• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1239-1249
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• 2018

Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.555-560
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• 2006

In contrast with the earlier remote education that simply recorded off-line classes, modern remote education emphasizes on offering additional functions that could maximize learning efficiency. Usage of such multimedia information as the texts, graphics, sounds, animations is considered fundamental element in offering the additional functions. This paper designs and implements an encoder/decoder that could accommodate the multimedia information with emphasis on demonstrating execution files. Instructors can demonstrate my type of execution files or application data files, and the remote learners can freely try running the corresponding execution files by themselves. Consequently, a high-level of learning efficiency can be achieved by the proposed encoder/decoder.

• Journal of the Korean Society of School Health
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• v.36 no.2
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• pp.21-30
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• 2023

Purpose: The study aimed to identify factors that affect college students' learning immersion in non-face-to-face remote classes. Methods: During COVID-19, a survey was conducted on 140 college students who were taking non-face-to-face remote courses at universities located in Seoul, Gyeonggi-do, and Chungcheong-do, Korea. Data were analyzed using the Pearson correlation coefficients, Independent t-test, ANOVA, and Hierarchial stepwise multiple regression with SPSS (Windows version 27.0). Results: In the study, the most important variable influencing learning immersion was the student's self-efficacy, followed by instructor presence, class participation, lecture satisfaction, and credits. Conclusion: Instructors who teach major courses at college need to develop and apply ways to enhance learners' self-efficacy and class content that can boost learners' motivation in order to maximize learners' learning immersion. In order to facilitate learners' access to online media and maintain their interest in remote classes, passionate efforts need to be made by active instructors.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1685-1695
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• 2021

To generate a deep learning model with high performance, a large training dataset should be required. However, it requires a lot of time and cost to generate a large training dataset in remote sensing. Therefore, the importance of transfer learning of deep learning model using a small dataset have been increased. In this paper, we performed transfer learning of trained model based on open datasets by using orthoimages and digital maps to detect changes of building objects in multitemporal orthoimages. For this, an initial training was performed on open dataset for change detection through the HRNet-v2 model, and transfer learning was performed on dataset by orthoimages and digital maps. To analyze the effect of transfer learning, change detection results of various deep learning models including deep learning model by transfer learning were evaluated at two test sites. In the experiments, results by transfer learning represented best accuracy, compared to those by other deep learning models. Therefore, it was confirmed that the problem of insufficient training dataset could be solved by using transfer learning, and the change detection algorithm could be effectively applied to various remote sensed imagery.

• Journal of Power Electronics
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• v.10 no.6
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• pp.604-610
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• 2010

Experiments in electrical engineering should mirror the key components of successful research and development: Understand the basic theory needed, test the resulting concepts by simulation and verify these, finally, in the experiment. For optimal learning outcome continuous monitoring of the progress of each individual student is necessary, immediately repeating those subjects which have not been learned successfully. Classically, this is the task of the teacher. In case of remote-controlled experiments this monitoring process and the repetition of subjects should be automated for optimal learning outcome. This paper describes a remote-controlled experiment combining theory, simulation and the experiment itself with an automated monitoring process. Only the evaluation of the experimental results and their comparison to the simulation results has to be checked by a teacher. This paper describes the details of the educational structure for a remote-controlled experiment introducing active filtering of harmonics. For better understanding the content of the learning material (theory and simulation) as well as the results of the experiment and the underlying booking system are shortly presented.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.473-480
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• 2006

Recently, remote education systems for web-based teaching-studying are rapidly increased. Also, a request for customized courseware suitable for individual learner's level and learning pattern is increasing. But, most remote education systems do not provide customized learning service fit for each learner's level and lots of learners easily lose their interest in studying. Therefore, a lot of researchers have tried to provide personalized customized learning service by analyzing leaner's level and learning pattern automatically with agents. In this paper, we designed and implemented a customized courseware for studying the computer application. There are four agents such as professor, assistant, student, and monitor agent in CCA and they cooperate with each other to provide learning contents suited to each learner's level.

• The Journal of Information Systems
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• v.2
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• pp.194-206
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• 1993

Intelligent remote learning system is a system that incorporate communication technology and others : a database engine, an intelligent tutorial system. Learners can study by themselves through the intelligent tutorial system. The existence of a communication, database and artificial intelligence enhance the capability of IRLS. According to Parsaye, an intelligent databases should have the following features : 1) Knowledge discovery. 2) Data integrity and quality control. 3) Hypermedia management. 4) Data presentation and display. 5) Decision support and scenario analysis. 6) Data format management. 7) Intelligent system design tools. I hope that this research of framework for IRLS paves for the future research. As mentioned in the above, the future work will include an intelligent database, self-learning mechanism using neural network.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.210-212
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• 2003

Active sensors are rapidly conquering a share on the remote sensing market and offer among others new possibilities toward automatically acquiring 3D building data. Better dissemination of information about new technological developments can possibly be achieved by short distance-learning courses. The paper describes the didactic and technical aspects of a course we have designed and conducted on airborne laser scanning and interferometric SAR. The building extraction application is a good example to illustrated the added value of short electronic-learning courses above simply publishing (digital) papers.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.245-251
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• 2022

In the context of the pandemic, educational institutions had to ensure an instant transition to remote technological models of communication within the new conditions of the educational environment. The purpose of the academic paper lies in determining the role of the communicative model of educational transformations in the realities of (post) modernity. The research methodology is based on a survey of 120 students from 10 higher educational institutions (HEIs) of Ukraine through an online form regarding the importance of live communication during a pandemic. Results. The communicative model changed significantly during the pandemic - the interaction was mainly due to technologies. The research has identified four communication models of educational transformations under the conditions of the pandemic, depending on learning models. The first traditional model of distance learning involves distance learning; the second model involves contact remote training using remote educational technologies; the third model is blended learning, which combines remote and traditional learning formats, synchronous and asynchronous modes of interaction; the fourth model is traditional contact training. The empirical study of the effectiveness of communication models proves that live communication remains extremely important for learning and understanding of educational materials by students, and technology has provided support for such communication. Along with this, seminars and video lectures with presentations combining live communication and communication technologies are as important as digital learning tools. The most effective teaching method for mastering and memorizing educational material was a live dialogue with a teacher at seminars in ZOOM, followed by individual written assignments on the studied topic.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.819-834
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• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.