• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.1-5
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• 2019

As the number of IOT devices is growing rapidly, various 'see-thru connection' techniques have been reported for efficient communication with them. In this paper, we propose a deep learning based IOT device recognition system for interaction with these devices. The overall system consists of a TensorFlow based deep learning server and two Android apps for data collection and recognition purposes. As the basic neural network model, we adopted Google's inception-v3, and modified the output stage to classify 20 types of IOT devices. After creating a data set consisting of 1000 images of 20 categories, we trained our deep learning network using a transfer learning technology. As a result of the experiment, we achieve 94.5% top-1 accuracy and 98.1% top-2 accuracy.

• Journal of Korean Dental Science
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• v.16 no.1
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• pp.47-62
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• 2023

Purpose: We aimed to investigate the objective cutoff values of unstimulated flow rates (UFR) and stimulated salivary flow rates (SFR) in patients with xerostomia and to present an optimal machine learning model with a classification and regression tree (CART) for all ages. Materials and Methods: A total of 829 patients with oral diseases were enrolled (591 females; mean age, 59.29±16.40 years; 8~95 years old), 199 patients with xerostomia and 630 patients without xerostomia. Salivary and clinical characteristics were collected and analyzed. Result: Patients with xerostomia had significantly lower levels of UFR (0.29±0.22 vs. 0.41±0.24 ml/min) and SFR (1.12±0.55 vs. 1.39±0.94 ml/min) (P<0.001), respectively, compared to those with non-xerostomia. The presence of xerostomia had a significantly negative correlation with UFR (r=-0.603, P=0.002) and SFR (r=-0.301, P=0.017). In the diagnosis of xerostomia based on the CART algorithm, the presence of stomatitis, candidiasis, halitosis, psychiatric disorder, and hyperlipidemia were significant predictors for xerostomia, and the cutoff ranges for xerostomia for UFR and SFR were 0.03~0.18 ml/min and 0.85~1.6 ml/min, respectively. Conclusion: Xerostomia was correlated with decreases in UFR and SFR, and their cutoff values varied depending on the patient's underlying oral and systemic conditions.

• Corrosion Science and Technology
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• v.18 no.2
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• pp.61-71
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• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1615-1622
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• 2022

In order to overcome the limitations of the instructor-centered lecture-style teaching method, recently, flipped learning, a learner-centered teaching method, has been widely introduced. However, despite the many advantages of flipped learning, there is a problem that students cannot solve questions that arise during prior learning in real time. Therefore, in order to solve this problem, we developed DBbot, an assistant chatbot for database design course managed in the flipped learning method. The DBBot is composed of a chatbot app for learners and a chatbot management app for instructors. Also, it's implemented so that questions that instructors can anticipate in advance, such as questions related to class operation and every semester repeated questions related to learning content, can be answered using Google's DialogFlow. It's implemented so that questions that the instructor cannot predict in advance, such as questions related to team projects, can be answered using the question/answer DB and the BM25 algorithm, which is a similarity comparison algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.2
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• pp.19-28
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• 2019

The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', 'deep learning', and 'artificial neural network' in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.63-73
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• 2023

The purpose of this study is to identify the level of learning flow, learning satisfaction, academic self-efficacy, academic achievement, and academic stress of nursing students who experienced non-face-to-face online lectures, and to investigate the correlation between variables and the factors affecting academic stress. The data of this study was collected from 143 students at a nursing college in Seoul, through a Google online questionnaire from September 1, 2023 to September 25, 2023, and descriptive statistics, Student's t-test, analysis of variance, Pearson's Correlation, and linear multiple regression were conducted using SPSS Statistics 25.0. Following an analysis of the difference according to general characteristics, academic stress showed significant difference according to Motivation for applying to department(F=4.465, p=.005) and Major satisfaction(F=36.499, p=.000) of the subjects. The result of analyzing the correlation academic stress was negatively correlated with learning flow (r=-.464, p<.010), academic self-efficacy (r=-.522, p<.010), and academic achievement (r=-.379, p<.010), but learning satisfaction was not correlated with academic stress. Variables affecting academic stress were major satisfaction (𝛽=.367, p<.01), learning flow (𝛽=-.186, p<.05), and academic self-efficacy (𝛽=-.241, p<.05), and the explanatory power for academic stress was 40%. The results of this study can be used as basic data for intervention programs for relieving academic stress of nursing students.

• Journal of the Korean Society of Earth Science Education
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• v.9 no.1
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• pp.97-105
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• 2016

The purpose of this study was to examine the effects of storytelling skill on science concept and science learning motivation. For this study the 5 grade, 2 class was divided into a research group and a comparative group. The class was pre-tested in order to ensure the same standard. The research group had the science class with storytelling skill, and the comparative group had the class of the teacher centered lectures on 11 classes in 8 weeks. The storytelling skill was focused on set the astronomical target wants to set up a story, through the small group discussion, present subject of the story, set the protagonist of the story for smooth configuration of the story, in order to smooth the flow of the story, make up a story around a hero, to make a clear story, decorated with pictures, shapes, graphs, etc, group story, complete with an astronomical(saints) in storytelling. To prove the effects of this study, science concept was split up according to knowledge, inquiry, attitude. Also, science learning motivation consisted of assignment is worth, learning beliefs about control, self efficacy. The results of this study are as follows. First, using storytelling skill was effective in science concept. Second, using storytelling skill was effective in science learning motivation. Also, after using storytelling skill was good reaction by students. As a result, the elementary science class with storytelling skill had the effects of developing science concept and science learning motivation. It means the science class with storytelling skill has potential possibilities and value to develop science concept and science learning motivation.

• Journal of The Korean Association of Information Education
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• v.15 no.1
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• pp.1-10
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• 2011

The programming education in K-12 field is processing with conceptual approaches to obtain basic grammar not including higher knowledge processing. This is main reason that can't able to obtain the educational effects. This study aims to research the innovated methodology of programming education which can have educational effect by participating of learners with positive interest, and recognize the effect of the Scratch programming education on elementary school student's learning-flow and programming ability. As a result Scratch programming education has effect on elementary school student's improving the level of learning-flow and the programming ability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.106-114
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• 2017

This study was a one-group pre-post test design experimental investigation conducted to evaluate the effects of problem-based learning by type of learning in nursing students. The subjects of the study were 125 senior students in the Department of Nursing Science at a single university located in A. City, and the study was conducted for eight weeks from April 18 through June 10, 2016. Data analysis consisted of descriptive statistics, ANOVA, ${\chi}^2$-tests and t-tests. Most nursing students underwent converger type of learning, and after problem-based learning, their learning flow, problem-solving ability and critical thinking disposition increased significantly. In problem-based learning by type of learning, the problem-solving ability was significantly higher in the converger type than in the accommodator type, and there were no significant differences in learning flow and critical thinking disposition; however,in all types of learning, learning flow and critical thinking disposition increased. Since these results suggest that teaching and learning strategies should be set up for each type of learning, it is necessary to seek plans for teaching and learning strategies to make up for the weak points and strengthen the strong points by each type of learning when applying the problem-based learning method.