• 한국컴퓨터정보학회논문지
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• 제22권1호
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• pp.153-159
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• 2017

In recent years there has been a rise in the use and interest of the flipped learning as a teaching and learning paradigm. The flipped learning model includes any use of Internet technology to enrich the learning in a classroom, so that a professor can spend more time interacting with students instead of lecturing. In the flipped model, students viewed video lectures online outside of class time. Students then performed two kinds of assignments, a teamwork assignment and an individual work assignment, through the class time. In this paper, we propose a flipped educational model for a college class. This experimental research compares class of college algorithm using the flipped classroom methods and the traditional lecture-homework structure and its effect on student achievement. The result data of mid-term exam and final exam were analyzed and compared with previous year data. The findings of this research show that there was not a significant difference in the scores of student between two lecturing methods. The survey result and lecture evaluation by students show that students are in favor of the flipped learning.

• 전자통신동향분석
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• 제36권5호
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• pp.82-91
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• 2021

This study reviews application of data-driven anomaly detection techniques to the aviation domain. Recent advances in deep learning have inspired significant anomaly detection research, and numerous methods have been proposed. However, some of these advances have not yet been explored in aviation systems. After briefly introducing aviation safety issues, data-driven anomaly detection models are introduced. Along with traditional statistical and well-established machine learning models, the state-of-the-art deep learning models for anomaly detection are reviewed. In particular, the pros and cons of hybrid techniques that incorporate an existing model and a deep model are reviewed. The characteristics and applications of deep learning models are described, and the possibility of applying deep learning methods in the aviation field is discussed.

• 한국정보시스템학회지:정보시스템연구
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• 제30권4호
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• pp.247-275
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• 2021

Purpose This study attempted to predict corporate innovation and business performance using ensemble learning. Design/methodology/approach The ensemble techniques uses weak learning to create robust learning, which combines several weak models to derive improved performance. In this study, XGboost, LightGBM, and Catboost were used among ensemble techniques. It was compared and evaluated with traditional machine learning methods. Findings The summary of the research results is as follows. First, the type of innovation is expanding from technical innovation to non-technical areas. Second, it was confirmed that LightGBM performed best for radical innovation prediction, and XGboost performed best for incremental innovation prediction. Third, Catboost performed best for firm performance prediction. Although there was no significant difference in predictive power between ensemble techniques, we found that comparative analysis was necessary to confirm better prediction performance.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제25권2호
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• pp.135-158
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• 2022

This study aimed to building models to understand the relationships between reasoning resources and content knowledge. We applied Support Vector Machine and linear models to the data including fifth graders' scores in the Cornel Critical Thinking Test and the Iowa Assessments, demographic information, and learning science approach (a student-centered approach to learning called the Science Writing Heuristic [SWH] or traditional). The SWH model showing the relationships between critical thinking domains and academic achievement at grade 5 was developed, and its validity was tested across different learning environments. We also evaluated the stability of the model by applying the SWH models to the data of the grade levels. The findings can help mathematics educators understand how critical thinking and achievement relate to each other. Furthermore, the findings suggested that reasoning in mathematics classrooms can promote performance on standardized tests.

• 한국수산과학회지
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• 제55권5호
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• pp.638-644
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• 2022

The measurement of morphological features is essential in aquaculture, fish industry and the management of fishery resources. The measurement of fish requires a large investment of manpower and time. To save time and labor for fish measurement, automated and reliable measurement methods have been developed. Automation was achieved by applying computer vision and machine learning techniques. Recently, machine learning methods based on deep learning have been used for most automatic fish measurement studies. Here, we review the current status of automatic fish measurement with traditional computer vision methods and deep learning-based methods.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.212-216
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• 2024

Feature Selection have turned into the main point of investigations particularly in bioinformatics where there are numerous applications. Deep learning technique is a useful asset to choose features, anyway not all calculations are on an equivalent balance with regards to selection of relevant features. To be sure, numerous techniques have been proposed to select multiple features using deep learning techniques. Because of the deep learning, neural systems have profited a gigantic top recovery in the previous couple of years. Anyway neural systems are blackbox models and not many endeavors have been made so as to examine the fundamental procedure. In this proposed work a new calculations so as to do feature selection with deep learning systems is introduced. To evaluate our outcomes, we create relapse and grouping issues which enable us to think about every calculation on various fronts: exhibitions, calculation time and limitations. The outcomes acquired are truly encouraging since we figure out how to accomplish our objective by outperforming irregular backwoods exhibitions for each situation. The results prove that the proposed method exhibits better performance than the traditional methods.

