• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.11-16
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• 2022

Although the number of foreign students is increasing with the development of internationalization, international students are often unfamiliar to the campus environment in the early stages of their school visits. This research aims to solve the problems of foreign students' unfamiliarity with the campus and the inconvenience of study and life after enrollment and to design and produce an AR campus guide application based on gamification. The application built are designed according to the targets, missions, and rewards of different places. Through the 'A Survey on the Awareness of Kongju University's Buildings' questionnaire survey of international students at National Kongju University, six place were selected as POI (Point of Interest). Missions and questions suitable for users were designed. Through this application, it is hoped that users can learn about important places of the school interestingly and learn about the use of related convenience facilities.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.31-40
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• 2024

In the realm of domestic online fashion platform industry the manual registration of product information by individual business owners leads to inconvenience and reliability issues, especially when dealing with simultaneous registrations of numerous product groups. Moreover, bias is significantly heightened due to the low quality of product images and an imbalance in data quantity. Therefore, this study proposes a ResNet50 model aimed at minimizing data bias through oversampling techniques and conducting multiple classifications for 13 fashion categories. Transfer learning is employed to optimize resource utilization and reduce prolonged learning times. The results indicate improved discrimination of up to 33.4% for data augmentation in classes with insufficient data compared to the basic convolution neural network (CNN) model. The reliability of all outcomes is underscored by precision and affirmed by the recall curve. This study is suggested to advance the development of the domestic online fashion platform industry to a higher echelon.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.675-680
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• 2024

This study aims to examine the trends in Metaverse technology following the Post-COVID era and analyze the use cases in the jewelry industry. With the endemic, the business environment for companies has shifted from online to offline, leading to a reduced public interest in the Metaverse. However, examining the global jewelry brand trends in metaverse technology reveals advancements in AR/VR technologies that enhance realism and evolve the metaverse into a space without the uncanny gap between virtual and reality. The Metaverse exhibits three main characteristics in the Post-COVID era. First, there is a transformation in the business domain, starting with digital twins. Second, it is integrating with various information and communication technologies. Third, setting a direction for Metaverse operation as an omni-channel is being emphasized. Utilizing assets learned during the COVID-19 period and continuing to learn about digital and online technologies is essential for securing market competitiveness. This paper discusses how to enhance the competitiveness of jewelry industry entities based on the trends of Metaverse technology in the Post-COVID era.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.23-30
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• 2019

The development of content for language education using AR/VR technology is a necessary task to be pursued in line with commercialization of 5G. Research on service platform for systematic management and service is currently being carried out by global companies competitively, The unique language education service model for unique areas of culture has the right to pursue R & D jointly with Korea and China. In this study, we applied the developed "Korean language education service platform for Chinese people based on e-learning" to improve the acceptance of AR/VR contents and applied AR/VR technology to video-based language education contents. And to present a new paradigm of language education. Contents development is to develop AR-based vocabulary learning services, develop experiential learning contents for VR-based step-by-step situations, and gradually develop contents to enable beginner / intermediate / advanced language education services. The service platform enables management of learning management and learning contents, and complies with metadata attributes to complete a platform capable of accommodating large capacity AR/VR contents. In the future, systematic research will be carried out in order to develop as a portal for educational services through development of various contents using mixed reality technology.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.209-216
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• 2022

In this paper, we propose an effective software education program to promote employment of the disabled and verify the effectiveness of SW education through pilot operation. In this SW education program, we develop a SW curriculum consisting of the basic course, Unity programming course, and the advanced course, AR/VR digital content development course. The SW education achievement standard develops the basic and advanced course achievement standards in consideration of the level of the virtual reality content production job of the National Competency Standards(NCS) and the SW education achievement standards of youth with visual, hearing, and physical disabilities. SW education materials are developed on a project basis so that one AR/VR digital content can be implemented step by step according to the intellectual level of the disabled based on Unity. SW education pilot training is conducted as online education based on Blended Learning due to COVID-19. In order to derive the SW education effect and each learner's individual SW education academic achievement for the SW education pilot training, a survey is conducted on learners, and the results are analyzed. In the basic course, 77.3% of learners achieved academic achievement above excellent(80-90), and in the advanced course, 48.8% of learners achieved academic achievement above excellent(80-90). These results verify that the SW education program for the disabled developed in this paper is effective in activating employment for the disabled.

