• Journal of Information Processing Systems
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• v.8 no.2
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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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• v.16 no.2
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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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.

• Journal of the Korean Society for Library and Information Science
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• v.41 no.4
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• pp.251-271
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• 2007

The purpose of this study is to investigate grade differences in the effect of reading methods - "oral reading" "silent reading", "intensive reading", "extensive reading" "thorough reading", "selective reading" - influences on the self-directed learning ability. The data were collected by using 12 classes of 2nd, 4th, 6th-grade, 286 children of an elementary school. The influences according to reading methods on the self-directed learning ability were surveyed through the self-directed learning ability test and through questionnaire. Out of reading methods, "intensive reading" had significant influence on the self-directed learning ability Out of reading methods of 4th and 6th-grade children, "intensive reading" had the most influence on the self-directed learning ability. However. out of reading methods of 2nd-grade children "thorough reading" had most influence on the self-directed learning ability.

• Hwankyungkyoyuk
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• v.19 no.3
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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.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.477-486
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• 2011

The purpose of this paper was to examine the teaching and learning activities using mobiles devices in a university. We analyzed the instructional methods, instructional strategies, devices types, and evaluation activities. In addition, we conducted deep interviews with the students who used the mobile devices in terms of their understanding on the mobile learning, mobile learning methods, evaluation methods, difficulties and their expectations. These findings will be used to improve the quality of the teaching and learning methods using mobile devices in higher education.

• Journal of Korean Physical Therapy Science
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• v.28 no.1
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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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• v.42 no.2
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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.

• The Journal of Korea Robotics Society
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• v.18 no.3
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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.