• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.129-136
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• 2023

In this paper, we propose the positive effects of Virtual Reality(VR) sports classes and the foundation for VR sports to become the basis of lifelong sports education through the application of physical education classes in sports virtual reality programs are to be provided. For this purpose, the effect of VR sports experience on sports satisfaction, sports immersion, and sports attitude factors was investigated for 281 elementary school students in Busan. Results It was found that VR sports experience had a significant effect on sports satisfaction, sports satisfaction had a significant effect on sports immersion and sports attitude, and sports immersion had a significant effect on sports attitude. The great advantage of sports virtual reality is that sports activities for items that are difficult to deal with in physical education classes and unpopular items will be easily performed. In addition, by using a program that links physical education classes with English and mathematics, physical education will be recognized as a convergence subject by elementary school students, and at the same time, it will become an integrated subject that can acquire fun elements and learning elements at the same time through play or games.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.59-68
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• 2009

This paper aims to explore the factors of learner's flow and to develop and validate a scale to measure the flow in Massive Multiplayer Online Role Playing Game(MMORPG) for education. First of all, potential factors were drawn through literature review. The potential stage comprises 6 factors(learner's psychological characteristics, learner's skill, importance of game, environment for learner, instructional design, and instructional environment) and 16 subfactors. With total 48 items developed. a survey was carried out among 293 elementary learners who had been participating in a commercial MMORPG for English skills to measure their flow in the MMORPG by utilizing the potential scale. Using the responses collected from 288 respondents, exploratory factor analysis, reliability analysis, and confirmatory factor analysis were performed. The expository factor analysis showed that items within each sub-factors could be bound into one factor. That is, the variables evaluating learner's flow were divided into six factors(learner's psychological characteristics, learner's skill, importance of game, environment for learner, instructional design, and instructional environment). And these factors were interpreted consisting of 16 sub-ones. Reliability estimates indicated that the evaluation tool had good internal consistency. The confirmatory factor analysis did confirm the model suggested by the expository factor analysis. Over fit measures(CFI, NFI, NNFI) showed the good suitability of the model. Findings of this study confirmed the validity and reliability of the scale to measure learner's flow in MMORPG.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.182-189
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• 2016

For high accuracy of forecast using numerical weather prediction models, we need to get weather observation data that are large and high dense. Korea Meteorological Administration (KMA) mantains Automatic Weather Stations (AWSs) to get weather observation data, but their installation and maintenance costs are high. Mini-AWS is a very compact automatic weather station that can measure and record temperature, humidity, and pressure. In contrast to AWS, costs of Mini-AWS's installation and maintenance are low. It also has a little space restraints for installing. So it is easier than AWS to install mini-AWS on places where we want to get weather observation data. But we cannot use the data observed from Mini-AWSs directly, because it can be affected by surrounding. In this paper, we suggest a correcting method for using pressure data observed from Mini-AWS as weather observation data. We carried out preconditioning process on pressure data from Mini-AWS. Then they were corrected by using machine learning methods with the aim of adjusting to pressure data of the AWS closest to them. Our experimental results showed that corrected pressure data are in regulation and our correcting method using SVR showed very good performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.95-100
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• 2020

This paper related with the performance evaluation of mSE-MMA (modified Signed Error-Multi Modulus Algorithm) adaptive equalization algorithm which is possible to reduce the distortion that is occurs in nonlinear communication channel like as additive noise, intersymbol interference and fading. The SE-MMA algorithm are emerged in order to reducing the computational load compared to the presently MMA algorithm, it has the degraded equalization performance by this. In order to improve the performance degradation of SE-MMA, the mSE-MMA controls the step size according to the existence of arbitrary radius circle of equalizer output is centered at transmitted symbol point. The performance of proposed mSE-MMA algorithm were compared to present SE-MMA using the same channel and noise environment by computer simulation. For this, the recoverd signal constellation which is the output of equalizer, residual isi and MD (Maximum Distortion), MSE learning curve which is represents the convergence performance and SER which is represents the roburstness of noise were used as performance index. As a result of simulation, the mSE-MMA has more superior to the SE-MMA in every performance index, and was confirmed that mSE-MMA has roburstness to the noise in the SER performance than SE-MMA especially.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.772-780
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• 2008

