• 제목/요약/키워드: Learning presence

검색결과 369건 처리시간 0.027초

영역 특징 학습을 이용한 혀의 자동 영역 분리 및 한의학적 설진 시스템 (Automatic segmentation of a tongue area and oriental medicine tongue diagnosis system using the learning of the area features)

  • 이민택;이규원
    • 한국정보통신학회논문지
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    • 제20권4호
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    • pp.826-832
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    • 2016
  • 본 논문에서는 고가의 디지털 설진 장비와 특별한 장치 없이 누구나 손쉽게 사용할 수 있는 디지털 설진 시스템의 첫 단계로 미각 영역별 균열 유무를 판별하는 시스템을 제안한다. 훈련 DB는 한방 병원에서 수집한 사진 261장을 바탕으로 Haar-like feature, Adaboost 학습을 하였다. 학습된 결과를 통하여 입력영상으로부터 혀 후보영역을 검출하고, 검출된 혀 후보영역으로부터 혀 영역만을 분리하기 위하여 261장의 훈련 DB의 HSV 컬러모델의 Hue 성분 평균 값을 산출하였다. 검출된 혀 윤곽으로부터 Connected Component Labeling을 통하여 혀 영역을 분리 하였다. 분리된 혀 영역의 상대적 너비와 높이를 이용하여 미각 영역별 로 분할하였다. 분할된 미각 영역별 영상은 Gray영상으로 변환하고, 각각의 영역별 평균 밝기를 산출하여 이진화하였다. 이진화 영상에 Connected Component Labeling을 통하여 균열 유무를 판별하였다.

아동 교육 공간의 바이오필릭 디자인 패턴 적용 분석 (A Study on the Application of Biophilic Design Pattern in Educational space)

  • 최주영;박성준
    • 교육시설 논문지
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    • 제27권3호
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    • pp.3-14
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    • 2020
  • The purpose of this study is to discuss the planning direction of educational spaces to support children's healthy and creative learning based on bio_philic theory. This study analyzed the characteristics of the application of biophilic patterns in children's education space through case analysis. The conclusion of this study is summarized as follows. As a result of the analysis of children's classroom space, the pattern of 'A(Visual connection with nature), F(Dynamic & Diffuse Light), K(Prospect)' shows high application rate, but the pattern of 'C(Non-Rhythmic Sensory Stimuli), G(Connection with Natural Systems), I(Material Connection with Nature)' shows low application rate. In particular, there is a lack of connection with patterns such as hearing, smell, touch, taste stimulation and water experience, and curiosity through exploration of nature about 'B(Non-visual connection with nature), E(Presence of Water), N(Risk/Peril)' changes in nature and ecosystem. In the corridor and rest space, the pattern of 'A(Visual connection with nature), D(Thermal & Airflow Variability), F(Dynamic & Diffuse Light), G(Connection with Natural Systems), K(Prospect)' shows high application rate, but 'B(Non-visual connection with nature)' shows low application rate. In addition, the application of patterns related to the stimulation of curiosity through direct exploration of nature and the exploration of the patterns of 'E(Presence of Water), N(Risk/Peril)' is insufficient. Therefore, in the case of classroom spaces, the active use of nature as it is should be considered within the scope that does not cause visual confusion, and it should provide an area that can be experienced through the five senses. And corridors and rest spaces should be designed to introduce more active natural elements as spaces to recover stress caused by learning. In other words, the characteristics of children's education facilities need to be connected between classroom space, corridor, rest space and external space. This study is meaningful in that it analyzes and derives the application characteristics of 'biophilic design' which affects the 'Attention Restoration' of children's educational spaces through foreign cases.

다중 에이전트 시스템의 컨센서스를 위한 슬라이딩 기법 강화학습 (A slide reinforcement learning for the consensus of a multi-agents system)

