• The Korean Journal of Emergency Medical Services
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• v.20 no.3
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• pp.21-35
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• 2016

Purpose: The purpose of the study was to investigate the continuous effect of advanced cardiovascular life support (ACLS) simulation education according to Felder-Silverman learning style. Methods: A self-reported questionnaire was completed by 94 students of emergency medical technology and nursing. There were 50 female students (53.2%) and 88 students (93.6%) had basic life support certification. The study instruments included knowledge, performance, and confidence. Data were analyzed using SPSS v. 20.0. Results: The learning style consisted of reflective type (51.1%), sensory type (76.6%), visual type (63.8%), and sequential type (64.9%). There was a significant difference in continuous effect on performance by learning type. Conclusion: It is necessary to identify the learning style of students before simulation education in order to maintain continuous effect of ACLS education.

• Journal of IKEEE
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• v.24 no.2
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• pp.514-522
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• 2020

In Continuous Learning environment, we identify that the Catastrophic Forgetting phenomenon, which forgets the information of previously learned data, occurs easily between data having different domains. To control this phenomenon, we introduce how to measure the relationship between previously learned data and newly learned data through the distribution of the neural network's output, and how to use these measurements to mitigate the Catastrophic Forcing phenomenon. MNIST and EMNIST data were used for evaluation, and experiments showed an average 22.37% improvement in accuracy for previous data.

• Journal for History of Mathematics
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• v.32 no.3
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• pp.135-155
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• 2019

One of the reasons students have difficulty in studying probability is that they do not understand the meaning of mathematical terms precisely. One such term is a continuous random variable. Students tend not to think of the accurate definition of continuous random variables but to understand the definition of continuity of functions and the meaning of continuity in probability as equal. In this study, we try to explore the degree of pre-service teachers' understanding on the concept of continuation of functions and continuous random variables. To do this, the questionnaire items related to continuous random variables and continuity of functions were developed by experts and examined by pre-service teachers. Based on this, we make suggestions on implications for teaching and learning about continuous random variables.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.3
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.21-29
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• 2018

The purpose of this study is to explore supporting strategies for teaching and learning based on students' effect recognition and continuous intention in college flipped learning. It was analyzed 426 data by multivariate analysis of variance (MANOVA) by examining student's effect recognition and continuous intention on 15 flipped learning classes of K-university in Chungnam. The characteristics of learners were male, senior students, students who knew flipped learning, students who did not have previous experience, and students who were learning video at anytime. As a teaching strategy, it was found that effect recognition and continuous intention were high in the supplementary deepening flipped learning class and natural science or engineering area. As a teaching and learning supporting strategies, First, the university should develop and operate flipped class learning strategy program for females and low-grade students. Second, it should support the development of good flipped learning design and operation model of instructor. Third, it should support the development of high quality online learning contents that students can learn from time to time. Fourth, it should support the strengthening of teaching competency to develop and operate flipped learning classes. This study can be used as basic data to support and spread the effective flipped learning classes of the university in the future.

• The Journal of the Korea Contents Association
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• v.14 no.6
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• pp.477-490
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• 2014

The purpose of this study is to investigate factors influencing continuous usage intention of mobile learning and suggest practical strategies to enhance learners' continuous usage intention of mobile learning. In this study, we hypothesized that system quality, information quality, service quality and personal innovativeness have a positive effect on effort expectancy and performance expectancy, which ultimately have a positive effect on continuous usage intention. In order to examine structural relationship among variables, we surveyed 279 students who took courses at W Cyber University in 2013 fall semester. After collecting data, we examined causal relationship among variables using Structural Equation Modeling. The results of this study are as follows: First, system quality and personal innovativeness significantly affect effort expectancy. Second, information quality, service quality and personal innovativeness significantly affect performance expectancy. Last of all, effort expectancy and performance expectancy significantly affect continuous usage intention of mobile learning.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.4
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• pp.350-356
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• 1997

It is desirable for autonomous robot systems to possess the ability to behave in a smooth and continuous fashion when interacting with an unknown environment. Although Q-learning requires a lot of memory and time to optimize a series of actions in a continuous state space, it may not be easy to apply the method to such a real environment. In this paper, for continuous state space applications, to solve problem and a triangular type Q-value model\ulcorner This sounds very ackward. What is it you want to solve about the Q-value model. Our learning method can estimate a current Q-value by its relationship with the neighboring states and has the ability to learn its actions similar to that of Q-learning. Thus, our method can enable robots to move smoothly in a real environment. To show the validity of our method, navigation comparison with Q-learning are given and visual tracking simulation results involving an 2-DOF SCARA robot are also presented.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.145-156
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• 2022

Nowadays, the complexity of object tracking models among hardware applications has become a more in-demand duty to complete in various indeterminable environment tracking situations with multifunctional algorithm skills. In this paper, we propose a virtual city environment using AirSim (Aerial Informatics and Robotics Simulation - AirSim, CityEnvironment) and use the DQN (Deep Q-Learning) model of deep reinforcement learning model in the virtual environment. The proposed object tracking DQN network observes the environment using a deep reinforcement learning model that receives continuous images taken by a virtual environment simulation system as input to control the operation of a virtual drone. The deep reinforcement learning model is pre-trained using various existing continuous image sets. Since the existing various continuous image sets are image data of real environments and objects, it is implemented in 3D to track virtual environments and moving objects in them.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.1
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• pp.33-42
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• 2002

Recently, various experiments to apply reinforcement learning method to the self-learning intelligent control of continuous dynamic system have been reported in the machine learning related research community. The reports have produced mixed results of some successes and some failures, and show that the success of reinforcement learning method in application to the intelligent control of continuous control systems depends on the ability to combine proper function approximation method with temporal difference methods such as Q-learning and value iteration. One of the difficulties in using function approximation method in connection with temporal difference method is the absence of guarantee for the convergence of the algorithm. This paper provides a proof of convergence of a particular function approximation method based on \"barycentric interpolator\" which is known to be computationally more efficient than multilinear interpolation .

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.2
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• pp.49-59
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• 2022

This study aims to understand the effects of perceived usefulness, perceived ease, and expected matching on user satisfaction and continuous use intention for the learning management system (LMS). To this end, an online survey was conducted on K University students located in Chungcheongbuk-do, and 488 data were analyzed and utilized. First, it was found that the expected match of the learning management system had an effect on perceived usefulness and perceived ease. Second, it was found that perceived usefulness, perceived ease, and expected matching had an effect on user satisfaction. Perceived usefulness, user satisfaction and perceived ease of use were found to have an effect on the intention to continue using. It can be seen that the improvement of the quality of the university education system has an effect on the improvement of learners' learning effects and satisfaction. Accordingly, it is necessary to seek various ways to continuously manage the quality of the learning management system.