• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.65-73
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• 2023

In this paper, we propose a data augmentation method based on CNN(Convolutional Neural Network) learning for efficiently obtaining concrete crack image datasets. Real concrete crack images are not only difficult to obtain due to their unstructured shape and complex patterns, but also may be exposed to dangerous situations when acquiring data. In this paper, we solve the problem of collecting datasets exposed to such situations efficiently in terms of cost and time by using vector and thickness-based data augmentation techniques. To demonstrate the effectiveness of the proposed method, experiments were conducted in various scenes using U-Net-based crack detection, and the performance was improved in all scenes when measured by IoU accuracy. When the concrete crack data was not augmented, the percentage of incorrect predictions was about 25%, but when the data was augmented by our method, the percentage of incorrect predictions was reduced to 3%.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.90-96
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• 2023

Securing large amounts of training data in deep learning neural networks, including convolutional neural networks, is of importance for avoiding overfitting phenomenon or for the excellent performance. However, securing labeled training data in deep learning neural networks is very limited in reality. To overcome this, several augmentation methods have been proposed in the literature to generate an additional large amount of training data through transformation or manipulation of the already acquired traing data. However, unlike training data such as images and texts, it is barely to find an augmentation method in the literature that additionally generates bio-signal training data for convolutional neural network based human activity recognition. Thus, this study proposes a simple but effective augmentation method of bio-signal training data for convolutional neural network based human activity recognition. The usefulness of the proposed augmentation method is validated by showing that human activity is recognized with high accuracy by convolutional neural network trained with its augmented bio-signal training data.

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1290-1304
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• 2020

Medical image analyses have been widely used to differentiate normal and abnormal cases, detect lesions, segment organs, etc. Recently, owing to many breakthroughs in artificial intelligence techniques, medical image analyses based on deep learning have been actively studied. However, sufficient medical data are difficult to obtain, and data imbalance between classes hinder the improvement of deep learning performance. To resolve these issues, various studies have been performed, and data augmentation has been found to be a solution. In this review, we introduce data augmentation techniques, including image processing, such as rotation, shift, and intensity variation methods, generative adversarial network-based method, and image property mixing methods. Subsequently, we examine various deep learning studies based on data augmentation techniques. Finally, we discuss the necessity and future directions of data augmentation.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.373-379
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• 2023

With the 4th Industrial Revolution, VR/AR technology is developing, and since COVID-19, the use of VR/AR technology is increasing and is being used in various environments. In particular, AR technology can be used in various educational fields and the perception of VR in the educational field and learning satisfaction, problem-solving ability, and self-directed learning ability when using VR were analyzed. It was analyzed that perception of VR and learning satisfaction had a significant correlation at the significance level of 0.001, and problem-solving competency had a significant correlation between learning satisfaction and self-directed learning ability at the significance level of 0.05.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.205-210
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• 2005

유비쿼터스 컴퓨링과 네트워킹 환경이 준비됨에 따라 교육 분야에서도 새로운 환경에 적합한 교육학습 시스템에 대한 준비가 필요하다. 특히 유비쿼터스 컴퓨팅 환경에서는 단순히 새로운 기술을 교육학습 분야에 적용하는 것이 아니라 사고방식과 대상을 바꾸는 패러다임의 전환이 필요하다. 분야에서는 유비쿼터스 환경을 단계적으로 적용하여야 한다. 기존의 e-learning에서는 지능시스템이 교육학습 분야에 적용될 수 있는 부분이 한정되어 있었다. 그러나 유비쿼터스 맞춤형 학습 시스템을 구축할 수 있는 기본 환경이 제공하기 위하여 유비퀴터스 환경의 하부 단위에서 증강현실(augmented reality) 기술, 지능형 학습 기술들을 도출하고 적용 방법을 제안한다.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.243-250
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• 2019