  • 양장훈
    • 한국항행학회논문지
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    • 제26권4호
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    • pp.226-234
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    • 2022
  • 자율주행체와 네트워크기반 제어 기술의 발달에 따라서, 하나의 에이전트를 제어하는 것을 넘어서 다수의 이동체를 분산 제어하는데 사용 가능한 다중 에이전트의 컨센서스 제어에 대한 관심과 연구가 증가하고 있다. 컨센서스 제어는 분산형 제어이기 때문에, 정보 교환은 실제 시스템에서 지연을 가지게 된다. 또한, 시스템에 대한 모델을 정확히 수식적으로 표현하는데 있어서 한계를 갖는다. 이런 한계를 극복하는 방법 중에 하나로서 강화 학습 기반 컨센서스 알고리즘이 개발되었지만, 불확실성이 큰 환경에서 느린 수렴을 갖는 경우가 자주 발생하는 특징을 보이고 있다. 따라서, 이 논문에서는 불확실성에 강인한 특성을 갖는 슬라이딩 모드제어를 강화학습과 결합한 슬라이딩 강화학습 알고리즘을 제안한다. 제안 알고리즘은 기존의 강화학습 기반 컨센서스 알고리즘의 제어 신호에 슬라이딩 모드 제어 구조를 추가하고, 시스템의 상태 정보를 슬라이딩 변수를 추가하여 확장한다. 모의실험 결과 다양한 시변 지연과 왜란에 대한 정보가 주어지지 않았을 때에 슬라이딩 강화학습 알고리즘은 모델기반의 알고리즘과 유사한 성능을 보이면서, 기존의 강화학습에 비해서 안정적이면서 우수한 성능을 보여준다.

뇌기반 진화적 접근법에 따른 과학 야외학습이 초등학생들의 흥미와 성취도에 미치는 영향 (Analyses of Elementary School Students' Interests and Achievements in Science Outdoor Learning by a Brain-Based Evolutionary Approach)

  • 박형민;김재영;임채성
    • 한국초등과학교육학회지:초등과학교육
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    • 제34권2호
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    • pp.252-263
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    • 2015
  • This study analyzed the effects of science outdoor activity applying a Brain-Based Evolutionary (ABC-DEF) approach on elementary school students' interest and academic achievement. Samples of the study were composed of 3 classes of 67 sixth graders in Seoul, Korea. Unit of 'Ecosystem and Environment' was selected as a object of the research. Textbook- and teachers' guidebook-based instruction was implemented in comparison group, brain-based evolutionary approach within classroom in experimental group A, and science outdoor learning by a brain-based evolutionary approach in experimental group B. In order to analyze the quantitative differences of students' interests and achievements, three tests of 'General Science Attitudes', 'Applied Unit-Related Interests', and 'Applied Unit-Related Achievement' were administered to the students. To find out the characteristics which would not be apparently revealed by quantitative tests, qualitative data such as portfolios, daily records of classroom work, and interview were also analyzed. The major results of the study are as follows. First, for post-test of interest, a statistically significant difference between comparison group and experimental group B was found. Especially, the 'interests about biology learning' factor, when analyzed by each item, was significant in two questions. Results of interviews the students showed that whether the presence or absence of outdoor learning experience influenced most on their interests about the topic. Second, for post-test of achievement, the difference among 3 groups according to high, middle, and low levels of post-interest was not statistically significant, but the groups of higher scores in post-interest tends to have higher scores in post-achievement. It can be inferred that outdoor learning by a brain-based evolutionary approach increases students' situational interests about leaning topic. On the basis of the results, the implications for the research in science education and the teaching and learning in school are discussed.

학습동기가 시험 스트레스와 스트레스 대처 양식에 미치는 영향 (Effects of Learning Motivation on the Stress Coping Style and Stress of Test)

  • 장철;이은혜;천지수
    • 대한통합의학회지
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    • 제2권2호
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    • pp.89-96
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    • 2014
  • Purpose : In this study, we selected three research subjects to attempt to clear learning motivation of college students is what impact the stress coping style and stress test. First, age, gender, the future career after graduation, the presence or absence of part-time job is, its impact on learning motivation. Second, learning motivation is what effect the stress of the test. Third, based on the motivation of learning, how deal pursuing efforts form the social support, the center of the problem-solving approach, seeking to avoid the reaction to stress how different form. Methods : K University occupational therapy and one, two, three grade 100 students (male 22 people, female 78) to target age, sex, and after graduation, part-time status, motivation, stress, stress coping style questionnaire for distribution and was written. Results : First of all, women's social support form graduation course, more robust than pursue blank after the synchronization uncertainty and stress, and graduated from the trading center and avoid the use of career, more form. Second, motivation and stress test, a difference between the notice could not see. Third, the higher the motivation of learning, problem-solving, Action form to the center to use as many as you, but avoid using too much in the center form is addressed. Conclusion : As a result of the study that came out of the course after graduating from ensure that learning motivation is high, the more the uncertainty, the more to cope with stress in the center of the form to avoid form address was used. Because of this, the student careers after graduation, to make sure that can help you to compare efforts over is believed to be necessary.

