• Journal of Information Processing Systems
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• 제8권2호
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• pp.241-262
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• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.

• Journal of information and communication convergence engineering
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• 제16권2호
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• pp.106-113
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• 2018

Although deep learning methods of convolutional neural networks (CNNs) and long-/short-term memory (LSTM) are widely used for text categorization, they still have certain shortcomings. CNNs require that the text retain some order, that the pooling lengths be identical, and that collateral analysis is impossible; In case of LSTM, it requires the unidirectional operation and the inputs/outputs are very complex. Against these problems, we thus improved these traditional deep learning methods in the following ways: We created collateral CNNs accepting disorder and variable-length pooling, and we removed the input/output gates when creating bidirectional LSTMs. We have used four benchmark datasets for topic and sentiment classification using the new methods that we propose. The best results were obtained by combining LTSM regional embeddings with data convolution. Our method is better than all previous methods (including deep learning methods) in terms of topic and sentiment classification.

• 대한임베디드공학회논문지
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• 제16권5호
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• pp.215-223
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• 2021

3D data can be categorized into two parts : Euclidean data and non-Euclidean data. In general, 3D data exists in the form of non-Euclidean data. Due to irregularities in non-Euclidean data such as mesh and point cloud, early 3D deep learning studies transformed these data into regular forms of Euclidean data to utilize them. This approach, however, cannot use memory efficiently and causes loses of essential information on objects. Thus, various approaches that can directly apply deep learning architecture to non-Euclidean 3D data have emerged. In this survey, we introduce various deep learning methods for mesh and point cloud data. After analyzing the operating principles of these methods designed for irregular data, we compare the performance of existing methods for shape classification and segmentation tasks.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 가을 학술논문 발표대회
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• pp.121-122
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• 2020

Universities that offer architectural engineering programs in Korea are making efforts to introduce innovation teaching methods to cultivate teamwork, creativity, flexibility of thought and practical skills needed for the Fourth Industrial Revolution. However, there is a lack of specific measures to support them. In this regard, this study investigated a method of application development for building construction courses applied with the innovation teaching methods. It mainly focused on 'improvement directions for existing learning management systems' and 'online learning support plans using the innovation teaching method' as research contents. It is expected that these improvement directions can be applied to the field of education through the development of mobile and web-based applications. In the follow-up research, the development of specific software for field application will be carried out.

• 한국문헌정보학회지
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• 제41권4호
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• pp.251-271
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• 2007

본 연구는 음독, 묵독, 다독, 정독, 통독, 발췌독 등의 독서방식이 자기주도적 학습능력에 미치는 영향이 학년별로 어떤 차이가 있는지 파악하기 위한 것이다. 자기주도적 학습능력을 검증하기 위해 초등학교 2학년, 4학년, 6학년 12개 반의 전체 어린이 286명을 대상으로 자기주도적 학습능력 특성 검사지 결과와 질문지로 조사한 독서방식을 사용하여 분석하였다. 연구 결과 모든 어린이들에게 자기주도적 학습능력에 미치는 영향이 가장 큰 독서방식은 정독인 것으로 나타났다. 학년별 자기주도적 학습능력에 가장 많은 영향을 미치는 독서방식은 2학년의 경우에는 통독, 4학년과 6학년 어린이의 경우에는 정독인 것으로 나타났다.

• 한국환경교육학회지:환경교육
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• 제19권3호
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• pp.40-56
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• 2006

