• 한국정보교육학회:학술대회논문집
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• 2008.01a
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• pp.263-268
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• 2008

학교의 학습 과정에 있어서 학습자 간의 학습 능력의 차이는 존재하며 이를 해결하기 위해 교육과정에서는 개별 학습과 수준별 학습을 권장한다. 유무선 인터넷을 통한 수준별 학습은 최근에 많은 연구가 되고 있는 u-러닝(Ubiquitous Learning) 환경에도 부합하며 학습자 개개인이 자신의 속도와 수준에 맞게 자기주도적으로 학습을 하기에 알맞은 방법이라 할 수 있다. 이에 본 연구에서는 학습자가 시간과 공간에 구애받지 않고 자율적으로 수준에 맞게 학습할 수 있는 수준별 학습 시스템을 설계하였다. 특히 시스템에 체계성을 더하기 위해 개별화 학습 체제 중에서 과거 많은 연구를 통해 그 효과성이 입증된 Keller의 PSI(Personal System of Instruction) 이론을 활용하여 시스템의 각 과정을 설계하였다. 본 시스템의 장점은 다음과 같다. 첫째, 학습자가 원하는 시간과 공간에서 자신의 속도에 맞게 학습할 수 있으므로 자기주도적인 학습 능력을 기를 수 있다. 둘째, 시스템 구성상 평가를 통해 일정한 기준에 미달하면 목표에 도달할 때까지 계속 학습하고 도전해야 하므로 궁극적으로 완전학습에 도달할 수 있다. 셋째, 제한된 교실 상황에서 벗어나 온라인에서의 학습 지원이 가능하므로 학습자의 개인차에 따른 수준별 학습을 관리하고 책임져야 하는 교사의 부담을 덜어준다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1388-1391
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• 2010

In this paper, we present a design and implementation of U-learning system using pen based augmented reality approach. Student has been given a smart pen and a smart study book, which is similar to the printed material already serviced. However, we print the study book using CMY inks, and embed perceptually invisible dot patterns using K ink. Smart pen includes (1) IR LED for illumination, IR pass filter for extracting the dot patterns, and (3) camera for image captures. From the image sequences, we perform topology analysis which determines the topological distance between dot pixels, and perform error correction decoding using four position symbols and five CRC symbols. When a student touches a smart study books with our smart pen, we show him/her multimedia (visual/audio) information which is exactly related with the selected region. Our scheme can embed 16 bit information, which is more than 200% larger than previous scheme, which supports 7 bits or 8 bits information.

• The Journal of Information Systems
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• v.20 no.4
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• pp.181-204
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• 2011

Recently, the development of ICT(information & communications technology) with the advent of new media paradigm shift in learning has brought a dramatic impact on the competitiveness of universities. The previous studies on the academic performance of e-learning mainly targeted on e-learning users, studying additional synchronization and system quality factors to measure academic performance. This study empirically analyzed the learning flow and academic performance considering both DeLone & McLean model system quality and synchronizing factors based on ARCS model. Relating to quality and synchronization factors, the academic performance of e-learning system was tested, and the difference between learning flow and academic performance was also analyzed based on time-series data, by the test difference(in the beginning, during, and final of the semester). The results of the study are as follows. First, the study shows that both system quality and synchronization directly affected the learning performance. Thus, when designing e-learning system, it is necessary to consider these two factors at the same time. Second, the indirectly mediating effect on the system quality and synchronization factors turned out to be significant in learning flow. Third, the result of regression analysis on the contents of utilizing dummy variable presents that the teacher's explanation has greater influence than multimedia has to the academic performance, and furthermore, the test difference showed no significance. Further research should be undertaken to consider the learner's degree of acceptance which reflects various aspects for building m-learning or u-learning.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2011.11a
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• pp.23-26
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• 2011

U-러닝은 유비쿼터스러닝(Ubiquitous Learning)의 약자로 개방적 학습자원을 학습자의 필요에 따른 선택에 의해 활용하는 통합적 학습체제 의미한다. 국내에서의 소방교육은 다양한 소방교육 컨텐츠부족 및 시간적 공간적 제약으로 인해 소방안전에 대한 교육이 원활히 이루어지지 못하고 있는 실정이다. U-Learning 구성하여 다양한 컨텐츠를 거주자, 근무자, 방화관리자에게 제공하여 자기주도적인 학습을 통해 평상시 거주자 및 근무자에게 소방시설의 이해 및 사용방법을 교육하고, 방화관리자에게는 소방시설의 관리 및 점검방법을 교육함으로써 화재 및 재난으로 인한 피해를 최소화 할 수 있다.

