• Journal of Korean Academy of Nursing
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• v.42 no.6
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• pp.771-782
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• 2012

Purpose: This study was conducted to develop and implement a case-based small group learning program on the care of children with infectious disease, and to examine its effects on knowledge, attitude and preventive practice behaviors of daycare center teachers compared to a control group. Methods: Based on the need assessment, the case-based learning program for the management of infectious children was developed. For this quasi-experimental study, 69 teachers were recruited from 14 child daycare centers in a city located in J province. Thirty four teachers were assigned to experimental group and participated in the case-based small group learning once a week for 5 weeks. Data were analyzed using the SPSS 18.0 program to perform ${\chi}^2$-test and t-tests. Analysis of covariance was used to treat the covariate of the number of assigned children between experimental and control groups. Results: The experimental group showed significantly higher posttest scores in knowledge, attitude and preventive practice behaviors than those of control group (p<.001). Conclusion: These findings indicate that case-based small group learning is an effective educational strategy for daycare center teachers to learn infection management through the emphasis of self-reflection and discussion.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.300-312
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• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.215-226
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• 2024

This study analyzed the performance of educational program for the employees of the work-learning dual system training center using the results of the satisfaction survey of 2,543 and then, suggested implications for enhancing the effectiveness of the curriculum. As a result of the analysis, this educational program showed very high level of satisfaction and was evaluated to provide successful education. Education participant satisfaction was found to be affected by the curriculum situation and participant characteristics. Satisfaction in terms of instructor, subject, facility environment, and operation was all found to have a significant influence on the effectiveness of the educational program for employees of the work-learning dual system training center.

• 한국정보교육학회:학술대회논문집
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• 2005.08a
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• pp.3-12
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• 2005

평생교육 종합정보시스템은 국민들 누구나 자신이 원하는 정보에 접근할 수 있도록 평생교육에 대한 Guide 역할을 수행하는 평생교육 포탈사이트 구축을 목표로 하고 있으며, 나아가 평생교육의 'Information', 'Learning', 'Communication', 'Business' 창구로서의 역할을 하고 있다. 그러나 현재 이러한 비전을 수행할만한 시스템 설계 원리나 구체적인 기능들에 대해서는 논란이 되고 있는 것 같다. 이에 한국교육개발원 평생교육센터에서는 평생교육 체제에 관한 국민들의 요구를 반영할 새로운 시스템을 설계 중에 있다. 이 글은 이런 목표를 담당할 시스템의 설계에 관한 이론적인 접근이다.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.91-99
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• 2024

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

• Progress in Medical Physics
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• v.30 no.2
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• pp.49-58
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• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.428-432
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• 2005

This paper has studied the concept and technical factors of e-Learning system to which we intend to apply domestic biodiversity information. This article describes how we analyzed and designed the e-learning system to serve biodiversity information as e-Learning contents. It would be useful for the public and students if this information are organized and provided as e-learning contents especially in our country which has well-established network infrastructure considering the limited land space. It is expected that the establishment of e-Learning system based on this proposed design will help students and public to access and team biodiversity on cyber space.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.1-7
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• 2021

In this paper, We focused the issue of creating a socially problematic nurse schedule. The nurse schedule should be prepared in consideration of three shifts, appropriate placement of experienced workers, the fairness of work assignment, and legal work standards. Because of the complex structure of the nurse schedule, which must reflect various requirements, in most hospitals, the nurse in charge writes it by hand with a lot of time and effort. This study attempted to automatically create an optimized nurse schedule based on legal labor standards and fairness. We developed an I/O Q-Learning algorithm-based model based on Python and Web Application for automatic nurse schedule. The model was trained to converge to 100 by creating an Fairness Indicator Score(FIS) that considers Labor Standards Act, Work equity, Work preference. Manual nurse schedules and this model are compared with FIS. This model showed a higher work equity index of 13.31 points, work preference index of 1.52 points, and FIS of 16.38 points. This study was able to automatically generate nurse schedule based on reinforcement Learning. In addition, as a result of creating the nurse schedule of E hospital using this model, it was possible to reduce the time required from 88 hours to 3 hours. If additional supplementation of FIS and reinforcement Learning techniques such as DQN, CNN, Monte Carlo Simulation and AlphaZero additionally utilize a more an optimized model can be developed.

• Journal of Engineering Education Research
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• v.14 no.4
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• pp.11-19
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• 2011

The purpose of this study was to identify learning strategies by learning style of first-year engineering students in order to find implications for teaching and learning strategies in engineering education. This study was conducted with 273 first-year students in two universities in Korea. Following were the results: First, there were Sensing learners(72.2%), Visual learners(84.6%), Reflective learners(64.8%), and Sequential learners(58.2%) and the level of learning strategies was 3.28(SD=0.38). Secondly, the finding revealed that there was only significant difference in learning strategies on Information processing dimension and Active students demonstrated higher level of learning strategies than Reflective students. To be more specific, there were significant differences in cognitive, meta-cognitive, and internal and external management. For engineering education, implications for teaching strategies in classroom and self-regulated learning strategies were discussed.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.