• Communications of Mathematical Education
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• v.25 no.1
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• pp.47-61
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• 2011

The purpose of this study is to suggest a professional development system for elementary teachers who wish enhance mathematical teaching. The learning community on elementary mathematical teaching was composed of fourth grade teachers in a elementary school and an expert from education university. The activities was processed as establishing of objectives and contents of the learning community, discussing and seeing good lesson video, planning the lesson in collaboration with members, practicing the lesson, and reflecting on activities. To analyze these activities, record materials of meetings, lesson videos, member's writing were used. The results reported that the learning community lead teachers to search the method of professional development and showed itself as the effective media to enhance elementary mathematical teaching professionalism.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.57-66
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• 1994

In this paper, we present an iterative learning method of compensating for position sensor error. The previously known compensation algorithms need a special perfect position sensor or a priori information about error sources, while ours does not. to our best knowledge, any iterative learning approach has not been taken for sensor error compensation. Furthermore, our iterativelearning algorithm does not have the drawbacks of the existing interativelearning control theories. To be more specivic, our algorithm learns an uncertain function itself rather than its special time-trajectory and does not reuquest the derivatives of measurement signals. Moreover, it does not require the learning system to start with the same initial condition for all iterations. To illuminate the generality and practical use of our algorithm, we give the rigorous proof for its convergence and some experimental results.

• Journal of the Korean Society of Radiology
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• v.10 no.8
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• pp.637-643
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• 2016

Due to the popularity of artificial intelligent, medical image processing using artificial neural network is increasingly attracting the attention of academic and industry researches. Deep learning with a convolutional neural network has been proved to very effective representation of images. However, the training process requires high performance H/W platform. Thus, the realtime learning of a large number of high dimensional samples within low-power devices is a challenging problem. In this paper, we attempt to establish this possibility by presenting a realtime neural network method on Raspberry pi using online sequential extreme learning machine. Our experiments on high-dimensional dataset show that the proposed method records an almost real-time execution.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.599-607
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• 2021

Inactive region means a region filled with invalid pixel values to represent a specific image. Generally, inactive regions are occurred when the non-rectangular formatted images are converted to the rectangular shaped image, especially when 3D images are represented in 2D format. Because these inactive regions highly degrade the compression efficiency, filtering approaches are often applied to the boundaries between active and inactive regions. However, the image characteristics are not carefully considered during filtering. In the proposed method, inactive regions are padded through reinforcement learning that can consider the compression process and the image characteristics. Experimental results show that the proposed method performs an average of 3.4% better than the conventional padding method.

• ETRI Journal
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• v.46 no.2
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• pp.165-174
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• 2024

We propose a method to suppress the speckle noise and blur effects of the light field extracted from a hologram using a deep-learning technique. The light field can be extracted by bandpass filtering in the hologram's frequency domain. The extracted light field has reduced spatial resolution owing to the limited passband size of the bandpass filter and the blurring that occurs when the object is far from the hologram plane and also contains speckle noise caused by the random phase distribution of the three-dimensional object surface. These limitations degrade the reconstruction quality of the hologram resynthesized using the extracted light field. In the proposed method, a deep-learning model based on a generative adversarial network is designed to suppress speckle noise and blurring, resulting in improved quality of the light field extracted from the hologram. The model is trained using pairs of original two-dimensional images and their corresponding light-field data extracted from the complex field generated by the images. Validation of the proposed method is performed using light-field data extracted from holograms of objects with single and multiple depths and mesh-based computer-generated holograms.

• Journal of Korean Home Economics Education Association
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• v.26 no.1
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• pp.55-68
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• 2014

The study aims to develop teaching and learning media to raise up learners' interests and understanding during clothing class by providing a clothing swatch materials and its guide for applying to the unit of clothing in technology and home economics subject in middle school. To do so, clothing swatch materials, study sheet and guide were developed, applied and then analyzed for their effects. In this study, in order to figure out effects of class using clothing swatch materials on learning interest, learning necessity, learning understanding and academic achievements, the experiment was conducted for comparing and analyzing the learning interest, learning acceptance attitude and academic achievement between experiment group with using clothing swatch materials and control group without using the materials. The results of the study are as follows: First, the experiment group using clothing swatch materials shows higher learning interest than the control group without materials. Second, the experiment group showed higher learning acceptance attitude than the control group. Third, the experiment group achieved higher academic accomplishments than the control group.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.3
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• pp.89-109
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• 2018

This study investigates the influence of project-based learning method on the self-directed learning and problem-solving abilities of students taking the 'Media Center Management' course in Library Information Science (LIS). During this study, two tests measuring students' self-directed learning and problem-solving abilities were conducted, containing 48 items divided into 8 categories and 30 items divided into 5 steps of problem-solving processes, respectively. By utilizing the correspondence sample T-test during this study, statistically significant results were found in all categories of self-directed learning, excluding the 'self-understanding' category. In addition, significant differences were found in the 5 steps problem-solving processes as well. Subsequently, an in-depth interview was conducted, inquiring into the students' perspectives on the difficulty of attending classes, the content of lectures, the appropriateness of assignments, the validity of the evaluation method, the relationship with their team members, and the benefits acquired from completing the assignments. Finally, suggestions for future research were presented.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.511-518
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• 2022

Supervised learning based on deep learning has made a leap forward in various application fields. However, many supervised learning methods work under the common assumption that training and test data are extracted from the same distribution. If it deviates from this constraint, the deep learning network trained in the training domain is highly likely to deteriorate rapidly in the test domain due to the distribution difference between domains. Domain adaptation is a methodology of transfer learning that trains a deep learning network to make successful inferences in a label-poor test domain (i.e., target domain) based on learned knowledge of a labeled-rich training domain (i.e., source domain). In particular, the unsupervised domain adaptation technique deals with the domain adaptation problem by assuming that only image data without labels in the target domain can be accessed. In this paper, we explore the unsupervised domain adaptation techniques.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.305-321
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• 2004

With the advent of the digital broadcasting, the audiences can access a large number of TV programs and their information through the multiple channels on various media devices. The access to a large number of TV programs can support a user for many chances with which he/she can sort and select the best one of them. However, the information overload on the user inevitably requires much effort with a lot of patience for finding his/her favorite programs. Therefore, it is useful to provide the persona1ized broadcasting service which assists the user to automatically find his/her favorite programs. As the growing requirements of the TV personalization, we introduce our automatic user preference learning algorithm which 1) analyzes a user's usage history on TV program contents: 2) extracts the user's watching pattern depending on a specific time and day and shows our automatic TV program recommendation system using MPEG-7 MDS (Multimedia Description Scheme: ISO/IEC 15938-5) and 3) automatically calculates the user's preference. For our experimental results, we have used TV audiences' watching history with the ages, genders and viewing times obtained from AC Nielson Korea. From our experimental results, we observed that our proposed algorithm of the automatic user preference learning algorithm based on the Bayesian network can effectively learn the user's preferences accordingly during the course of TV watching periods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.180-181
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• 2019

This paper proposes a rate control algorithm for intra coding frame in HEVC encoder using a deep learning approach. The proposed algorithm is designed for CTU level bit allocation in intra frame by considering visual features spatially and temporally. Our features are generated using visual geometry group (VGG-16) with deep convolutional layers, then it is used for bit allocation per each CTU within an intra frame. According to our experiments, the proposed algorithm can achieve -2.04% Luma component BD-rate gain with minimal bit accuracy loss against the HM-16.20 rate control model.