• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.3
• /
• pp.363-368
• /
• 2004

In this paper, the robustness property of 2nd-order iterative learning control(ILC) method for a class of linear and nonlinear discrete-time dynamic systems is studied. 2nd-order ILC method has the PD-type learning algorithm based on both time-domain performance and iteration-domain performance. It is proved that the 2nd-order ILC method has robustness in the presence of state disturbances, measurement noise and initial state error. In the absence of state disturbances, measurement noise and initialization error, the convergence of the 2nd-order ILC algorithm is guaranteed. A numerical example is given to show the robustness and convergence property according to the learning parameters.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.9
• /
• pp.533-539
• /
• 2003

Reinforcement learning (RL) has been widely used as a learning mechanism of an artificial life system. However, RL usually suffers from slow convergence to the optimum state-action sequence or a sequence of stimulus-response (SR) behaviors, and may not correctly work in non-Markov processes. In this paper, first, to cope with slow-convergence problem, if some state-action pairs are considered as disturbance for optimum sequence, then they no to be eliminated in long-term memory (LTM), where such disturbances are found by a shortest path-finding algorithm. This process is shown to let the system get an enhanced learning speed. Second, to partly solve a non-Markov problem, if a stimulus is frequently met in a searching-process, then the stimulus will be classified as a sequential percept for a non-Markov hidden state. And thus, a correct behavior for a non-Markov hidden state can be learned as in a Markov environment. To show the validity of our proposed learning technologies, several simulation result j will be illustrated.

• International journal of advanced smart convergence
• /
• v.5 no.4
• /
• pp.32-39
• /
• 2016

Machine learning is a field of artificial intelligence (AI), and a technology that collects, forecasts, and analyzes securities data is developed upon machine learning. The difference between using machine learning and not using machine learning is that machine learning-seems similar to big data-studies and collects data by itself which big data cannot do. Machine learning can be utilized, for example, to recognize a certain pattern of an object and find a criminal or a vehicle used in a crime. To achieve similar intelligent tasks, data must be more effectively collected than before. In this paper, we propose a method of effectively collecting data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.120-122
• /
• 2016

While smart learning have been introduced for more learning effect, this study is to understand students' smart learning acceptance using TAM model. Through using the result of the study, it is to provide suggestions for the improvement of smart learning effect and the acceptance using various multi media on thew view of students.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.131-133
• /
• 2016

While smart learning have been introduced for more learning effect, this study is to understand teacher's smart learning acceptance using TAM model. Through using the result of the study, it is to provide suggestions for the improvement of smart learning effect and the acceptance using various multi media on thew view of teachers.

• Journal of Industrial Convergence
• /
• v.7 no.2
• /
• pp.33-44
• /
• 2009

I first present a set of features for distinguishing group learning from other concepts. I then develop a framework for understanding group learning that focuses on learning's basic processes at the group level of analysis: storage, and retrieval.

• Journal of Industrial Convergence
• /
• v.7 no.2
• /
• pp.45-57
• /
• 2009

I first present a set of features for distinguishing group learning from other concepts. I then develop a framework for understanding group learning that focuses on learning's basic processes at the group level of analysis: sharing.

• Journal of the Korea Convergence Society
• /
• v.11 no.1
• /
• pp.119-130
• /
• 2020

This study developed an English conversation learning program in immersive virtual reality (VR) environments and compared its effects with non-immersive VR environment using a computer monitor. The effects of the program was assessed using language anxiety and learning achievement. The results indicated that students' language anxiety decreased significantly after learning English conversation in VR environment, but there was no difference between immersive and non-immersive VR. The two VR conditions also produced similar learning outcomes. Future research on immersive VR need to address cyber sickness problems and develop effective learning contents in order to realize its potential for learning.

• Journal of the Korea Convergence Society
• /
• v.10 no.12
• /
• pp.503-510
• /
• 2019

This study aimed to analyze the effects of nursing students' learning motivation, learning attitude and academic self-efficacy on their application of reflective journal with teaching feedback. For this retrospective comparative study data from 190 undergraduate students in the health assessment skills course from 2018 to 2019. The collected data were analyzed by chi-square test, independent t-test, Ancova. After writing reflection journal with teaching feedback showed significant difference in learning motivation(t=2.10, p=.037) and learning attitude (t= 4.54, p=.034) compared to writing reflection journal without teaching feedback. However, no significant difference was found between reflection journal with teaching feedback and reflection journal without teaching feedback. These results suggest writing reflection with teaching feedback is an effective strategy for improving learning motivation and learning attitude.

• Journal of the Korea Convergence Society
• /
• v.10 no.5
• /
• pp.9-16
• /
• 2019

In this paper, it will analyze security problems, so technology's potential can apply to business security area. First, in order to deep learning do security tasks sufficiently in the business area, deep learning requires repetitive learning with large amounts of data. In this paper, to acquire learning ability to do stable business tasks, it must detect abnormal IP packets and attack such as normal software with malicious code. Therefore, this paper will analyze whether deep learning has the cognitive ability to detect various attack. In this paper, to deep learning to reach the system and reliably execute the business model which has problem, this paper will develop deep learning technology which is equipped with security engine to analyze new IP about Session and do log analysis and solve the problem of mathematical role which can extract abnormal data and distinguish infringement of system data. Then it will apply to business model to drop the vulnerability and improve the business performance.