• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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• pp.61-68
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• 2000

Now that the complete genomes of numerous organisms have been ascertained, key problems in molecular biology include determining the functions of the genes in each organism, the relationships that exist among these genes, and the regulatory mechanisms that control their operation. These problems can be partially addressed by using machine learning methods to induce predictive models from available data. My group is applying and developing machine learning methods for several tasks that involve characterizing gene regulation. In one project, for example, we are using machine learning methods to identify transcriptional control elements such as promoters, terminators and operons. In another project, we are using learning methods to identify and characterize sets of genes that are affected by tumor promoters in mammals. Our approach to these tasks involves learning multiple models for inter-related tasks, and applying learning algorithms to rich and diverse data sources including sequence data, microarray data, and text from the scientific literature.

• 교육녹색환경연구
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• 제19권1호
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• pp.13-24
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• 2020

본 연구는 미래 교수-학습방법 유형과 학습 공간 유형을 분석하여 교수-학습방법 유형에 적합한 공간 유형을 연결(matching)해보고, 공간 유형별 교수-학습방법과 학습양식의 활용정도를 학교급별로 제안하고자 하였다. 그 결과, 플립 러닝, 디퍼 러닝, 협력 학습, 몰입형 가상현실 학습, 놀이 학습, OER활용 학습 등 6가지의 교수-학습방법 유형과 놀이 및 탐구 공간, 제작 및 거치 공간, 발표 및 전시 공간, 독립된 학습 공간, 학습의 장으로서의 교실, 학습의 내용으로서의 교실 등 6가지의 학습 공간 유형을 도출하였다. 아울러 Table 12와 13과 같이 미래 교수-학습방법 유형에 적합한 공간 유형을 연결해보고, 공간 유형별 교수-학습방법뿐만 아니라 22가지 학습양식의 활용정도를 학교급 또는 학년군별로 제시하였다.

• 한국전자통신학회논문지
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• 제14권4호
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• pp.701-706
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• 2019

최근에는 인공지능의 도래로 인하여 수많은 산업과 일반적인 응용에 적용됨으로써 우리의 생활에 큰 영향을 발휘하고 있다. 이러한 분야에 다양한 기계학습의 방식들이 제공되고 있다. 기계학습의 한 종류인 지도학습은 학습의 과정 중에 특징값과 목표값을 입력으로 가진다. 지도학습에도 다양한 종류가 있으며 이들의 성능은 입력데이터인 빅데이터의 특성과 상태에 좌우된다. 따라서, 본 논문에서는 특정한 빅 데이터 세트에 대한 다수의 지도학습 방식들의 성능을 비교하기 위해 텐서플로우(Tensorflow)와 사이킷런(Scikit-Learn)에서 제공하는 대표적인 지도학습의 방식들을 이용하여 파이썬언어와 주피터 노트북 환경에서 시뮬레이션하고 분석하였다.

• 대한물리치료과학회지
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• 제31권2호
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• pp.90-104
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• 2024

Background: Hybrid learning is an educational approach that combines the teaching methods of online and lecture-style classes to compensate for each method's strengths and weaknesses. Compared to lecture-style classes, flipped learning improves overall class satisfaction and self-directed learning but is associated with lower learning motivation. It is necessary to determine whether hybrid flipped learning can solve the learning motivation problem of flipped learning by incorporating flipped learning into hybrid learning. The purpose of this study is to compare the effects of hybrid flipped learning and flipped learning on students' learning ability. Design: Cross-sectional study Methods: For students in the Department of Physical Therapy, classes were conducted using both flipped learning and hybrid flipped learning. In both learning methods, students took online classes first and participated in them every week. Flipped learning classes was conducted offline at school every week, while hybrid flipped learning alternated between live classes on YouTube and offline classes at school every other week. Results: Hybrid flipped learning resulted in significantly lower learning satisfaction and course evaluation than flipped learning, with no significant difference in grades. Conclusion: Hybrid flipped learning was able to cope with the situation well with the non-face-to-face teaching method caused by COVID-19, but it was difficult to improve learning ability because there were restrictions on activities that could interact with students. Flipped learning is a smooth offline activity that enables two-way activities between professors and students to improve learning ability, but the effect of improving test scores is still unclear.

• 공학교육연구
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• 제16권5호
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• pp.24-35
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• 2013

This study aims to explore self-directed learning strategies of high academic achievers majoring in engineering. The research participants were 21 fourth-year students who had attained the first or second highest cumulative grade point average in each department during the past three-year and were asked to write an essay on "my successful learning methods or techniques." The essays were analyzed by theme analysis method which is one of the qualitative methods to extract the self-directed learning strategies used by high performing students. According to the results of this study, the self-directed learning strategies of excellent students could be categorized into fundamental strategies to induce self-directed learning, preparatory strategies, implementation strategies and management strategies for marinating self-directed learning. Detail information on each category is as follow: 1) fundamental strategies refer to positive and pleasant mind, academic confidence and effort attribution, 2) preparatory strategies refer to concrete and challenging goal setting, establishment of learning strategies adjusted courses characteristics and practical learning planning, 3) implementation strategies refer to intensive learning in class, knowledge exploration, knowledge acquisition, social networking and exhaustive preparation for exams and 4) management strategies refer to time management and learning environment management.