• Tunnel and Underground Space
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• v.32 no.2
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• pp.131-143
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• 2022

The lunar exploration autonomous vehicle operates based on the lunar topography information obtained from real-time image characterization. For highly accurate topography characterization, a large number of training images with various background conditions are required. Since the real lunar topography images are difficult to obtain, it should be helpful to be able to generate mimic lunar image data artificially on the basis of the planetary analogs site images and real lunar images available. In this study, we aim to artificially create lunar topography images by using the location information-based style transfer algorithm known as Wavelet Correct Transform (WCT2). We conducted comparative experiments using lunar analog site images and real lunar topography images taken during China's and America's lunar-exploring projects (i.e., Chang'e and Apollo) to assess the efficacy of our suggested approach. The results show that the proposed techniques can create realistic images, which preserve the topography information of the analog site image while still showing the same condition as an image taken on lunar surface. The proposed algorithm also outperforms a conventional algorithm, Deep Photo Style Transfer (DPST) in terms of temporal and visual aspects. For future work, we intend to use the generated styled image data in combination with real image data for training lunar topography objects to be applied for topographic detection and segmentation. It is expected that this approach can significantly improve the performance of detection and segmentation models on real lunar topography images.

• Convergence Security Journal
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• v.22 no.3
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• pp.25-32
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• 2022

Recently, the image processing industry has been activated as deep learning-based technology is introduced in the image recognition and detection field. With the development of deep learning technology, learning model vulnerabilities for adversarial attacks continue to be reported. However, studies on countermeasures against poisoning attacks that inject malicious data during learning are insufficient. The conventional countermeasure against poisoning attacks has a limitation in that it is necessary to perform a separate detection and removal operation by examining the training data each time. Therefore, in this paper, we propose a technique for reducing the attack success rate by applying modifications to the training data and inference data without a separate detection and removal process for the poison data. The One-shot kill poison attack, a clean label poison attack proposed in previous studies, was used as an attack model. The attack performance was confirmed by dividing it into a general attacker and an intelligent attacker according to the attacker's attack strategy. According to the experimental results, when the proposed defense mechanism is applied, the attack success rate can be reduced by up to 65% compared to the conventional method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.323-330
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• 2023

The development of an analysis model that reflects the microstructure characteristics of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites, which have a highly complex microstructure, enables synergy between efficient material design and real experiments. PVA fiber orientations are an important factor that influences the mechanical behavior of PVA fiber-reinforced cementitious composites. Owing to the difficulty in distinguishing the gray level value obtained from micro-CT images of PVA fibers from adjacent phases, fiber segmentation is time-consuming work. In this study, a micro-CT test with a voxel size of 0.65 ㎛³ was performed to investigate the three-dimensional distribution of fibers. To segment the fibers and generate training data, histogram, morphology, and gradient-based phase-segmentation methods were used. A U-net model was proposed to segment fibers from micro-CT images of PVA fiber-reinforced cementitious composites. Data augmentation was applied to increase the accuracy of the training, using a total of 1024 images as training data. The performance of the model was evaluated using accuracy, precision, recall, and F1 score. The trained model achieved a high fiber segmentation performance and efficiency, and the approach can be applied to other specimens as well.

• Journal of Information Technology Services
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• v.18 no.5
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• pp.85-97
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• 2019

In this study, we developed the Field-Experiential Learning Framework Using the Location Based Mobil-learning System (LBMS) and it is mobile Augmented Reality (AR) for smart learning system which is advanced e-learning. AR is technology that seamlessly overlays computer graphics on the real world. LBMS-AR has become widely available because of mobile AR. Mobile AR is possible to get information from real world anytime, anywhere. Nowadays, there are various areas using AR such as entertainment, marketing, location-based AR. We analysed the result of survey and implemented the functions. Also, for survey about application's effectiveness, we have focus group interview (FGI). Then we demonstrated and explained the application to them. The result of survey about application's effectiveness shows that application have higher utilization in education area. One of the most promising areas is education. AR in education shows lifelike images to users for realism. It's a good way for improving concentration and attention. We utilize only a beacone for image-based AR without other sensor.