비감독형 학습 기법을 사용한 심각도 기반 결함 예측 (Severity-based Fault Prediction using Unsupervised Learning)

  • 홍의석
    • 한국인터넷방송통신학회논문지
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    • 제18권3호
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    • pp.151-157
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    • 2018
  • 소프트웨어 결함 예측에 관한 기존의 연구들은 대부분 모델의 입력 모듈이 결함을 가지고 있는지 여부를 판단하는 이진 감독형 분류 모델들에 관한 것들이었다. 하지만 이진 분류 모델은 결함의 복잡한 특성들을 고려하지 않고 단순히 입력 모듈의 결함 유무만을 판단한다는 문제점이 있고, 감독형 모델은 대부분의 개발 집단이 보유하고 있지 않은 훈련 데이터 집합을 필요로 한다는 한계점이 있다. 본 논문은 이러한 두 가지 문제점을 해결하기 위해 비감독형 알고리즘을 사용한 심각도 기반 삼진 분류 모델을 제안하였으며, 평가 실험 결과 제안 모델이 감독형 모델들에 필적하는 예측 성능을 보였다.

Teachers' Perspectives on Obstacles Facing Gifted Students with Learning Disabilities in Saudi Arabia

  • Alsharif, Nawal;Alasiri, Hawazen
    • International Journal of Computer Science & Network Security
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    • 제22권4호
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    • pp.254-260
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    • 2022
  • The purpose of this study was to identify the obstacles facing gifted students with learning disabilities (GSLDs) from the point of view of their teachers in the Makkah region and to find suggested solutions to overcome these obstacles. The study covered Makkah, Jeddah and Taif and used semi-structured interviews which included open-ended questions. The study findings indicated that there were several educational obstacles including the absence of adapted courses or specialized teachers for GSLDs category and the insufficient time for the students to express their talents. According to the findings, there were also societal obstacles including the society's failure to expect the presence of talents along with disabilities, or its denial or rejection of their talents in addition to ridiculing them. The findings also confirmed the existence of administrative obstacles including the lack of community partnership. There were also family obstacles such as the family's lack of encouragement for the students, and ignorance of the nature of GSLDs. The study came up with a number of solutions and proposals related to awareness, educational institutions, education and competitions for talented people with learning disabilities.

Identification of shear transfer mechanisms in RC beams by using machine-learning technique

  • Zhang, Wei;Lee, Deuckhang;Ju, Hyunjin;Wang, Lei
    • Computers and Concrete
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    • 제30권1호
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    • pp.43-74
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    • 2022
  • Machine learning technique is recently opening new opportunities to identify the complex shear transfer mechanisms of reinforced concrete (RC) beam members. This study employed 1224 shear test specimens to train decision tree-based machine learning (ML) programs, by which strong correlations between shear capacity of RC beams and key input parameters were affirmed. In addition, shear contributions of concrete and shear reinforcement (the so-called Vc and Vs) were identified by establishing three independent ML models trained under different strategies with various combinations of datasets. Detailed parametric studies were then conducted by utilizing the well-trained ML models. It appeared that the presence of shear reinforcement can make the predicted shear contribution from concrete in RC beams larger than the pure shear contribution of concrete due to the intervention effect between shear reinforcement and concrete. On the other hand, the size effect also brought a significant impact on the shear contribution of concrete (Vc), whereas, the addition of shear reinforcements can effectively mitigate the size effect. It was also found that concrete tends to be the primary source of shear resistance when shear span-depth ratio a/d<1.0 while shear reinforcements become the primary source of shear resistance when a/d>2.0.

The ensemble approach in comparison with the diverse feature selection techniques for estimating NPPs parameters using the different learning algorithms of the feed-forward neural network

  • Moshkbar-Bakhshayesh, Khalil
    • Nuclear Engineering and Technology
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    • 제53권12호
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    • pp.3944-3951
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    • 2021
  • Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.

Deep-learning-based system-scale diagnosis of a nuclear power plant with multiple infrared cameras

  • Ik Jae Jin;Do Yeong Lim;In Cheol Bang
    • Nuclear Engineering and Technology
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    • 제55권2호
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    • pp.493-505
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    • 2023
  • Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.