated Cognition)의 중요성을 탐색해 보고 상황인지 원리가 반영된 학습환경 중의 하나로 MOO(Multi-user Object Oriented)의 특성을 살펴보고자 하였다. 다른 분야에서도 그러하겠지만, 외국어 학습은 특히 학습해야 할 어휘 또는 표현법의 개념 이해를 넘어 이를 실제 활용할 수 있는 고차원적 수준으로 전개되어야 한다. 어린이가 실제 생활 속에서 주변 사람들과 상호작용하는 가운데 자연스럽게 모국어를 습득하듯이, 상황적 맥락이 충분히 제시되는 환경 속에서 외국어를 학습해야 이를 실제상황에서 십분 적용할 수 있는 가능성이 높아지는 것이다. 바로 이런 점에서 상황인지의 교육적 의의가 있다고 할 수 있다. 최근 교육 분야에서 관심을 모으고 있는 MOO는 텍스트 기반의 공간적 메타포(Spatial Metaphor)를 적용한 가상현실로서, 학습과정에 상황적 맥락을 제공하고 학습자의 상호작용을 촉진할 수 있다는 점에서 시사점이 매우 크다. 이에 본 연구에서는 MOO의 특성들을 활동중심, 맥락중심, 상호작용 측면에서 접근하여 외국어 학습환경으로서의 적용가능성을 제안하였다.

• 로봇학회논문지
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• 제16권4호
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• pp.336-344
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• 2021

This paper presents a robust deep learning-based human tracking framework in crowded environments. For practical human tracking applications, a target must be robustly tracked even in undetected or overcrowded situations. The proposed framework consists of two parts: robust deep learning-based human detection and tracking while recognizing the aforementioned situations. In the former part, target candidates are detected using Detectron2, which is one of the powerful deep learning tools, and their weights are computed and assigned. Subsequently, a candidate with the highest weight is extracted and is utilized to track the target human using a Kalman filter. If the bounding boxes of the extracted candidate and another candidate are overlapped, it is regarded as a crowded situation. In this situation, the center information of the extracted candidate is compensated using the state estimated prior to the crowded situation. When candidates are not detected from Detectron2, it means that the target is completely occluded and the next state of the target is estimated using the Kalman prediction step only. In two experiments, people wearing the same color clothes and having a similar height roam around the given place by overlapping one another. The average error of the proposed framework was measured and compared with one of the conventional approaches. In the error result, the proposed framework showed its robustness in the crowded environments.

• Journal of Animal Science and Technology
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• 제66권1호
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• 과학교육연구지
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• 제41권2호
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• pp.195-212
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• 2017

소집단 학습은 소집단 내 학생들 간의 상호작용을 통해 공동의 목표 및 과제를 해결해 나가는 학습 방법이다. 과학 교육에서 소집단 학습은 중요 학습 전략 중의 하나이며, 학업 성취도와 태도 향상에 효과적이다. 소집단 구성은 3명일 때 학생들이 적극적으로 참여하며 과학 탐구 능력 향상에 효과적인 것으로 보고되었다. 그러나 집단의 구성 방법 즉, 동질과 이질 집단으로 구성하느냐에 따라 그 효과가 다른 것으로 보고되고 있다. 따라서 이 연구에서는 집단의 구성에 따라 7학년 학생의 광합성 개념의 생태적 지위에 차이가 있는지를 검증하였다. 이를 위해서 7학년 학생 1107명을 대상으로 이질 집단과 동질 집단은 상위, 중위, 하위로 구분하여 구성하였다. 광합성 개념은 광합성 장소, 광합성 생성물질, 광합성 필요물질, 광합성 환경 요인 영역으로 구분하였다. 광합성 개념의 생태적 지위 변화에 대한 선행 연구에 기초하여 빈도율 4%이상인 개념을 선정하여 설문을 구성하였다. 설문지는 4가지 영역에 각각 제시된 관련 개념들에 대한 관련성 점수와 이해 수준 점수를 측정하였다. 이 연구의 결론은 다음과 같다. 1) 3명의 소집단 수업에서는 과학 개념 학습에 향상이 있었다. 2) 집단 구성 시 평균을 향상시키고자 할 때는 동질 집단으로 구성하고 구성원간의 편차를 줄이고자 할 때는 이질 집단으로 구성하는 것을 제안한다. 이 연구를 통해서 집단 구성에 따른 결과의 이중성에 대한 차이 또는 효과를 검증하는 구체적인 연구가 이루어지길 기대한다.

