• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.153-162
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• 2016

This paper proposes an optimal dynamic resource allocation method in IoT (Internet of Things) parking guidance system using Q-learning resource allocation model. In the proposed method, a resource allocation using a forecasting model based on Q-learning is employed for optimal utilization of parking guidance system. To demonstrate efficiency and availability of the proposed method, it is verified by computer simulation and practical testbed. Through simulation results, this paper proves that the proposed method can enhance total throughput, decrease penalty fee issued by SLA (Service Level Agreement) and reduce response time with the dynamic number of users.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.1-10
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• 2012

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and the life distribution applied exponential and log shaped type hazard function. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model could be confirmed. This paper, a failure data analysis of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and coefficient of determination.

• Journal of Engineering Education Research
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• v.15 no.6
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• pp.34-42
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• 2012

Problem solving by homework assignment is a process of practicing what were discussed in classrooms and thus is considered as an essential part of learning procedure in engineering education. We introduced the concept of cooperative learning, Student Teams-Achievement Division(STAD), to improve the students' learning efficiency by in-class problem solving. The instructor explained fundamental concepts, and lecture materials were handed out to compensate for the time of in-class team activity. Brief tests were given after every chapter, and team-based additional credits were given for the improvement comparing the average of previous tests of each student. This attempt of modified STAD was evaluated to have brought about a significant improvement in students' academic achievement, in addition to activating classroom atmosphere.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.2
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• pp.1-8
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• 2011

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure non-homogeneous Poisson process models presented and the life distribution applied extreme distribution which used to find the minimum (or the maximum) of a number of samples of various distributions. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than automatic error that is generally efficient model could be confirmed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error.

• Journal of IKEEE
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• v.25 no.4
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• pp.728-734
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• 2021

In recent years, there has been a strong interest in the end-to-end autonomous driving based on deep reinforcement learning. In this paper, we present a reward function of latent SAC deep reinforcement learning to improve the longitudinal driving performance of an agent vehicle. While the existing reward function significantly degrades the driving safety and efficiency, the proposed reward function is shown to maintain an appropriate headway distance while avoiding the front vehicle collision.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.399-404
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• 2020

There are many attempts to apply AI technology to diagnose facilities or improve the work efficiency of the power industry. The emergence of new machine learning technologies, such as deep learning, is accelerating the digital transformation of the power sector. The problem is that traditional power systems face security risks when adopting state-of-the-art AI systems. This adoption has convergence characteristics and reveals new cybersecurity threats and vulnerabilities to the power system. This paper deals with the security measures and implementations of the power system using machine learning. Through building a commercial facility operations forecasting system using machine learning technology utilizing power big data, this paper identifies and addresses security vulnerabilities that must compensated to protect customer information and power system safety. Furthermore, it provides security guidelines by generalizing security measures to be considered when applying AI.

• The KIPS Transactions:PartA
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• v.18A no.6
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• pp.241-248
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• 2011

Many learning systems are applying Self-Directed Learning to improve learning efficiency. The degree of understanding of the same learning contents can be different even if the learner's level is same. Therefore, it is difficult to represent an effective learning experience because the learning is progressed by the determined difficulty of learning and the learning process even thought the provided content is difficult to understand. In this paper, we augmented SCORM to reconstruct the learning contents which are suitable for the changed level of each learner in real-time. Also, we designed and implemented this augmented SCORM based DCOS(Differential Contents Organization System). In order to provide the suitable contents for each learner, DCOS reorganizes learning contents based on the learner's level, the learner's achievement of learning objectives, and the correlation between learning objects, that is the component of the learning content. Each 30 Each 30 students studied e-learning contents, which are constructed based on the existing System and DCOS respectively. And the average score and system's satisfaction of the students who studied DCOS based e-learning contents was higher.

• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.469-477
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• 2010

In this study, the new teaching-learning method for the students with disabilities is suggested and verified its efficiency. For this purpose, the current teaching-learning method will be examined and compared by attending Hangeul class of the computer training courses in the special schools and regular schools. In addition, for evaluation, the questionnaire survey on the existing teaching-learning method will be conducted for the students with disabilities of the special schools and after applying new teaching-learning method, the questionnaire survey will be conducted again. Through the conducted questionnaire surveys, the impact of the new teaching-learning method on the students with disabilities shall be analyzed.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.

• Archives of design research
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• v.20 no.2 s.70
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• pp.103-116
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• 2007

Art education in design has repeatedly stressed the importance of developing creativity. In the digital period, however, which shows rapid change in both forms and contents, it needs to be equipped with more flexible and systematic ways of approaching to the creativity development, especially involved with cultural diversity of the digital world. This paper primarily proposes a maximally efficient, productive creativity learning program in which the integration of expressive media and communication generates a comprehensive network of communicative information in the development of digital technologies, which, consequently, brings forth valuable cultural contents of art. The amalgamation of Won (2006)'s Prism Effect, with distinctive three devices, and the facilitator factors, with two different facilitators such as self-controlled and controlled plays, would function as a catalyst for cultural diversity in the digital forms and contents of art. And this will, consequently, result in producing a number of practices that can be classified and assorted for a later performance. This paper thus suggests a roadmap of how to develop the creativity learning program in which two categories of facilitators based on three thinking devices function to classify four activities. In addition, selected activities are shaped as a creativity learning program by generating learning practices with the formalizing instructional strategy that fit into a specialized educational environment and learners. The samples of loaming practice design show guidelines for practice and the results of learning activity. Therefore, the eventual goal of this paper would be to establish a creativity learning program that constitutes a highly systematized and modularized database to maximize the efficiency and productivity of the creativity development.