With the popularization of online learning, mobile learning and blended learning model, augmented reality technology is increasingly widely applied in the field of education today. This article stresses the necessity of applying augmented reality to education by summarizing the current situation and advantages of augmented reality technology in education. Next, the author proposes a new education system-"AR+E education cloud platform system". Last, the author proposes several inspirations for the design of AR-based education software according to the observation and interview of the behaviors and preferences of the respondents.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1103-1110
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• 2022

Due to the rapid development of AI-related industries, countless edge devices are working in the real world. Since data generated within the smart space consisted of these devices is beyond imagination, it is becoming increasingly difficult for edge devices to process. To solve this issue, EdgeCPS has appeared. EdgeCPS is a technology to support harmonious execution of various application services including AI applications through interworking between edge devices and edge servers, and augmenting resources/functions. Therefore, we propose a knowledge-sharing graph-based componentized AI application support system applicable to the EdgeCPS platform. The graph is designed to effectively store information which are essential elements for creating AI applications. In order to easily change resource/function augmentation under the support of the EdgeCPS platform, AI applications are operated as components. The application support system is linked with the knowledge graph so that users can easily create and test applications, and visualizes the execution aspect of the application to users as a pipeline.

• Annual Conference of KIPS
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• 2022.05a
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• pp.37-39
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• 2022

기존 IoE 환경에서 수집데이터는 특정 서비스를 위한 도메인 지식과 연계되어 서비스를 제공한다. 하지만 수집되는 데이터의 유형이 다양하고, 정적인 지식베이스가 상황에 따라 동적으로 변화하는 IoE 환경에서는 기존의 지식베이스 시스템을 통하여 원활한 서비스를 제공할 수 없었다. 따라서, 본 논문에서는 IoE 환경에서 발생하는 대용량/실시간성 데이터를 시맨틱으로 처리하여 공통 도메인 지식베이스와 연계하고 기존의 지식베이스 추론 방법과 기계학습 기반 지식 임베딩 기법을 통하여 지식 증강을 유기적으로 진행하는 빅시맨틱 시스템을 제시한다. 제시한 시스템은 IoE 환경의 멀티모달(정형, 비정형) 데이터를 수집하고 반자동적으로 시맨틱 변환을 수행하여 도메인 지식베이스에 저장하고, 시맨틱 추론을 통해 지식베이스를 증강 시키며 증강된 지식베이스를 포함한 전체 지식베이스를 정형 및 반정형 사용자 쿼리를 통해 지식정보를 사용자에게 제공한다. 또한, 기계학습 기반 지식 임베딩 기법을 통해 학습·예측을 함으로써, 기존의 지식베이스를 증강하는 기능을 수행한다. 본 논문에서 제시한 시스템은 공장내의 에너지 정보를 수집하여 공정 및 설비 상태 및 운영정보를 바탕으로 실시간 제어를 통한 에너지 절감 시스템인 공장 에너지 관리 시스템의 기반 기술로 구현될 예정이다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.10
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• pp.883-892
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• 2021

This paper provides a process for developing deep learning-based aerial object detection models that can run in realtime on onboard. To improve object detection performance, we pre-process and augment the training data in the training stage. In addition, we perform transfer learning and apply a weighted cross-entropy method to reduce the variations of detection performance for each class. To improve the inference speed, we have generated inference acceleration engines with quantization. Then, we analyze the real-time performance and detection performance on custom aerial image dataset to verify generalization.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.453-463
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• 2018

This study investigated the effects of an Augmented Reality(AR) implemented book on flow of reading, interest in history, and acquisition of history knowledge. Perceptions of AR infused books were investigated as well. Researchers provided a history book implementing AR and the same book without any AR content respectively to an experiment group(n=15) and a control group(n=15) composed of $3^{rd}$ and $4^{th}$ grade elementary school children. Results indicate that AR implemented reading had a positive effect on the flow of reading and interest in history, but not on acquisition of history knowledge. Also, AR-based contents were attractive to learners due to its amusing characters, sound, realistic visual motions, and vivid three-dimensional effects. Lastly, students preferred amusing interesting characters, lengthier animations and subtitles, and AR that could be seen without holding smart devices for a long while.