• Asia pacific journal of information systems
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• v.31 no.1
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• pp.43-75
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• 2021

Clearly, the smartphone is increasingly playing a greater role in everyday life, thus providing opportunities to evaluate how well the use of the smartphone meets the requirements of undergraduate students in independent learning outside of a classroom setting. This study used the task-technology fit (TTF) model to explore the effectiveness of smartphone usage to interact with learning materials in independent learning outside of classrooms, the need for smartphone support, and the fit of devices to tasks as well as performance. First, the study used interviews, observation, and survey data to identify what are the most important constructs of smartphones that stimulate students to interact with learning materials in independent learning outside of classrooms. Based on the findings from the exploratory study and Task Technology Fit theory, we postulated the Navigation design, Ergonomic design, Content support, and Capacity as the essential dimension of the smartphone construct. Then, we proposed a research model and empirically tested hypotheses with the structural model analysis. The results reveal a significant positive impact of task and technology on TTF for smartphone usage to interact with learning materials in independent learning outside of classrooms; it also confirmed the TTF and performance have a direct effect on actual use.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.2073-2081
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• 2021

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.660-669
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• 2020

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.810-813
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• 2002

We present the information theoretic learning based on the Tsallis entropy maximization principle for various q. The Tsallis entropy is one of the generalized entropies and is a canonical entropy in the sense of physics. Further, we consider the dependency of the learning on the parameter $\sigma$, which is a standard deviation of an assumed a priori distribution of samples such as Parzen window.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.36-41
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• 2012

We tested a single pendulum simulation and observed the influence of several situation space segmentation types in reinforcement learning processes in order to propose a new adaptive automation for situation space segmentation. Its segmentation is performed by the Contraction Algorithm and the Cell Division Approach. Also, its automation is performed by "entropy," which is defined on action values’ distributions. Simulation results were shown to demonstrate the influence and adaptability of the proposed method.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.339-339
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• 2006

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.472-472
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• 2006

• Korean Journal of Radiology
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• v.22 no.2
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• pp.168-178
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• 2021

Objective: To provide an automatic method for segmentation and diameter measurement of type B aortic dissection (TBAD). Materials and Methods: Aortic computed tomography angiographic images from 139 patients with TBAD were consecutively collected. We implemented a deep learning method based on a three-dimensional (3D) deep convolutional neural (CNN) network, which realizes automatic segmentation and measurement of the entire aorta (EA), true lumen (TL), and false lumen (FL). The accuracy, stability, and measurement time were compared between deep learning and manual methods. The intra- and inter-observer reproducibility of the manual method was also evaluated. Results: The mean dice coefficient scores were 0.958, 0.961, and 0.932 for EA, TL, and FL, respectively. There was a linear relationship between the reference standard and measurement by the manual and deep learning method (r = 0.964 and 0.991, respectively). The average measurement error of the deep learning method was less than that of the manual method (EA, 1.64% vs. 4.13%; TL, 2.46% vs. 11.67%; FL, 2.50% vs. 8.02%). Bland-Altman plots revealed that the deviations of the diameters between the deep learning method and the reference standard were -0.042 mm (-3.412 to 3.330 mm), -0.376 mm (-3.328 to 2.577 mm), and 0.026 mm (-3.040 to 3.092 mm) for EA, TL, and FL, respectively. For the manual method, the corresponding deviations were -0.166 mm (-1.419 to 1.086 mm), -0.050 mm (-0.970 to 1.070 mm), and -0.085 mm (-1.010 to 0.084 mm). Intra- and inter-observer differences were found in measurements with the manual method, but not with the deep learning method. The measurement time with the deep learning method was markedly shorter than with the manual method (21.7 ± 1.1 vs. 82.5 ± 16.1 minutes, p < 0.001). Conclusion: The performance of efficient segmentation and diameter measurement of TBADs based on the 3D deep CNN was both accurate and stable. This method is promising for evaluating aortic morphology automatically and alleviating the workload of radiologists in the near future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.

• Journal of Fisheries and Marine Sciences Education
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• v.17 no.2
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• pp.252-259
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• 2005

In this research, as an educational tool in the Refrigeration instruction, a project learning method was applied which enables active hand on practice and encourages a more active participation of students with an increased level of interest in learning. The purpose of this study is to present project learning method as an educational tool that can arouse interest and motivation of students by an active hands-on learning process. This will aid in the enhancement of understanding of educators and students in the project learning method, and assist in development of project learning method that can increase intuitiveness and originality of students. For the purpose of the study, the following questions were asked. First, what is the level of existing knowledge pertaining to Refrigeration Training Program of students? Second, what effect does Refrigeration Training Program conducted via the project approach have on the level of motivation and interest of students? Third, what effect does Refrigeration Training Program conducted via the project approach have on the study habits of students?