• Journal of the Korean Institute of Educational Facilities
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• v.23 no.1
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• pp.43-54
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• 2016

The purpose of this study was to survey teachers' perception of classroom space utilization through analyzing the physical environment of elementary school classrooms (56 classrooms in 10 schools). Most of the teachers arranged desks in the two person parallel type (sectional layout : standard type) for their classes. Although the number was small, some classrooms used the T type, H type, U type, group type, and the teachers of such cases used these layouts for children's play activities or group learning. Some teachers changed the desk layout depending on the contents of learning or for different atmosphere of class, but about 40% of the teachers used the same classroom layout without any change during a semester. When the teachers' perception of classroom space utilization was examined according to the type and change of desk layout, the quantity and characteristics of posts, the position of posting spaces, and the size of activity spaces in the classroom, most of the teachers tended to be conventional without any characteristic, and only 16% of them were relatively active in utilizing classroom spaces. In addition, teachers of a relatively small class were more active in utilizing classroom spaces. In particular, perception was very low to utilize the classroom as a space for children's life or play activities or various types of learning. These findings suggest that it is necessary to improve teachers' perception of classroom space utilization in the future.

• Journal of Gifted/Talented Education
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• v.12 no.4
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• pp.1-25
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• 2002

This study investigated the background of the intellectual ability that consists of family environment, intellectual priority, career aims, intellectual extracurricular activities of the students who were selected for the Science Gifted Education Center. We performed a survey and analysis of the students at the Science Gifted Education Center in order to explore their learning attitudes and intellectual ability, as well as the elements that represent their attitudes. As a result of the study, I found that the students in the Science Gifted Education Center generally had good economic and family backgrounds that showed no significant hardship in supporting the students. The families were also highly educated intellectually. In case the talented students in Science desired to be pure scientists, they liked the Science subjects and enjoyed reading science books. It was mostly the male students that took part. As for the attitude toward intellectual ability, they expected to improve their abilities as they gained knowledge and increased their intelligence through learning, although they didn't confide with us on their intelligence quotients. There were four factors for these attitudes. They include the high expectation of them by others; confidence and ease of mind regarding the assignment; the expectation for improvement in intelligence and ability through learning; and, confidence in intelligence and positive attitude in the participation situatio

• Current Optics and Photonics
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• v.4 no.4
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• pp.286-292
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• 2020

Imaging through multicore fiber (MCF) is of great significance in the biomedical domain. Although several techniques have been developed to image an object from a signal passing through MCF, these methods are strongly dependent on the surroundings, such as vibration and the temperature fluctuation of the fiber's environment. In this paper, we apply a new, strong technique called deep learning to reconstruct the phase image through a MCF in which each core is multimode. To evaluate the network, we employ the binary cross-entropy as the loss function of a convolutional neural network (CNN) with improved U-net structure. The high-quality reconstruction of input objects upon spatial light modulation (SLM) can be realized from the speckle patterns of intensity that contain the information about the objects. Moreover, we study the effect of MCF length on image recovery. It is shown that the shorter the fiber, the better the imaging quality. Based on our findings, MCF may have applications in fields such as endoscopic imaging and optical communication.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1663-1676
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• 2022

Recently, a number of deep-learning based land cover segmentation studies have been introduced. Some studies denoted that the performance of land cover segmentation deteriorated due to insufficient training data. In this study, we verified the improvement of land cover segmentation performance through data augmentation. U-Net was implemented for the segmentation model. And 2020 satellite-derived landcover dataset was utilized for the study data. The pixel accuracies were 0.905 and 0.923 for U-Net trained by original and augmented data respectively. And the mean F1 scores of those models were 0.720 and 0.775 respectively, indicating the better performance of data augmentation. In addition, F1 scores for building, road, paddy field, upland field, forest, and unclassified area class were 0.770, 0.568, 0.433, 0.455, 0.964, and 0.830 for the U-Net trained by original data. It is verified that data augmentation is effective in that the F1 scores of every class were improved to 0.838, 0.660, 0.791, 0.530, 0.969, and 0.860 respectively. Although, we applied data augmentation without considering class balances, we find that data augmentation can mitigate biased segmentation performance caused by data imbalance problems from the comparisons between the performances of two models. It is expected that this study would help to prove the importance and effectiveness of data augmentation in various image processing fields.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.205-219
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• 2019

As the education and training is such a virtual environment is applied to various fields, its usability is endless. However, there is an underestimation of the depth of perception in the training environment. In order to solve this problem, we tried to solve the problem by applying the top-down correction method. However, it is difficult to classify the result as a learning effect or perception change. In this study, it was confirmed that the proportion of spatial components of urine had a significant effect on the depth perception, and it was confirmed that the size perception were corrected together. In this study, we propose a correction method using spatial component and depth perception to improve the accuracy of depth perception.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.63-80
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• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.4
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• pp.3-12
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• 2009

In recent years, the development of ubiquitous computing environment, according to the time, place, regardless of the environment changes dynamically based on providing a service. In particular, ubiquitous computing environment, education support services in the fields of education according to the principal of each member is required to provide personalized information. Therefore, this paper describes the education support system which provide adaptive information to member of education. The structure of the proposed system consists of mobile agents multi-agent system platform, JADE (Java Agent DEvelopment framework) is based. Also, we describes the design of agents for application services and the interaction model. In this paper, the performance of proposed system to verify availability, classroom teachers, students and parents and administrators as a service application based on the user's role to provide appropriate information system was implemented. Finally, we shows the result of user interface GUIs according to adaptive education services.

• Journal of English Language & Literature
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• v.54 no.1
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• pp.31-60
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• 2008

As a way to situate environmental literary study, urban nature recently seems to have become an increasingly important part of ecocritical studies. Considering the recent deprecation on the alleged ecocriticism's ecocentric position, this move looks promising. However, a scrutable review of recent publications of ecocritical studies reveals a contradicting result that an ecocritical approach to urban nature not only lacks substance but also makes too much of the cultural and political issues of 'environmental justice' in which the traditional value and beauty of nature is totally sacrificed and neglected for its political purpose. Under the current circumstance that the environmental crisis threatens all landscapes of wild, rural, and urban, ecocriticism needs to put together "green" landscapes of wildness and "brown" landscapes of urban environment. The interdependence between outback and urban landscapes is best observed in suburban areas in which both landscapes coexist and merge. Provided with due learning and attention of nonhuman environment in their backyard, suburban residents have privilege of both appreciating nature's beauty and value of its own, on the one hand, and acutely reckoning urban environmental concerns related to their health, safety, and sustenance, on the other, in their own home grounds. The post-1980s in the United States has witnessed the emerging voices of suburban nature writings that speak for both green and brown landscapes, which have escaped from ecocritical attention. Among the suburban nature writings, those of Michael Pollan and Thomas Mitchell well illustrate how the green and brown landscapes are interwoven and, accordingly, how environmental awareness of both landscapes can start in suburban 'home.' Ecocriticism's validation as relevant studies of literature and environment may depend on these suburban nature writings which demonstrate an 'ancient-future' ethic of "home" based environmentalism.

• Journal of the Korea Computer Industry Society
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• v.2 no.10
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• pp.1309-1316
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• 2001

In the majority of todays animation and computer games, the behaviours of characters are controlled by pre-defined game logic or pre-generated motion. As game developers strive for richer and more interactive games, they often encounter limitations with this approach. This paper attempts to construct a game model using Genetic Algorithms (GAs) in order to produce more intelligent and compelling computer games. Based on learning ability, the use of GAs will enable the characters to continually evolve, providing a changing and dynamic game environment. A real-time game was implemented to investigate the performance and limitations of the system.