• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Phonetics and Speech Sciences
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• v.12 no.4
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• pp.91-98
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• 2020

This study investigated the acoustic characteristics of sustained vowel /a/ and sentence utterance produced by patients with muscle tension dysphonia (MTD) using cepstrum-based acoustic variables. 36 women diagnosed with MTD and the same number of women with normal voice participated in the study and the data were recorded and measured by ADSVTM. The results demonstrated that cepstral peak prominence (CPP) and CPP_F0 among all of the variables were statistically significantly lower than those of control group. When it comes to the GRBAS scale, overall severity (G) was most prominent, and roughness (R), breathiness (B), and strain (S) indices followed in order in the voice quality of MTD patients. As these characteristics increased, a statistically significant negative correlation was observed in CPP. We tried to classify MTD and control group using CPP and CPP_F0 variables. As a result of statistic modeling with a Random Forest machine learning algorithm, much higher classification accuracy (100% in training data and 83.3% in test data) was found in the sentence reading task, with CPP being proved to be playing a more crucial role in both vowel and sentence reading tasks.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.83-91
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• 2011

The purpose of this thesis was to implement effective blended PBL(Problem-Based Learning) systems for information communication ethics education. Online learning and face-to-face classes were systematically combined for achieving the teaching-learning goals. And the main module for online learning run on Moodle, an open source learning management system. To examine educational effectiveness of the proposed systems, an experimental study was conducted through the method to the subject of two class in the second-grade of university located in ${\bigcirc}{\bigcirc}$ city. For experiment 60 students(treatment group=30, control group=30) are participated. And they were assigned to one of ten subgroups, comprising of six students, respectively. The results of this study are follows, First, the education using proposed blended PBL method is more effective in cultivating consciousness of information communication ethics than the education using face-to-face PBL learning method. Second, learners who participated in the proposed blended PBL more experienced various effects of PBL, such as (1) Improvement of problem solving ability, (2) Understanding of cooperative learning than the other learners who participated in the face-to-face PBL.

• Journal of the Korea Computer Graphics Society
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• v.16 no.1
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• pp.21-27
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• 2010

Today the lectures are usually practiced in a teacher-led traditional classroom system or a student-led e-learning system. Students passively follow the teacher's lectures in both systems, though. Also due to the advances in 3D Computer Graphics and Game technologies, there are trials to exploit the positive effect of games in learning. The serious games, specifically designed games for an educational goal, or existing games for a special class have been used as lectures. Still these games have a great difficulty in being integrated into the educational system technically and economically. Therefore a new 3D MMORPG based lecturing system is presented in this paper. In our new lecturing system, the characteristics of a 3D MMORPG, achievement, sociality, and immersion, are provided to motivate students to participate actively in a lecture. A teacher and students interact with each other in realtime as 3D characters in a 3D virtual classroom on-line. An ordinary teacher can also easily apply our new system to existing classes since a teacher only needs to specify a slide file to prepare a lecture. For the future work, a user study and the effect of our new lecturing system will be performed.

• The Journal of Korean Association of Computer Education
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• v.9 no.5
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• pp.65-75
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• 2006

With rapid developments in technology and communications' many teachers are increasingly exposed to a variety of e-Learning environments that they must develop new competencies and skills to be successful e-Learning teachers. The purpose of this training program, sponsored by HRD Korea (Human Resources Development Services of Korea), is to provide e-Learning teachers with meaningful opportunities for promoting quality e-Learning teaching. This program covers pedagogical issues as well as technical and practical aspects of the e-Learning environments. Before starting the program development the survey and the current e-Learning program assessments were conducted. The training program is divided into three modules as follows: 1) theoretical issues of e-Learning, 2) development of e-Learning contents, and 3) implementation of e-Learning environments. These three modules can be selectively reorganized in response to teacher requirements and demands. ln each module, there are five subtopics that include creative teaching and interaction strategies for promoting the effective e-Learning teaching. ln conclusion, teachers will gain greater understanding of teacher roles in e-Learning instruction, more flexibility in teaching jobs, increased confidence and knowledge to act as e-Learning facilitators through the completion of this training program.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1012-1015
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• 2021

Covid-19 로 인한 마스크 착용이 청각장애인들의 소통을 더 어렵게 하는 바, 제 3 자의 도움 없이 쌍방향 소통을 가능하게 하는 서비스의 필요성이 커지고 있다. 이에 본 논문은 소통의 어려움을 겪는 청각장애인과 비청각장애인을 위한 쌍방향 소통 서비스에 대한 연구와 개발 과정, 기대 효과를 담는다. 서비스는 GRU-CNN 하이브리드 아키텍처를 사용하여 데이터셋을 영상 공간 정보와 시간 정보를 포함한 프레임으로 분할하는 영상 분류 기법과 같은 딥 러닝 알고리즘을 통해 수어 영상을 분류한다. 해당 연구는 "눈속말" 모바일 어플리케이션으로 제작 중이며 음성을 인식하여 수어영상과 텍스트로 번역결과를 제공하는 청각장애인 버전과 카메라를 통해 들어온 수어 영상을 텍스트로 변환하여 음성과 함께 제공하는 비청각장애인 버전 두 가지로 나누어 구현한다. 청각장애인과 비장애인의 쌍방향 소통을 위한 서비스는 청각장애인이 사회로 나아가기 위한 가장 기본적인 관문으로서의 역할을 할 것이며 사회 참여를 돕고 소통이라는 장벽을 넘어서는 발돋움이 될 것이라 예측된다.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1671-1678
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• 2023

