• Educational Technology International
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• v.19 no.1
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• pp.35-60
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• 2018

The purpose of this study was to analyze the types of lesson and its effectiveness with digital textbook. To address those goals, we had observed five classes of the primary school, which designated as a research pilot school for digital textbook. Based on the result of observation, 3 types of lesson with digital textbook were categorized: Teacher-directed lecture (type 1), Blended learning (type 2), and Flipped learning (type 3). Depending on the type of lesson was analyzed the positive and negative effectiveness by means of matrix analysis method. As a result, in Teacher-directed lecture (type 1), there was found out the participation of the lesson in atmosphere of stable and comfortable as positive experience, also digital textbook operating immature and boring as negative experience. In Blended learning (type 2), there was found out the fun by sharing the product and peer feedback, and flow by learning transfer as positive experience, also digital textbook operating immature and understanding the difference between assignments as negative experience. In Flipped learning (type 3), there was shown the positive attitude and ownership in the lesson as positive experience, also distracting and boring in the lesson when learner was excluded in participation as negative experience. Based on the results, we suggested some strategies for improving positive experience and protecting negative experience in the lesson with using digital textbook.

• Journal of the Korean Chemical Society
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• v.46 no.3
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• pp.279-286
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• 2002

The purpose of this study was to investigate undergraduate students' preconception about the needed knowledge to understand organic reactions based on electron flow and undergraduate students' ability that understand fundamental chemical reaction based on electron flow, and was to offer pertinent teaching and learning method. For this study, 18 sophomores that majored in chemistry education of H University, were sampled. Test papers were newly developed that based on previous research. Undergraduate students' response was classified and reasons of the response were qualitatively analyzed by interview. According to the results of this study, it was found that undergraduate students had good understanding on the concept about the electron configuration of atoms and on the concepts about the needed knowledge to understand chemical reactions based on electron flow. But they didn't apply the concepts to the fundamental chemical reaction. Therefore, teaching and learning strategy that apply the needed concepts to understand chemical reactions based on electron flow to chemical reactions should be developed.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.687-697
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• 2023

Botnets, whose attack patterns are becoming more sophisticated and diverse, are recognized as one of the most serious cybersecurity threats today. This paper revisits the experimental results of botnet detection using autoencoder, a semi-supervised deep learning model, for UGR and CTU-13 data sets. To prepare the input vectors of autoencoder, we create data points by grouping the NetFlow records into sliding windows based on source IP address and aggregating them to form features. In particular, we discover a simple power-law; that is the number of data points that have some flow-degree is proportional to the number of NetFlow records aggregated in them. Moreover, we show that our power-law fits the real data very well resulting in correlation coefficients of 97% or higher. We also show that this power-law has an impact on the learning of autoencoder and, as a result, influences the performance of botnet detection. Furthermore, we evaluate the performance of autoencoder using the area under the Receiver Operating Characteristic (ROC) curve.

• Journal of Korea Technology Innovation Society
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• v.9 no.1
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• pp.149-174
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• 2006

Although conceptual and empirical researches on the technological learning is increasing rapidly, a few empirical researches of technological learning processes have been undertaken, taking into account a reality of learning processes of a firm. In order to analyze the learning processes of technological knowledges, based on integrative organizational learning theory, this study investigated technological learning processes by analyzing 13 technology development projects of one company with case study research design. Results of the empirical analyses suggested two taxonomy of technological learning processes. First are tour group of technological learning processes derived by the dimension of flow of cognitive and behavioral learning which is explained by the technological competency level of a firm. The other is two group of technological learning processes derived by the dimension of relative difficulty of cognitive and behavioral learning which is explained by the technology characteristics. Finally, the managerial implications for effective management of technological learning and limitations are discussed.

• The Journal of the Korea Contents Association
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• v.6 no.3
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• pp.108-115
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• 2006

e-Learning contents are composed of compounding multimedia data. It requires many professionals in contents development stage. The process of e-learning contents development can be seen as a collaborative work. In the perspective of a collaborative work process, the whole process of e-learning contents development would be regarded as collaborative work process for each participant as well as for whole group members. Most of collaborative works in contents development field are widely distributed. Members of work groups require workspaces for sharing information and communicating each other. In addition to workspaces, it also needs to support collaborative reflection such as planning for collaborative work and monitoring for work process. This paper is intended to develop the reflective collaborative work system for e-Learning contents development in order to support the systemic process of e-learning contents development. The reflective collaborative work system is composed of four supportive parts: work flow management, personal workspace, collaborative workspace, and collaborative reflection.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.547-553
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• 2021

Dynamic object recognition is an important task for autonomous vehicles. Since dynamic objects exhibit a higher collision risk than static objects, our own trajectories should be planned to match the future state of moving elements in the scene. Time information such as optical flow can be used to recognize movement. Existing optical flow calculations are based only on camera sensors and are prone to misunderstanding in low light conditions. In this regard, to improve recognition performance in low-light environments, we applied a normalization filter and a correction function for Gamma Value to the input images. The low light quality improvement algorithm can be applied to confirm the more accurate detection of Object's Bounding Box for the vehicle. It was confirmed that there is an important in object recognition through image prepocessing and deep learning using YOLO.

• Convergence Security Journal
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• v.24 no.2
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• pp.47-53
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• 2024

The traditional personal information life cycle model, primarily tailored to conventional systems, is inherently unsuitable for comprehending the nuances of personal information flow within artificial intelligence frameworks and for formulating effective protective measures. Therefore, this study endeavors to introduce a personal information life cycle model specifically designed for artificial intelligence (AI). This paper presents a personal information life cycle model suitable for artificial intelligence, which includes the stages of collection, retention, learning, use, and destruction/suspension, along with the re-learning process for destruction/suspension. Subsequently, we compare the performance of these existing models (such aspersonal information impact assessment and the ISMS-P model) with the newly proposed model. This underscores the superiority of our proposed model in comprehensively understanding the personal information flow in AI and establishing robust protective measures.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.369-384
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• 2014

Since the start of gifted education in 2002, educational support system has now been established, and sufficient growth in quantitative aspects has been achieved in Korea. On the other hand, they report that there are insufficient points in terms of education quality. In other words, most of the gifted education simply expands knowledge by prior-learning. In order to improve the quality of gifted education, they should enhance critical-thinking and creativity able to apply interdisciplinary principles or phenomena for solving problems. In this study, we designed and developed a convergent teaching-learning materials based on the concept of integrated education, which explore the process that basic logic operations such as AND, OR, XOR do the role of computer cells. A survey result showed that student satisfaction(usefulness, understanding, interest) of the materials is significantly higher than that of other traditional learning topics, and the design intent was met.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.600-602
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• 1995

The paper presents neural network control techniques for load frequency control of two area power system. Using learning algorithm of error back propagation after learning accept input on the optimal control $e_{i}$, $\dot{e}_{i}$, and $u_{i}$ frequency characteristic and tie-line load flow characteristic investigated dynamic. From result simulation, frequency deviation and tie-line load flow deviation have reduction remarkable.