• 과학교육연구지
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• 제37권2호
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• pp.389-404
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• 2013

이 연구에서는 개방형 설문과 면담을 통하여 초등학교 교사가 과학 교수, 과학 학습, 과학의 본성에 대하여 가진 신념의 내용과 특징을 알아보고, 그들의 일관성을 분석하였다. 연구 결과, 전체 30명의 교사들 중 상대적으로 많은 수의 교사가 과학 교수, 과학 학습, 과학의 본성에 대하여 전통적인 신념을 가진 것으로 나타났다(각각 60%, 66.7%, 83.3%). 세 가지 측면에 대하여 '일관적 신념'을 가진 교사의 비율은 40%였고, 두 가지 측면에서 같은 신념을 보인 '연관적 신념'을 가진 교사는 53.3%, 세 가지 측면 모두 서로 다른 신념으로 이루어진 '확산적 신념'을 가진 교사는 6.7%였다. 또 일관적 신념을 가진 교사 중 83.3%는 전통적 신념 중 내용 지식 중심의 신념을 일관적으로 가지고 있었다. 즉 일관적 신념을 보인 교사의 비율이 40%로 적지는 않지만 대부분 전통적 내용 지식 중심의 신념을 가지고 있어 바람직한 신념 체계를 이루고 있다고 보기는 어렵다. 1980년대 이후 구성주의가 널리 강조되어왔음에도 불구하고 구성주의적으로 일관된 신념을 가진 교사의 비율(6.7%)은 낮은 편이어서 교사 신념의 구조에 대한 보다 심층적인 연구가 필요하다.

• 한국과학교육학회지
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• 제20권2호
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• pp.250-261
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• 2000

본 연구에서는 토론 과정에서의 사회적 합의 형성을 강조한 개념 학습 전략(SCS)을 개발하여 성취도, 개념 이해도, 의사소통 불안, 과학 학습 환경에 대한인식, 소집단 토론에 대한 인식 등의 측면에서 인지 갈등 유발 전략(CCS) 및 전통적 수업과 그 효과를 비교하였다. 성취도 점수에서는 유의미한 차이가 없었으나, 의사소통 능력 하위 학생들의 경우, 전통적 수업 집단에 비해 CCS 집단의 점수가 유의미하게 높았다. 개념 검사에서는 SCS 집단의 교정 평균이 다른 집단들에 비하여 높은 경향이 있었고, 개념 학습전략은 의사소통 능력이 뛰어난 학생들에게 더 효과적이었다. 의사 소통 불안에서는 세 집단 간에 차이가 없었다. 과학 학습 환경에 대한 인식의 경우, 개인적 적합성 영역이나 학생간의 협상 영역에서는 집단간 차이가 없었으나, 참여도 영역에서는 SCS 집단의 점수가 높았다. 또한, SCS 집단 학생들이 소집단 토론에 대해 보다 긍정적으로 인식하고 있었다.

• 방송공학회논문지
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• 제20권5호
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• pp.676-686
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• 2015

Light field 카메라를 이용하여 영상을 취득한 후 다양한 응용 프로그램으로 확장이 가능한 4차원 light field 영상은 일반적인 2차원 공간영역(spatial domain)과 추가적인 2차원 각영역(angular domain)으로 구성된다. 그러나 이러한 4차원 light field 영상을 유한한 해상도를 가진 2차원 CMOS 센서로 취득하므로 저해상도의 제약이 존재한다. 본 논문에서는 이러한 4차원 light field 영상이 가지는 해상도 제약 조건을 해결하기 위하여, 4차원 light field 영상에 적합한 딕셔너리 학습 기반(dictionary learning-based) 초해상도(superresolution) 알고리즘을 제안한다. 제안하는 알고리즘은 4차원 light field 영상으로부터 추출한 많은 수의 4차원 패치(patch)들을 바탕으로 딕셔너리를 구성 및 훈련하며, 학습된 딕셔너리를 바탕으로 저해상도 입력 영상의 해상도를 향상시키는 과정을 수행한다. 제안하는 알고리즘은 공간영역과 각영역의 해상도를 동시에 각각 2배 향상시킨다. 실험에 사용된 영상은 상용 light field 카메라인 Lytro에서 취득하였고 기존의 알고리즘과의 비교를 통해 제안하는 알고리즘의 우수성을 검증한다.