The Korea multi-purpose satellite (KOMPSAT) series consisting of multi-sensors has been used in various fields such as land, environmental monitoring, and disaster analysis since its first launch in 1999. Recently, as various information processing technologies (high-speed computing technology, computer vision, artificial intelligence, etc.) that are rapidly developing are utilized in the field of remote sensing, it has become possible to develop more various satellite image processing and analysis algorithms. In this special issue, we would like to introduce recently researched technologies related to the KOMPSAT image application and research topics participated in the 2023 Satellite Information Application Contest.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.89-97
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• 2021

Fault detection in seismic data is well suited to the application of machine learning algorithms. Accordingly, various machine learning techniques are being developed. In recent studies, machine learning models, which utilize synthetic data, are the particular focus when training with deep learning. The use of synthetic training data has many advantages; Securing massive data for training becomes easy and generating exact fault labels is possible with the help of synthetic training data. To interpret real data with the model trained by synthetic data, the synthetic data used for training should be geologically realistic. In this study, we introduce a method to generate realistic synthetic seismic data. Initially, reflectivity models are generated to include realistic fault structures, and then, a one-way wave equation is applied to efficiently generate seismic stack sections. Next, a migration algorithm is used to remove diffraction artifacts and random noise is added to mimic actual field data. A convolutional neural network model based on the U-Net structure is used to verify the generated synthetic data set. From the results of the experiment, we confirm that realistic synthetic data effectively creates a deep learning model that can be applied to field data.

• Journal of Korean Library and Information Science Society
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• v.53 no.3
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• pp.263-285
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• 2022

This study presented the factor analysis on constructing the new factors affecting the middle school students' online learning behaviors from the questionnaires employed among middle school students. A total of 204 students participated and the data were collected in South Korea. The sample of middle school ninth-grade students was selected and used through purposive sampling. Findings from the factor analysis provided evidence for the eight-factor solution for the 35-items accounting for 66.15% of the shared variance. A wide range of factors has been considered to identify students' online learning behaviors. The appropriate experience and use of e-learning in the middle school period is also important as it will be a critical stepstone for future education. This research provides information that has been taken into account for advancing online learning to enhance the quality of e-learning systems for middle school students. The study results provided eight new factors affecting the middle school students' online learning behaviors; that is 1) communication using social media as a learning tool, 2) intention to share information using ICT, 3) addiction of technology, 4) adoption of technology, 5) seeking information using ICT, 6) use of social media learning, 7) information search using ICT, and 8) immersion of technology. This study confirmed that middle school students prefer communication using social media as a learning tool, and value intention to share information using ICT for the most part. The data obtained based on factor analysis can highlight the online learning behaviors towards a mixture of social media learning and ICT to ensure a new educational platform for the future of e-learning. This research expects to be useful for both middle schools of online learning to better understand students' online learning behaviors and design online learning environments and information professionals to better assist students who particularly need digital literacy.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.59-68
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• 2009

This paper aims to explore the factors of learner's flow and to develop and validate a scale to measure the flow in Massive Multiplayer Online Role Playing Game(MMORPG) for education. First of all, potential factors were drawn through literature review. The potential stage comprises 6 factors(learner's psychological characteristics, learner's skill, importance of game, environment for learner, instructional design, and instructional environment) and 16 subfactors. With total 48 items developed. a survey was carried out among 293 elementary learners who had been participating in a commercial MMORPG for English skills to measure their flow in the MMORPG by utilizing the potential scale. Using the responses collected from 288 respondents, exploratory factor analysis, reliability analysis, and confirmatory factor analysis were performed. The expository factor analysis showed that items within each sub-factors could be bound into one factor. That is, the variables evaluating learner's flow were divided into six factors(learner's psychological characteristics, learner's skill, importance of game, environment for learner, instructional design, and instructional environment). And these factors were interpreted consisting of 16 sub-ones. Reliability estimates indicated that the evaluation tool had good internal consistency. The confirmatory factor analysis did confirm the model suggested by the expository factor analysis. Over fit measures(CFI, NFI, NNFI) showed the good suitability of the model. Findings of this study confirmed the validity and reliability of the scale to measure learner's flow in MMORPG.