• Educational Technology International
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• v.8 no.2
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• pp.1-19
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• 2007

This study is to analyze the implications of effective learning in a ubiquitous environment. Research proceeded according to the multiple case study analysis method. This paper is one result of the Korean case study to examine the effectiveness of and satisfaction with u-learning. We will introduce necessary conditions for effective learning in a ubiquitous environment. Each condition was elicited through the case study, and the analyzing framework was classified into hardware related to infra structure; software such as learning contents, teaching-learning activity and support, and class management; human-ware related to learner and teacher; system-ware as an education system, and administrative supporting.

• Journal of Biomedical Engineering Research
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• v.45 no.3
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• pp.118-127
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• 2024

Commonly deep learning methods for enhancing the quality of medical images use unpaired dataset due to the impracticality of acquiring paired dataset through commercial imaging system. In this paper, we propose a supervised learning method to enhance the quality of ultrasound images. The U-net model is designed by incorporating a divide-and-conquer approach that divides and processes an image into four parts to overcome data shortage and shorten the learning time. The proposed model is trained using paired dataset consisting of 828 pairs of low-quality and high-quality images with a resolution of 512x512 pixels obtained by varying the number of channels for the same subject. Out of a total of 828 pairs of images, 684 pairs are used as the training dataset, while the remaining 144 pairs served as the test dataset. In the test results, the average Mean Squared Error (MSE) was reduced from 87.6884 in the low-quality images to 45.5108 in the restored images. Additionally, the average Peak Signal-to-Noise Ratio (PSNR) was improved from 28.7550 to 31.8063, and the average Structural Similarity Index (SSIM) was increased from 0.4755 to 0.8511, demonstrating significant enhancements in image quality.

• Journal of Korean Society on Water Environment
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• v.37 no.4
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• pp.275-285
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• 2021

Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1486-1495
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• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.609-620
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• 2010

In order to provide a tailored education for learners within the ubiquitous environment, it is critical to undertake an analysis of the learning activities of learners. For this purpose, SCORM (Sharable Contents Object Reference Model), IMS LD (Instructional Management System Learning Design) and other standards provide learning design support functions, such as, progress checks. However, in order to apply these types of standards, contents packaging is required, and due to the complicated standard dimensions, the facilitation level is lower than the work volume when developing the contents and this requires additional work when revision becomes necessary. In addition, since the learning results are managed by the server there is the problem of the OS being unable to save data when the network is cut off. In this study, a system is realized to manage the actions of learners through the event interception of a web-browser by using event hooking. Through this technique, all HTMLbased contents can be facilitated again without additional work and saving and analysis of learning results are available to improve the problems following the application of standards. Furthermore, the ubiquitous learning environment can be supported by tracking down learning results when the network is cut off.

• The Journal of Sustainable Design and Educational Environment Research
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• v.4 no.4
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• pp.39-54
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• 2004

In this study, the following findings were obtained: First, life-long educational system should be reinforced that can train and educate people to fit their situation and provide the necessary manpower in a just-in-time manner by getting away from the school-centered education and rapidly introducing the knowledge required in both the world market and the domestic market. This can be said to be the global trend in the ubiquitous age. Second, government should make efforts to build up the life-long educational system that can make the persons trained and educated in schools the manpower required by the state and society. Third, Life-long learning policy starts with providing for the system of lifting all kinds of limits and obstacles so that anyone needing learning can learn and his learning may not discriminated from schooling. For this policy or system to be effectively promoted, government should reinforce administrative and financial support system for investment in and research on the ubiquitous department. Fourth, It is quiet right that the very effort we are going give the super to the ubiquitous education is a shortcut to solving rapidly lots of problems heaped on our present life-long educational system.