• 한국과학교육학회지
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• 제40권4호
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• pp.399-413
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• 2020

이 연구에서는 과학탐구실험 수업에서 나타나는 과학교사의 NOS-PCK를 분석하였다. 수도권에 소재한 고등학교에서 과학탐구실험을 담당하고 있는 4명의 과학교사가 연구에 참여하였다. 이들의 NOS 수업을 관찰하였고, 교수학습 자료를 수집하였으며, 반구조화된 면담을 실시하였다. 수집한 자료를 NOS-PCK의 다섯 가지 요소에 따라 분석하였다. 연구 결과, NOS와 관련된 교육과정에 대한 이해와 고려가 부족한 경우가 있었다. 그리고 주어진 탐구 활동이나 교과서의 구성이 NOS 교수에 효과적이지 않다고 생각하여 교육과정을 적극적으로 재구성하였다. 교수전략의 측면에서 교사들의 수업은 명시적인 접근에 가까웠으나 반성적인 접근은 대체적으로 부족하였다. 교사들은 NOS에 대한 견해가 개인의 주관적인 것이라거나 NOS가 인지적 학습의 영역이 아니라는 등의 이유를 들어 NOS에 대한 평가에 소홀한 모습을 보였다. 교사들은 평가 결과에 기반하기보다는 자신의 경험에 의존하여 학생들의 상태를 추측하였다. 마지막으로 NOS 교수에 대한 의지는 교사의 수업 전반에서 중요한 역할을 하였으며 교사들은 NOS 학습의 가치 중 특정 측면에 주목하였다. 이상의 결과를 바탕으로 NOS 교수에 대한 과학교사의 전문성을 높이기 위한 방안을 논의하였다.

• 한국수학교육학회지시리즈A:수학교육
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• 제44권4호
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• pp.603-625
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• 2005

Identity is the concept which approaches individuals' affective problems with the social and cultural view. The previous studies on the problems, studied the attitudes, beliefs, or emotions while they restricted the problems to teachers or students' private problems. Otherwise, identities focus on individuals which participate to any community and share its social practices(Mclead, 1994). This study purposed to get an understanding on the teaching and learning mathematics in elementary mathematics classroom with an ethnographic view, while we consider mathematics as a kind of social practices, and mathematics classrooms as communities of practice. We analysed teacher's identities on mathematics and teaching mathematics depending on her responses of the questions as following: How does she think about mathematics, what are the instructional goals in her mathematics classroom, how do students learn mathematics in her mathematics classroom. In addition, we analysed students' identities on mathematics and learning mathematics depending on their responses of the questions as following: What do students think of mathematics, do they like mathematics, why do they study mathematics, how do they feel their mathematics classroom(describe your classroom) and themselves in it(describe yourselves in your classroom), what are their duties and what do they do actually in their mathematics classroom.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.652-657
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• 2018

With the rapid development of e-commerce, many customers can now express their opinion on various kinds of product at discussion groups, merchant sites, social networks, etc. Discerning a consensus opinion about a product sold online is difficult due to more and more reviews become available on the internet. Opinion Mining, also known as Sentiment analysis, is the task of automatically detecting and understanding the sentimental expressions about a product from customer textual reviews. Recently, researchers have proposed various approaches for evaluation in sentiment mining by applying several techniques for document, sentence and aspect level. Aspect-based sentiment analysis is getting widely interesting of researchers; however, more complex algorithms are needed to address this issue precisely with larger corpora. This paper introduces an approach of knowledge representation for the task of analyzing product aspect rating. We focus on how to form the nature of sentiment representation from textual opinion by utilizing the representation learning methods which include word embedding and compositional vector models. Our experiment is performed on a dataset of reviews from electronic domain and the obtained result show that the proposed system achieved outstanding methods in previous studies.

• Smart Structures and Systems
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• 제6권3호
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

• 한국수소및신에너지학회논문집
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• 제32권6호
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• pp.592-611
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• 2021

In this study a rarely available high-throughput cycling data set of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles including in-cycle temperature and per-cycle IR measurements. We worked out own Python codes which reproduced the various data plots and machine learning approaches for cycle life prediction using early cycles and more details not presented in the article and the supplementary information. Particularly, we applied regularized ridge, lasso and elastic net linear regression models using features extracted from capacity fade curves, discharge voltage curves, and other data such as internal resistance and cell can temperature. We found that due to the limitation in the quantity and quality of the data from costly and lengthy battery testing a careful hyperparameter tuning may be required and that model features need to be extracted based on the domain knowledge.