The purpose of this study is to enhance elementary students' awareness of environment-friendly life and help them to prepare for a better life in the future. To achieve this purpose we examined the effect typical environmental experience-based learning activities, which were based on the local circumstances with high environmental-educational potential, have on the attitudes toward environment-friendly life. This study was carried out on the basis of typical environmental experience-based learning in the small class size. The research group used was composed of one sixth grade elementary school class called Sangroksu, whose total students were 9. The research period lasted from March 2005 to February 2006. To analyze the result of this study, two research methods were applied simultaneously : quantitative research methods and qualitative research methods. Especially statistical analysis in quantitative research methods by self-administrated questionnaire was done with SAS program. Qualitative research methods were analyzed in a cyclic pattern, including the processes of domain analysis, classification analysis, and factor analysis which continued to be associated with data-collecting methods. This research shows the following results. First of all, students have shown meaningful differences after typical environmental experience-based learning activities.(p<.05). Followings are fields of the differences - students‘ interest on the subject, their understanding levels of necessity for basic environmental facilities around us as well as for the kinds of environmental experience-based learning, awareness levels of various environmental problems, consciousness on environment conservation, and the practicing ability of environment - friendly lifestyles. Secondly, We have discovered improvements in the following fields after this study - the knowledge and understanding levels on our environment and human relationships, students' fundamental abilities to work out environmental problems, right ideas and appropriate attitudes on environment protection, the practicing ability of environment-friendly life styles, and their parents' understanding levels on the education related to environment. In conclusion, typical environmental experience-based learning activities have a positive effect on the improvement of elementary school students' environment-friendly life styles.

• 한국콘텐츠학회논문지
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• 제11권2호
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• pp.477-486
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• 2011

본 논문의 목적은 대학에서 모바일 기기를 활용한 수업에서 어떠한 형태의 교수 학습 활동이 이루어지고 있는지를 분석하고 유형화하는 것이다. 모바일 기기를 제공하였을 때 교수학습 활동이 기존의 방법과 어떻게 다른가 하는 점이 본 논문의 주요 논점이었으며, 수업에 주로 활용된 교수 방법, 교수 전략, 활용 기기, 평가 방법 등이 분석의 기준이 되었다. 또한 구체적인 교수 학습 활동에 대해 심층면담을 통해 분석 결과를 뒷받침하고자 하였다. 본 논문의 대상 교과목에서는 강의식보다 사례 분석식 교수방법, 교수 학습 전략 면에서는 구체적인 사례에 대한 설명과 질문에 대한 피드백을 제공하는 전략이 사용되었다. 학습자활동 면에서는 발표와 질문이 많이 활용되었고, 자료 면에서는 그래픽이나 인터넷 자료 등이 다수 활용되었으며, 평가 면에서는 과제 해결을 통한 평가 방법이 주로 활용되었다. 이러한 결과는 향후 대학에서 모바일 기기를 활용한 수업에서 교수 학습 모형을 개발하기 위한 기초자료가 될 것이다.

• 대한물리치료과학회지
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• 제28권1호
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• pp.62-75
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• 2021

Background: The world has entered the age of biotechnology and artificial intelligence, and encouraging students to test the value of information and knowledge ie to become information fluent, is becoming more important. The education system is also changing in order to adapt to the times. As a part of this, the cultivation of creative talent is a core goal of many nation states, and Israel's Jewish education methods are attracting attention; havruta (or chavrusa) is one such method. This study aims to effects of havruta class on learning attitudes and class satisfaction in a class of college physical therapy students. Design: Pretest-posttest design. Methods: The subjects were 95 students in College A. The learning attitudes questionnaire were used by the Korea Educational Development Institute, and the class satisfaction questionnaire before and after intervention. Results: The results showed significant differences in learning habits about physical therapy of learning attitudes (p<.05) and class methods and contents attention and understanding (p<.05), class interest of class satisfaction (p<.05). Conclusion: These results suggest that havruta class improves learning attitudes and class satisfaction. Therefore, follow-up study is needed to apply the havruta class in various students and teaching methods.

• ETRI Journal
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• 제42권2호
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• pp.292-304
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• 2020

As side-channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side-channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning-based side-channel analysis. In particular, we outline how deep learning is applied to side-channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

• 로봇학회논문지
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• 제18권3호
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• pp.352-357
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• 2023

Since robotic assembly in an unstructured environment is very difficult with existing control methods, studies using artificial intelligence such as reinforcement learning have been conducted. However, since long-time operation of a robot for learning in the real environment adversely affects the robot, so a method to shorten the learning time is needed. To this end, a method based on a pre-trained neural network was proposed in this study. This method showed a learning speed about 3 times than the existing methods, and the stability of reward during learning was also increased. Furthermore, it can generate a more optimal policy than not using a pre-trained neural network. Using the proposed reinforcement learning-based assembly trajectory generator, 100 attempts were made to assemble the power connector within a random error of 4.53 mm in width and 3.13 mm in length, resulting in 100 successes.