• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.567-568
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• 2015

인터넷이 활성화 되면서 웹기반 교육으로 e-Training이 발전되었다. e-Training은 업무에 필요한 수행능력을 습득 향상 시키기 위하여 정보통신 기술, 장비, 환경을 활용하여 실시하는 교육훈련이다. e-Training에 포함되는 기술에는 시뮬레이션, 3D 가상현실, 증강현실 등이 있다. 본 연구에서는 가상현실 기반의 디젤엔진훈련 시뮬레이션을 개발하였다. 제안한 시스템은 3차원 디스플레이 시스템을 통해 몰입감과 상호작용 교육훈련방식을 도입하여 3D입체형 화면안의 시야가 실제 현실과 같은 몰입의 효과가 있으며 엔진부품의 이름과 기능 등을 게임화를 통해 교육 훈련하는 시스템으로써 시뮬레이션을 통해 필요한 문제풀이를 재미있게 할 수 있어 학습능률에 효과가 있다.

• Journal of The Korean Association of Information Education
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• v.4 no.1
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• pp.98-108
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• 2000

Recently, web based virtual trainings in the teacher training are on the increase. However, we have evaluated only about hardware systems of virtual training systems and considered a question in their good aspects. Now, it is time we evaluate the problems of virtual training systems and search the solutions of them. However, we have no chance to evaluate of problems of each virtual training systems. Therefore, we will analysis management aspects some web based virtual training courses and propose the solutions of them.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1347-1355
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• 2019

Deep learning approach based on convolution neural network (CNN) has extensively studied in the field of computer vision. However, periocular feature extraction using CNN was not well studied because it is practically impossible to collect large volume of biometric data. This study uses the ResNet model which was trained with the ImageNet dataset. To overcome the problem of insufficient training data, we focused on the training of multi-layer perception (MLP) having simple structure rather than training the CNN having complex structure. It first extracts features using the pretrained ResNet model and reduces the feature dimension by principle component analysis (PCA), then trains a MLP classifier. Experimental results with the public periocular dataset UBIPr show that the proposed method is effective in person authentication using periocular region. Especially it has the advantage which can be directly applied for other biometric traits.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.787-790
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• 2017

Data quality is the main issue in the classification problems; generally, the presence of noisy instances in the training dataset will not lead to robust classification performance. Such instances may cause the generated decision tree to suffer from over-fitting and its accuracy may decrease. Decision trees are useful, efficient, and commonly used for solving various real world classification problems in data mining. In this paper, we introduce a preprocessing technique to improve the classification accuracy rates of the C4.5 decision tree algorithm. In the proposed preprocessing method, we applied the naive Bayes classifier to remove the noisy instances from the training dataset. We applied our proposed method to a real e-commerce sales dataset to test the performance of the proposed algorithm against the existing C4.5 decision tree classifier. As the experimental results, the proposed method improved the classification accuracy by 8.5% and 14.32% using training dataset and 10-fold crossvalidation, respectively.

• Journal of the Semiconductor & Display Technology
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• v.12 no.1
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• pp.1-7
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• 2013

Grey-level statistical models have been widely used in many applications for object location and identification. However, conventional models yield some problems in model refinement when training images are not properly aligned, and have difficulties for real-time recognition of arbitrarily rotated models. This paper presents improved grey-level statistical models that align training images using image or feature matching to overcome problems in model refinement of conventional models, and that enable real-time recognition of arbitrarily rotated objects using efficient hierarchical search methods. Edges or features extracted from a mean training image are used for accurate alignment of models in the search image. On the aligned position and orientation, fitness measure based on grey-level statistical models is computed for object recognition. It is demonstrated in various experiments in PCB inspection that proposed methods are superior to conventional methods in recognition accuracy and speed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.384-388
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• 1996

The closed-loop system modeling of an Active/semiactive suspension system has been accomplished through an artificial neural Networks. The 7DOF full model as the system equation of motion has been derived and the output feedback linear quadratic regulator has been designed for the control purpose. For the neural networks training set of a sample data has been obtained through the computer simulation. A 7DOF full model with LQR controller simulated under the several road conditions such as sinusoidal bumps and the rectangular bumps. A general multilayer perceptron neural network is used for the dynamic modeling and the target outputs are feedback to the input layer. The Backpropagation method is used as the training algorithm. The modeling of system and the model validation have been shown through computer simulations.

• ETRI Journal
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• v.33 no.6
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• pp.914-923
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• 2011

This paper concerns robust and reliable speaker model training for text-independent speaker verification. The baseline speaker modeling approach is the Gaussian mixture model (GMM). In text-independent speaker verification, the amount of speech data may be different for speakers. However, we still wish the modeling approach to perform equally well for all speakers. Besides, the modeling technique must be least vulnerable against unseen data. A traditional approach for GMM training is expectation maximization (EM) method, which is known for its overfitting problem and its weakness in handling insufficient training data. To tackle these problems, variational approximation is proposed. Variational approaches are known to be robust against overtraining and data insufficiency. We evaluated the proposed approach on two different databases, namely KING and TFarsdat. The experiments show that the proposed approach improves the performance on TFarsdat and KING databases by 0.56% and 4.81%, respectively. Also, the experiments show that the variationally optimized GMM is more robust against noise and the verification error rate in noisy environments for TFarsdat dataset decreases by 1.52%.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.81-84
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• 2021

The article substantiates the need to use innovative pedagogical technologies as an effective mechanism for implementing the idea of advanced vocational education, highlights the main components of the idea of anticipation, highlights the experience of using modern technologies in institutions of secondary vocational education. The purpose of the study is to increase the level of professional training of qualified workers and specialists through the effective choice of methodological tools aimed at the formation of professional competencies on the basis of an educational institution.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.279-283
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• 2022

The main purpose of the study is to analyze the specifics of training future preschool education specialists for the development of conscious citizenship and environmental values of students. The methodology includes a number of theoretical methods of analysis. The importance of the issue of professional training of a specialist in a preschool institution is proved, along with an increase in the general and professional culture of a teacher, his readiness to work in alternative institutions for preschool children, it is necessary to develop creative pedagogical thinking, independence, motivational and value attitude to the profession, readiness for further self-education. Based on the results of the research, the key aspects of training future preschool education specialists for the development of conscious citizenship and environmental values of students were determined.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.187-193
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• 2022

The study deals with the problem of transformation of the teacher's role in the information society. A comparative analysis of the competencies of the teacher, declared in the Pedagogical Constitution of Europe, the documents of the New Ukrainian School, the scientific research of contemporary scholars was conducted. The correlation analysis of the survey results for teachers and students' parents on their expectations of contemporary teachers was presented. It was noted that the analyzed views of scientists, legislative documents, and the results of sociological research help to modify the educational process of competitive and effective prospective teachers' training. Based on the survey results the acmeograms of the teacher's main role positions as an orientation for training of prospective teachers were developed.