• 대한통합의학회지
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• 제6권4호
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• pp.63-73
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• 2018

Purpose : This study aims to verify the effects of flipped learning on self-directed learning and class satisfaction in a class of college physical therapy students. Methods : The subjects were 97 students in College A who had registered for musculoskeletal examination and assessment and practice at the second semester of 2017. All subjects were measured with the self-directed learning questionnaire for college student proposed by Lee et al., and the class satisfaction questionnaire proposed by Lee et al., before and after intervention. The collected data were processed using a computerized statistical program SPSS Win version 21.0. Mean, standard deviation, paired t-test and Cronbach's alpha coefficient were calculated. Results : The results showed significant differences in goal setting, identify resources for learning, effort attributed to results, self-reflection of self-directed learning and problem solving excellence, class methods and contents attention and understanding(p<.05), class interest of class satisfaction(p<.05). Conclusion : These results suggest that flipped learning improves learning motivation and attitudes. Therefore, follow-up study is necessary to investigate further the application of flipped learning in various students and teaching methods.

• Investigative Magnetic Resonance Imaging
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• 제23권2호
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• 지능정보연구
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• 제25권4호
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• pp.53-65
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• 2019

영상 기반의 보안 시스템의 증가함에 따라 각 용도마다 다른 다양한 객체들에 대한 처리들이 중요해지고 있다. 객체 추적은 객체 인식, 검출과 같은 작업들과 함께 필수적인 작업으로 다뤄진다. 이 객체 추적을 달성하기 위해서 다양한 머신러닝이 적용될 수 있다. 성공적인 분류기로써 전체 에러율 최소화(total-error-rate minimization) 기반의 방법론이 사용될 수 있다. 이 전체 에러율 최소화 기반의 방법론은 오프라인 학습을 기반으로 하고 있다. 객체 추적은 실시간으로 처리하며 갱신해야하는 것이 필수적이므로 온라인 학습(online learning)을 기반으로 하는 것이 적합하다. 온라인 전체 에러율 최소화 방법론이 개발되었지만 점근적으로 재가중되는(approximately reweighted) 작업이 포함되어 에러를 누적시킬 수 있다는 단점이 있다. 본 논문에서는 정확하게 재가중되는(exactly reweighted) 방법론을 제안하면서 온라인 전체 에러율 최소화가 달성되었다. 이 제안된 온라인 학습 방법론을 객체 추적에 적용하여 총 8개의 데이터베이스에서 다른 추적 방법론들 보다 좋은 성능이 달성되었다.

• 한국수학교육학회지시리즈A:수학교육
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• 제45권4호
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• pp.493-505
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• 2006

This research studied learning model for the purpose of renovation of mathematics teaching methods. Especially, this research classified the types of cooperative learning, the theoretical background for cooperative learning, the need of cooperative learning in school mathematics, and the differences between cooperative learning and traditional small group learning, This research also suggested special features of cooperative learning and various types of cooperative learning models. The main types of cooperative learning which this research supported are TAI(Team-Assisted Individualization, JIGSAW cooperative learning, JIGSAW II cooperative learning, JIGSAW III cooperative learning, STAD(Student Team-Achievement division) cooperative learning, and TGT(Teams-Games-Tournament).

• 컴퓨터교육학회논문지
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• 제15권1호
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• pp.13-21
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• 2012

이 연구는 학생이 선호하는 학습 전략과 방법이 있다는 학습 양식을 전제로 교사가 수업에 적용할 수 있는 학습 양식에 따른 프로그래밍 교수 전략과 방법을 제안한다. 관련 연구들은 대부분 학습 양식집단이 특정 능력에 차이가 있음을 증명하는 연구들이기 때문에 교사가 수업을 설계하거나 실현할 때, 연구의 결과가 큰 도움을 주지 못한다. 따라서 실제 수업을 설계하고 실현할 때 도움을 주기 위한 교수전략과 방법에 대한 연구가 필요하다. 이에 컴퓨터 교육 전문가들로 구성된 전문가 설문을 통해 연구된 학습 양식에 따른 프로그래밍 교수 전략과 방법을 제안한다. 제안된 교수 전략과 방법의 유용성을 확인하기 위해 실험 집단을 구성하여 실제 프로그래밍 교육을 설계하여 실현하였다. 수업을 마친 후 성취도 평가를 실시하여, 그 결과 차이를 학습 양식 집단별로 검증하였다. 이 연구의 실험자의 수가 적고 적용기간이 짧다는 제한점이 있지만, 앞으로의 학습 양식에 기반한 교수 전략과 방법 연구에 좋은 사례 연구가 될 것이다.