• Journal of Digital Convergence
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• v.14 no.4
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• pp.69-78
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• 2016

The purpose of this study is to analyze convergent effects of team interaction and team metacognition of participants on a continuous participation in the university learning community. We developed 19 items of team interaction and 17 items of team metacognition through literature review. The subjects were 113 students who participated in learning community in A university. The results are as follows. First, team interaction level and team metacognition level can affect a continuous participation in learning community. The higher team interaction is and the lower team metacognition is, the higher continuous participation is. Second, among team interaction factors that affect a continuous participation in learning community, the more number of learning is and the more encouragement of one another is, the higher continuous participation is. But the less participation of members is, the less flow to learning is, and the less learning time is, the lower a continuous participation is. Third, among team metacognition factors that affect a continuous participation in learning community, the more number of learning is, the higher continuous participation is. But the more use of various learning tools is and the more learning time is, the lower continuous participation is. Based on these results, the convergent ways of support for continuous participation in the university learning community are as follows. First, supporting system is needed to induce students to experience the positive atmosphere of learning community by increasing number of learning to facilitate team interaction and urging them to encourage one another. Second, providing the effective utilization method is necessary for students to fully acknowledge the necessity and value of team metacognition activity.

• Cartoon and Animation Studies
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• s.35
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• pp.291-312
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• 2014

This study is based on the theory of R.Caillois about element of play which is motivated to infant for studying Hangul. The ultimate goal of play has to be accompanied by pleasure. And learning means permanent changes from experiences for the individual's. Play and learning, these two elements are united to the genre of serious game since the GBL (game based learning) was lead. Most importantly, in order to achieve their own Hangul learning is the fun. Coupled with fun and learning has an important issue for flow because concentration is low in infants than adults. In this case study is to know about fun factor has been applied effectively to Hangul serious mobile game. 20 Infant Hangul mobile serious games of Google Android mobile game section were selected as a case study based on more than 10,000 downloads and user's review rate by April 22, 2014. After that is currently available on the market can play a variety of cases of infant learning Hangul from previous research of R.Caillois offers four categories of play. R.Caillois of Agon, Mimicry, Alea, Ilinx have unique characteristics in comparison with its functional characteristics Hangul four are present any role in Hangul serious mobile games. As a result of the cases selected and the rules of the game will include a maximum of two of the most common types of Agon. Each attribute of the play, rather than one single factor is applied to four kinds of game play performance when properties are distributed to experience together gave the best flow. As a result of this study will be a based research for infants Hangul serious mobile game reflects the properties of the elements of a fun game that you want to combine learning.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1998-2008
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• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.478-489
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• 2013

The controllability and stability of power systems can be increased by Flexible AC Transmission Devices (FACTs). One of the FACTs devices is Interline Power-Flow Controller (IPFC) by which the voltage stability, dynamic stability and transient stability of power systems can be improved. In the present paper, the convenient operation and control of IPFC for transient stability improvement are considered. Considering that the system's Lyapunov energy function is a relevant tool to study the stability affair. IPFC energy function optimization has been used in order to access the maximum of transient stability margin. In order to control IPFC, a Brain Emotional Learning Based Intelligent Controller (BELBIC) and PI controller have been used. The utilization of the new controller is based on the emotion-processing mechanism in the brain and is essentially an action selection, which is based on sensory inputs and emotional cues. This intelligent control is based on the limbic system of the mammalian brain. Simulation confirms the ability of BELBIC controller compared with conventional PI controller. The designing results have been studied by the simulation of a single-machine system with infinite bus (SMIB) and another standard 9-buses system (Anderson and Fouad, 1977).

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.40-50
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• 2021

Science museum are dealing with exhibits on field of changing science and technology, and previous research suggested that exhibits replacement should carried out at least every 5 years. In order to efficiently replace exhibits within a limited budget, various studies analyzed visitors' preferences to exhibits. Recently, studies use various technologies to collect the data on visitors' preferences automatically, but almost of studies had a high dependency on their visitors such as visitors needed to carry specific sub-devices in the museums for gathering data. As complementing the limitations of previous research, this study introduces the improved method which is able to automatically collect and quantify visitors' preferences to exhibits using TensorFlow, a deep learning technology. By the proposed analysis method, it was possible to collect 2,520 data of visitors' experience on exhibits in totality. Based on collected data, attraction power and holding power indicating the preference of visitors on exhibits were able to be calculated. The result also confirmed antecedent research conclusion that the attraction power and holding power of the exhibit which consists of 3 dimensional structures work are higher than other exhibits. As a conclusion, the proposed method will provide more convenient data collection method for detecting visitors' preference.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1239-1248
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• 2021

In this paper, we present FDNN algorithm to perform prediction based on academic understanding. In order to apply prediction based on academic understanding rather than data-dependent prediction to deep learning, we constructed algorithm based on mathematical and hydrology. We construct a model that predicts flow rate of a river as an input of precipitation, and measure the model's performance through K-fold cross validation.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Wind and Structures
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• v.36 no.6
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• pp.379-392
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• 2023

Structural health monitoring is used to ensure the well-being of civil structures by detecting damage and estimating deterioration. Wind flow applies external loads to high-rise buildings, with the horizontal force component of the wind causing structural displacements in high-rise buildings. This study proposes a deep learning-based predictive model for measuring lateral displacement response in high-rise buildings. The proposed long short-term memory model functions as a sequence generator to generate displacements on building floors depending on the displacement statistics collected on the top floor. The model was trained with wind-induced displacement data for the top floor of a high-rise building as input. The outcomes demonstrate that the model can forecast wind-induced displacement on the remaining floors of a building. Further, displacement was predicted for each floor of the high-rise buildings at wind flow angles of 0° and 45°. The proposed model accurately predicted a high-rise building model's story drift and lateral displacement. The outcomes of this proposed work are anticipated to serve as a guide for assessing the overall lateral displacement of high-rise buildings.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.433-441
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• 2020

This study was conducted to identify the factors that influence the other behaviors taken by nursing students during online lectures. The study subjects were 304 nursing students in three universities. Data were collected between April 20 and 30, 2020, using by completing structured self report questionnaires. Data were analyzed using T-test, ANOVA, Pearson's correlation coefficient, and multiple regression using SPSS 26.0 program. In correlation analysis, significant negative correlations were found between other behaviors, interest(r=-17, p<.01), understanding(r=-19, p<.01), needs(r=-12, p<.05), learning motivation(r=-12, p<.05), self-regulation efficacy(r=-11, p<.05), learning confidence(r=-14, p<.05), lecture satisfaction(r=-22, p<.01), lecture flow(r=-24, p<.01). In the multiple regression analysis, learning confidence, prefer to discuss & present (β=.19), lecture flow(β=-.15), lecture satisfaction(β=-.15) were statistically significant factors that explained 10.6% of variance of other behaviors taken by nursing students during online lectures. Thus, we suggest to develop that teaching methods and programs to reduce other behaviors taken by nursing students during online lectures.