• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.662-672
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• 2016

The purpose of this study was to develop an integrated-simulation practice program using standardized patients and to identify the effects of such program. The present study used a pretest-posttest design with a single group applied to 30 fourth-year nursing students in a university and developed a case scenario for alcoholism patients with diabetes mellitus in a community. As results showed, communication skills were significantly improved (t = 4.24, p < .001), but the learning of self-efficacy and motivation of transfer were insignificantly improved compared with the pretest. Moreover, motivation of transfer showed a positive correlation with the learning of self-efficacy (r = .758, p < .01). The purpose of utilizing an appropriate case development based on practical experience and hands-grade students was to improve the motivation of transfer and increase self-efficacy through problem solving. Therefore, we identified that an integrated-simulation practice program using standard patients was useful in the improvement of client centered nursing competence, such as communication skills. In addition, further studies would help develop various scenarios for the integrated-simulation practice program to improve not only communication skills but also increase self-efficacy and motivation of transfer.

• International Commerce and Information Review
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• v.13 no.2
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• pp.121-148
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• 2011

Korean small and medium sized firms are dramatically expanding during the past two decades. Since small and medium sized firms begun to invest overseas to cope with the external and internal business environment. the influencing factors should defined for the successful foreign investment. This paper presents the research model explaining successful knowledge transfer between Korean small and medium sized firms and partners for foreign investment. This model examines investing companies' organizational characteristics, partners' learning capability and relational characteristics between two partners. Detail variables include the learning culture and codifiability of investing companies, and absorptive capability of partners, and communication and trust as a relational factors between investing companies and partners. The result of empirical analysis of sample companies shows that knowledge culture and codifiability of investing companies, and communication from the relational factors are important for knowledge transfer. These results provide some implications for the successful foreign investment of small and medium sized firms. Firstly the investing company should develop its own learning culture and internal procedure for the successful foreign investment. And frequent communication channel is necessary for knowledge transfer and the trustful relationship between investors and partner.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.93-104
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• 2022

This study was done to identify the factors affecting the problem-solving ability of nursing students who have received online psychiatric nursing practice. The subjects of the study were 280 fourth-grade nursing students. The data were subjected to descriptive statistics, 𝑥²-test, t-test, one-way ANOVA, Pearson's correlation analysis, and multiple regression analysis using the SPSS 24.0 program. As a result of the analysis, the mean of each variable was learning satisfaction 4.03±.70, self-efficacy in learning 5.69±.82, transfer motivation 5.52±.86, and problem-solving ability was 3.65±.41. Learning satisfaction and problem-solving ability (r=.387, p<.001), self-efficacy in learning and problem-solving ability (r=.576, p<.001), transfer motivation and problem-solving ability (r=.536, p <.001) showed a significant correlation. Factors affecting problem-solving ability were gender (𝛽=.11), grade point average (𝛽=.12), motivation of personal major choice (𝛽=-.12), satisfaction of major (𝛽=.13), self-efficacy in learning (𝛽=.36) and transfer motivation (𝛽=.16), and the explanatory power of variables was 41.4%. Based on the results of this study, a follow-up study is required to identify the causal relationship between variables related to problem-solving ability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.792-812
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• 2022

Multi-agent systems often need to achieve the goal of learning more effectively for a task through coordination. Although the introduction of deep learning has addressed the state space problems, multi-agent learning remains infeasible because of the joint action spaces. Large-scale joint action spaces can be sparse according to implicit or explicit coordination structure, which can ensure reasonable coordination action through the coordination structure. In general, the multi-agent system is dynamic, which makes the relations among agents and the coordination structure are dynamic. Therefore, the explicit coordination structure can better represent the coordinative relationship among agents and achieve better coordination between agents. Inspired by the maximization of social group utility, we dynamically construct a factor graph as an explicit coordination structure to express the coordinative relationship according to the utility among agents and estimate the joint action values based on the local utility transfer among factor graphs. We present the application of such techniques in the scenario of multiple intelligent vehicle systems, where state space and action space are a problem and have too many interactions among agents. The results on the multiple intelligent vehicle systems demonstrate the efficiency and effectiveness of our proposed methods.

• Journal of the Korean Society of Visualization
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• v.19 no.2
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• pp.41-47
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• 2021

River and stream are the important water supply source in our lives. Eutrophication causes excessive green algae growth including microcystis, which makes harmful to ecosystem and human health. Therefore, the water purification process to remove green algae is essential. In Korea, green algae alarm system exists depending on the concentration of green algae cells in river or stream. To maintain the growth amount under control, green algae monitoring system is being used. However, the unmanned, small and automatic monitoring system would be preferable. In this study, we developed the 3D printed device to measure the concentration of green algae cell using microfluidic droplet generator and deep learning. Deep learning network was trained by using transfer learning through pre-trained deep learning network. This newly developed microfluidic cell counter has sufficient accuracy to be possibly applicable to green algae alarm system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5179-5196
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• 2019

To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.313-322
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• 2020

A large volume of data will improve the robustness of deep learning models and avoid overfitting problems. In automatic tongue segmentation, the availability of annotated tongue images is often limited because of the difficulty of collecting and labeling the tongue image datasets in reality. Data augmentation can expand the training dataset and increase the diversity of training data by using label-preserving transformations without collecting new data. In this paper, augmented tongue image datasets were developed using seven augmentation techniques such as image cropping, rotation, flipping, color transformations. Performance of the data augmentation techniques were studied using state-of-the-art transfer learning models, for instance, InceptionV3, EfficientNet, ResNet, DenseNet and etc. Our results show that geometric transformations can lead to more performance gains than color transformations and the segmentation accuracy can be increased by 5% to 20% compared with no augmentation. Furthermore, a random linear combination of geometric and color transformations augmentation dataset gives the superior segmentation performance than all other datasets and results in a better accuracy of 94.98% with InceptionV3 models.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.17-25
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• 2019

Accurate tracking of the sinkholes that are appearing frequently now is an important method of protecting human and property damage. Although many sinkhole detection systems have been proposed, it is still far from completely solved especially in-depth area. Furthermore, detection of sinkhole algorithms experienced the problem of unstable result that makes the system difficult to fire a warning in real-time. In this paper, we proposed a method of sinkhole tracking by deep learning and data association, that takes advantage of the recent development of CNN transfer learning. Our system consists of three main parts which are binary segmentation, sinkhole classification, and sinkhole tracking. The experiment results show that the sinkhole can be tracked in real-time on the dataset. These achievements have proven that the proposed system is able to apply to the practical application.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Journal of Korea Game Society
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• v.16 no.3
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• pp.109-116
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• 2016

Distance learning systems that teacher and learner(s) are located at remote have been in progress in a way that directly transfer the video and audio. To get the interest of learners and effectiveness of education or to overcome the poor network environment, various methods utilizing computer graphics in the distance learning system have been attempted. This paper describes a design and implementation of a distance learning system using 3D animation control technology based on Kinect and network game technology. Distance learning system designed and implemented in this paper is a good example of combining education and game technology. And I expect to be used at various educational contents in the future.