K-means is one of the simplest unsupervised learning algorithms that solve the clustering problem. However K-means suffers the basic shortcoming: the number of clusters k has to be known in advance. In this paper, we propose extensions of X-means, which can estimate the number of clusters using Bayesian information criterion(BIC). We introduce two different versions of algorithm: modified X-means(MX-means) and generalized X-means(GX-means), which employ one full covariance matrix for one cluster and so can estimate the number of clusters efficiently without severe over-fitting which X-means suffers due to its spherical cluster assumption. The algorithms start with one cluster and try to split a cluster iteratively to maximize the BIC score. The former uses K-means algorithm to find a set of optimal clusters with current k, which makes it simple and fast. However it generates wrongly estimated centers when the clusters are overlapped. The latter uses EM algorithm to estimate the parameters and generates more stable clusters even when the clusters are overlapped. Experiments with synthetic data show that the purposed methods can provide a robust estimate of the number of clusters and cluster parameters compared to other existing top-down algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.347-350
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• 2018

In order for an IoT system to automatically make the house temperature pleasant for the user, the system needs to predict the optimal start-up time of air-conditioner or heater to get to the temperature that the user has set. Predicting the optimal start-up time is important because it prevents extra fee from the unnecessary operation of the air-conditioner and heater. This paper introduces an ANN(Artificial Neural Network) and an IoT system that predicts the cooling and heating time in households using air-conditioner and heater. Many variables such as house structure, house size, and external weather condition affect the cooling and heating. Out of the many variables, measurable variables such as house temperature, house humidity, outdoor temperature, outdoor humidity, wind speed, wind direction, and wind chill was used to create training data for constructing the model. After constructing the ANN model, an IoT system that uses the model was developed. The IoT system comprises of a main system powered by Raspberry Pi 3 and a mobile application powered by Android. The mobile's GPS sensor and an developed feature used to predict user's return.

• The Journal of Korean Association of Computer Education
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• v.23 no.4
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• pp.23-33
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• 2020

Online judge provides compilation, execution, and immediate feedback on the source submitted by the learner, and ensures the accuracy and reliability of the evaluation, but it's difficult to select the language according to the level of the learner because most of them provide only textual language. In this study, a block language for online judge was developed and applied to high school classes, and the difference in self-efficacy between the block language and the textual language group was confirmed. It was found that Block language group have more ability expectation to overcome disgust experience than textual language group and Textual language group have significant decrease in ability expectation to start activity and to continue activity. It implies that Block language has an effect on self-efficacy for afterward programming activities, and methods of teaching, learning and evaluation should be devised in the case of textual language so that student's self-efficacy does not deteriorate at the initial and ongoing stage of activity. The results of this study are meaningful in that it provide various implications of methods for enhancing self-efficacy in high school class of programming.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.1-9
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• 2022

In this paper, we propose a vector-based augmentation technique that can generate data required for crack detection and a ConvNet(Convolutional Neural Network) technique that can learn it. Detecting cracks quickly and accurately is an important technology to prevent building collapse and fall accidents in advance. In order to solve this problem with artificial intelligence, it is essential to obtain a large amount of data, but it is difficult to obtain a large amount of crack data because the situation for obtaining an actual crack image is mostly dangerous. This problem of database construction can be alleviated with elastic distortion, which increases the amount of data by applying deformation to a specific artificial part. In this paper, the improved crack pattern results are modeled using ConvNet. Rather than elastic distortion, our method can obtain results similar to the actual crack pattern. By designing the crack data augmentation based on a vector, rather than the pixel unit used in general data augmentation, excellent results can be obtained in terms of the amount of crack change. As a result, in this paper, even though a small number of crack data were used as input, a crack database can be efficiently constructed by generating various crack directions and patterns.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Journal of The Korean Association of Information Education
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• v.18 no.4
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• pp.499-508
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• 2014

Developing Computational Thinking is getting attention as the importance of SW is emphasized. Also programming education is getting attention, especially, various researches that utilize robot in programming education are being carried out. This study focused on compensating the defects of the prior robot programming education and exploring the way of utilizing web based tool 2.0 while putting emphasis on communication and cooperation. This plan is based on $Gagn{\acute{e}}$ & Briggs nine events of instruction and can be used to implement cooperative learning with the Web 2.0 based tools at every instructional events. Tests for learner's cooperation were done before and after this new plan to evaluate its value. The result proves that this plan had a positive influence on improving learner's cooperative attitude.