• Journal of the Korea Computer Industry Society
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• v.2 no.2
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• pp.151-156
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• 2001

We describe a new representation for learning concepts that differs from the traditional decision tree and rule induction algorithms. Our algorithm PROLEARN learns one or more prototype per class and follows instance based classification with them. Prototype here differs from psychological term in that we can have more than one prototype per concept and also differs from other instance based algorithms since the prototype is a "ficticious ideal example". We show that PROLEARN is as good as the traditional machine learning algorithms but much move stable than them in an environment that has noise or changing training set, what we call 'stability’.tability’.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.23 no.3
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• pp.283-291
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• 2016

Purpose: This study was done to evaluate the persistency of effects of an integrated nursing simulation program on interest in learning, recognition of importance of communication, communication skills, and problem-solving abilities. Method: Forty-seven nursing students were recruited for this quasi-experimental design research. The experimental group (n=23) performed the simulation program for two weeks, and the control group (n=24) performed traditional clinical nursing practice for two weeks. Data were collected at baseline, immediately after the intervention, at 4 weeks, and finally at 8 weeks. Results: With respect to all variables, no significant differences were found between the experimental group and the control group. Interest in learning showed a significant increase in the control group (F=3.59, p=.018) at 4 weeks, and there was a significant increase in problem-solving abilities in the experimental group (F=4.98, p=.004) immediately after the intervention. Conclusion: Findings from this study suggest that the integrated nursing simulation program is as effective as the traditional clinical nursing practice, and the integrated nursing simulation program could be used as an alternative.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.2
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• pp.24-32
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• 2012

This purpose of study is to identify types of study groups in online learning and the relationships between the types and teams' performance. In order to address these research questions, four study group types are developed based on Neiderman & Beise(1999)'s typology: 1) fully-supported; 2) highly-virtual; 3) traditional; and 4) inactive. And then three study group types are identified based on taxonomy-approach using cluster analysis from 46 teams participated in this study: 1) fully-supported; 2) traditional; 3), and 4) highly-virtual. The result shows that the groups are validated in the latter, except for the inactive type. The result indicates that fully-supported type groups achieved the highest performance, while the highly-virtual type group achieved the lowest performance.

• International Journal of Contents
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• v.15 no.4
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• pp.59-64
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• 2019

In recent years, emotion recognition has been an interesting and challenging topic. Compared to facial expressions and speech modality, gesture-based emotion recognition has not received much attention with only a few efforts using traditional hand-crafted methods. These approaches require major computational costs and do not offer many opportunities for improvement as most of the science community is conducting their research based on the deep learning technique. In this paper, we propose an end-to-end deep learning approach for classifying emotions based on bodily gestures. In particular, the informative keyframes are first extracted from raw videos as input for the 3D-CNN deep network. The 3D-CNN exploits the short-term spatiotemporal information of gesture features from selected keyframes, and the convolutional LSTM networks learn the long-term feature from the features results of 3D-CNN. The experimental results on the FABO dataset exceed most of the traditional methods results and achieve state-of-the-art results for the deep learning-based technique for gesture-based emotion recognition.

• Journal of Korean Elementary Science Education
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• v.20 no.2
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• pp.217-228
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• 2001

The purpose of this study was to investigate the effect of learning cycle model on the changes of electricity conceptions of elementary students. Four classes in forth grade of an elementary school in Busan were selected and two of them were served as experimental group and the others as control group. The experimental group were taught the unit of "Light an electric bulb" in elementary science textbook with teaching model based on teaming cycle and the control group with traditional teaching style. The instruction effects were analyzed through pre and post-test results using questionnaire on the electricity. The results of pre-test showed that there was not a significant difference between experimental group and control group at .05 level, so two groups could be regarded as homogeneous. The mean score of experimental group was significantly higher than that of control group on the post-test at .05 level. And within-group comparison revealed that both groups made improvement on the mean score and that the improvement of each group had significant difference at .05 level. Above results said that the teaching model based on learning cycle, which focuses on hands-on activity and considers each student as an active subject, was more effective than traditional teaching style in improving the formation of scientific conceptions on electricity.ectricity.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.155-174
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• 2005

Parametric life-cycle cost(LCC) models have been integrated with traditional design tools, and used in prior work to demonstrate the rapid solution of holistic, analytical tradeoffs between detailed design variations. During early designs stages there may be competing concepts with dramatic differences. Additionally, detailed information is scarce, and decisions must be models. for a diverse range of concepts, and the lack of detailed information make the integration make the integration of traditional LCC models impractical. This paper explores an approximate method for providing preliminary life-cycle cost. Learning algorithms trained using the known characteristics of existing products be approximated quickly during conceptual design without the overhead of defining new models. Artificial neural networks are trained to generalize on product attributes and life cycle cost date from pre-existing LCC studies. The Product attribute data to quickly obtain and LCC for a new and then an application is provided. In additions, the statistical method, called regression analysis, is suggested to predict the LCC. Tests have shown it is possible to predict the life cycle cost, and the comparison results between a learning LCC model and a regression analysis is also shown

• Journal of Korean Academy of Nursing
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• v.48 no.6
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• pp.743-753
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• 2018

Purpose: This study aimed to explore the effects of a community-based first and third Intergenerational Exchange Program (IGEP) on older adults' health-related quality of life (HRQoL), loneliness, depression, and walking speed, and on 4~5-year-old preschool children's learning-related social skills. Methods: This study employed a non-equivalent control group pre-post-test design. The experimental group included 42 older adults and 42 children who participated in the IGEP for 8 weeks, and the control group included 39 older adults. The experimental group participated in the IGEP once a week for 8 weeks. It comprised a traditional play program based on the intergroup contact theory. Results: Compared to the control group, there was a significant increase in scores on the HRQoL-Visual analogue scale (VAS) and a decrease in loneliness and depression in older adults in the experimental group (p<.05). Children who participated in the IGEP showed an improvement in their learning-related social skills (p<.001). Conclusion: These results confirm that the IGEP is an effective intervention to improve HRQoL-VAS, loneliness, and depression among older adults and learning-related social skills among preschool children in the community.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.313-315
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• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.

• Korean Journal of Radiology
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• v.25 no.6
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• pp.550-558
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• 2024

Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.2
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• pp.103-123
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• 2006

Assuming that the knowledge creation and retrieval functions could be the most important factors for a successful knowledge management(KM) especially during the promotion stage of KM, this study suggests an e-learning application as one of best methods for producing knowledge and also the integrated knowledge repository model in which learning, knowledge. and information objects can be semantically associated through topic map-based knowledge map. The traditional KM system provides a simple directory-based knowledge map. which can not provide the semantic links between topics or objects. The proposed model can be utilized as a solution to solve the above-mentioned disadvantages of the traditional models. In order to collect the basic data for the proposed model, first, case studies utilizing interviews and surveys were conducted targeting at three Korean insurance companies' knowledge managers(or e-learning managers) and librarians. Second, the related studies and other topic map-based pilot systems were investigated.