• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.209-216
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• 2021

In this paper, I developed a scenario-based admission management virtual reality (SAM VR) content for practical training for nursing students and verified the effectiveness. The SAM VR contents used in the study was developed by the researcher using Gear VR and smartphone according to the standard practical procedure suggested by the Korea Acreditation Board of Nursing Education and Evaluation. In the 30 experimental groups who received practical training using SAM VR contents, learning flow, learning confidence, and learning satisfaction increased statistically significantly after the practical training (p<.001). In the control group, who received practical training in the traditional way, learning confidence increased after the practical training (p<.005), but there was no change in learning flow and learning satisfaction (p>.005). It was verified that the SAM VR contents are effective practical education contents for nursing students' learning flow, learning confidence and learning satisfaction.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.325-332
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• 2014

It is generally accepted that classification accuracy is affected by the number of learning documents, but there are few studies that show how this influences automatic text classification. This study is focused on evaluating the deviation-based classification model which is developed recently for genre-based classification and comparing it to other classification algorithms with the changing number of training documents. Experiment results show that the deviation-based classification model performs with a superior accuracy of 0.8 from categorizing 7 genres with only 21 training documents. This exceeds the accuracy of Bayesian and SVM. The Deviation-based classification model obtains strong feature selection capability even with small number of training documents because it learns subject information within genre while other methods use different learning process.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.31-38
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• 2011

A RAM-based Neural Net is a weightless neural network based on binary neural network. 3-D neural network using this paper is binary neural network with multiful information bits and store counts of training. Recognition method by MRD technique is based on the supervised learning. Therefore neural network by itself can not distinguish between the categories and well-separated categories of training data can achieve only through the performance. In this paper, unsupervised learning algorithm is proposed which is trained existing 3-D neural network without distinction of data, to distinguish between categories depending on the only input training patterns. The training data for proposed unsupervised learning provided by the NIST handwritten digits of MNIST which is consist of 0 to 9 multi-pattern, a randomly materials are used as training patterns. Through experiments, neural network is to determine the number of discriminator which each have an idea of the handwritten digits that can be interpreted.

• The Journal of Korean Association of Computer Education
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• v.23 no.3
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• pp.31-40
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• 2020

In this study, researchers developed the core teacher training program for elementary SW education. In order to develop the training program, the direction of training development was set, and the final version of the training was prepared through the pilot training. The main focus of the training program was to cultivate the teachers' ability to understand and apply SW education based on the national curriculum. After the program development, its effectiveness was analysed by applying it to the actual national training course. This training program was applied to the 2019 winter and summer teacher training courses organized by the Ministry of Education. To analyze the effectiveness of the training program, SW education teaching efficacy and satisfaction were surveyed. The results analysis found out the developed training program has positive effects on trained teachers.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.423-433
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• 2014

For the proper decision making and responsibility enhancement for an unexpected accident in large-scale facilities, it is important to train operators or first responders to minimize potential human errors and consequences resulted from them. Simulation technologies, including human-computer interaction and virtual reality, enables personnel to participate in simulated hazardous situations with a safe, interactive, repetitive way to perform these training activities. For the development of accident response training simulator, it is necessary to define components comprising the simulator and to integrate them for the given training purpose. In this paper, we analyze requirements of the training simulator, derive key components, and design the training simulator. Based on the design, we developed a prototype training simulator and verified the simulator through experiments.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.202-208
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• 2020

Body balance necessary for ordinary daily activities can be undermined by diverse causes. In this study, as a way to control such a problem, we have produced smart insole as a wearable device in the form of insole and developed analysis software evaluating body balance, which measures ground reaction force applied to each area of sole and Center of Pressure (COP). The software visualized changes in COP positions while a user was moving and average COP positions, and it is also capable of measuring the COP values in the Anterior-Posterior (AP) and Medial-Lateral (ML) areas of feet. Through gait analysis, it can analyze the time of walking, strides, speed, COP trajectory while walking, etc. In addition, we have developed training contents for body balance improvement designed in consideration of Y-Balance Test and Timed Up and Go (TUG) Test. They were established in virtual reality similar to daily living environment so that people can expect more effective training results regardless of places.

• Journal of IKEEE
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• v.22 no.3
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• pp.816-821
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• 2018

Recently, machine learning algorithms based on artificial neural networks started to be used widely as classifiers in the field of EEG research for emotion analysis and disease diagnosis. When a machine learning model is used to classify EEG data, if training data is composed of only data having similar characteristics, classification performance may be deteriorated when applied to data of another group. In this paper, we propose a method to construct training data set by selecting several groups of data using semi-supervised learning algorithm to improve these problems. We then compared the performance of the two models by training the model with a training data set consisting of data with similar characteristics to the training data set constructed using the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.69-75
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• 2022

This paper proposes a method of identifying the moment when a student with developmental disabilities needs assistance intervention in performing barista vocational training using virtual reality-based realistic contents. To this end, 21 students enrolled in a vocational training center for persons with disabilities were selected as study subjects. These students were trained to recognize the barista tools in a virtual reality environment. During the training, if students experienced difficulties and were unable to proceed further, they were asked to raise their hands or verbally request assistance. Using the collected data, two hypotheses were established based on the distance between the hand of the student and each barista tool in the virtual reality space in order to derive a criterion for judging the moment when an intervention is required. As a result of verifying the hypotheses, this study found that the cumulative distance from the hand of a student, who successfully finished the training without requiring an intervention, to the target barista tool as well as adjacent tools was significantly shorter than the cumulative distance to other barista tools.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.11-20
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• 2024

Among the generative models in Artificial Intelligence (AI), especially Generative Adversarial Network (GAN) has been successful in various applications such as image processing, density estimation, and style transfer. While the GAN models including Conditional GAN (CGAN), CycleGAN, BigGAN, have been extended and improved, researchers face challenges in real-world applications in specific domains such as disaster simulation, healthcare, and urban planning due to data scarcity and unstable learning causing Image distortion. This paper proposes a new progressive learning methodology called Progressive Step Training (PST) based on the Auxiliary Classifier GAN (ACGAN) that discriminates class labels, leveraging the progressive learning approach of the Progressive Growing of GAN (PGGAN). The PST model achieves 70.82% faster stabilization, 51.3% lower standard deviation, stable convergence of loss values in the later high resolution stages, and a 94.6% faster loss reduction compared to conventional methods.