• The Journal of Korean Association of Computer Education
• /
• v.15 no.5
• /
• pp.11-22
• /
• 2012

This study aimed to analyze applying TAM model the process that flow factors such as 'harmony of challenge and technology' exert effects on learners' attitudes of keeping learning in STEAM class employing robots. For the study, the 'Energy and Tools' chapter of the science textbook for the 6th grade's second semester was re-arranged, and applied for 189 students, and among them, only the 174 usable data were used for the analysis. As a result of analysis, students' learning immersion factor(factor of harmony of challenge and technology) had deeper effects on the factor of ease of learning than usefulness of learning and this in turn, had an effect on their intention to keep learning ultimately through the factor of value of learning as the study found. As a result of research, it was found that for indications identified, in order to use robots in STEAM class, for the students' intention to keep learning, it's essential for learners to have proper and active attitudes towards learning and basic knowledge of robots, and aspects of values should be considered that based on this, robot can assist in learning and affect results of learning in STEAM class. On the other hand, the factors of ease of learning and the combination of the challenge and technology do not gives direct (+) effect on the intention to continue learning and the value for learning, respectively. However, each of the two factor has indirect influence on each of the dependent variable within the significant range, which is the reason the author includes the result of the analysis.

• Journal of Aerospace System Engineering
• /
• v.16 no.4
• /
• pp.109-115
• /
• 2022

The CNN (Convolution Neural Network) algorithm which combines a deep learning technique, and a computer vision technology, makes image classification feasible with the high-performance computing system. In this thesis, the CNN algorithm is applied to the classification problem, by using a typical deep learning framework of TensorFlow and machine learning techniques. The data set required for supervised learning is generated with the same type of bolts. some of which have undamaged threads, but others have damaged threads. The learning model with less quantity data showed good classification performance on detecting damage in a bolt image. Additionally, the model performance is reviewed by altering the quantity of convolution layers, or applying selectively the over and under fitting alleviation algorithm.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.4
• /
• pp.83-90
• /
• 1998

In this paper, we present neural network for control of Left Ventricular Assist Device(LVAD) system with a pneumatically driven mock circulation system. Beat rate(BR), Systole-Diastole Rate(SDR) and flow rate are collected as the main variables of the LVAD system. System modeling is completed using the neural network with input variables(BR, SBR, their derivatives, actual flow) and output variable(actual flow). It is necessary to apply high perfomance control techniques, since the LVAD system represent nonlinear and time-varing characteristics. Fortunately. the neural network can be applied to control of a nonlinear dynamic system by learning capability In this study, we identify the LVAD system with neural network and control the LVAD system by PID controller and neural network feedforward controller. The ability and effectiveness of controlling the LVAD system using the proposed algorithm will be demonstrated by experiment.

• Educational Technology International
• /
• v.16 no.2
• /
• pp.167-181
• /
• 2015

The purpose of this study was to examine the effects of using learner-created DST to communicate academic information on the creativity and flow of university students. The sample consisted of 100 undergraduate students who were assigned to either the DST group or the expository instruction group. The DST group created digital stories, and the expository group were taught using an expository instructional method. An achievement test, the Creativity Personality Scale (CPS), and the Flow State Scale (FSS) were used to collect data. The results showed that the achievement scores of the DST group were higher than those of the expository group, and the scores on the patience sub-factor of the CPS of the DST group significantly differed from those of the expository instruction group. Finally, the scores on the seven sub-factors of the FSS of the DST group differed significantly from those of the expository instruction group. The findings of this study suggest that the DST can be applied as teaching and learning method in a university class.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.9
• /
• pp.137-148
• /
• 2023

This study investigated the influence of major satisfaction on learning flow and self-efficacy of students majoring in culinary arts at local junior colleges. In the 2022-2 semester, 260 freshmen and sophomore college students majoring in culinary from five junior colleges in the Gwangju and Jeonnam regions were analyzed. For data processing, SPSS Ver. 25.0 was used. The data is used to measure reliability by Cronbach's α, t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis. The results of this study are as follows : First, there was a difference in satisfaction between freshmen and sophomores in major satisfaction with cooking related departments at local junior colleges. Second, there was a significant effect of satisfaction with cooking-related majors at local junior colleges on learning immersion. Third, there was a significant effect of satisfaction with cooking-related majors at local junior colleges on self-efficacy. In conclusion, it was found that major satisfaction affects learning immersion and self-efficacy for both students enrolled in cooking-related departments at local junior colleges. In the future, we suggest follow-up research on educational measures to increase learning immersion and self-efficacy for students who are not majoring in cooking in the high school curriculum and students who are insufficient in major classes due to part-time jobs during the semester.

• The Journal of Korean Association of Computer Education
• /
• v.6 no.2
• /
• pp.71-79
• /
• 2003

In the flow state, people are absorbed in their activities, while irrelevant thoughts and perceptions are screened out. In Csikszentmihalyi's view, the term 'flow' is used in describing the best feeling in human lives. Flow has been conceptualized as an optimal experience that stems from people's perception on challenges and skills in a given situation. We can easily observe that students who are learning from WBI experience flow state occasionally. If we identify the related factors that make flow experiences possible in WBI, the designers and instructors of WBI might be greatly benefitted by having ideas of how to provide optimal flow experiences to their students. In this article, we attempted to analyze what are critical Flow factors in WBI and provided guidelines on how to design optimal flow experiences in WBI.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.6
• /
• pp.904-914
• /
• 2008

An action-selection-mechanism(ASM) has been proposed to work as a fully connected finite state machine to deal with sequential behaviors as well as to allow a state in the task program to migrate to any state in the task, in which a primitive node in association with a state and its transitional conditions can be easily inserted/deleted. Also, such a primitive node can be learned by a shortest path-finding-based reinforcement learning technique. Specifically, we define a behavioral motivation as having state-dependent value as a primitive node for action selection, and then sequentially construct a network of behavioral motivations in such a way that the value of a parent node is allowed to flow into a child node by a releasing mechanism. A vertical path in a network represents a behavioral sequence. Here, such a tree for our proposed ASM can be newly generated and/or updated whenever a new behavior sequence is learned. To show the validity of our proposed ASM, experimental results of a mobile robot performing the task of pushing- a- box-in to- a-goal(PBIG) will be illustrated.

• Journal of Korea Game Society
• /
• v.8 no.2
• /
• pp.107-118
• /
• 2008

The world is changing fast from the information society to the knowledge society as the amount of knowledge exploded over through development of the information communication technology. Much efforts to use serious fames for students' learning is being made actively via international and domestic. It is expected that the results of this study would suggest how to utilize serious games in learning and training. The purpose of this study is to totally understanding of serious games that made to achieve educational goal based on specific character of game in which make flow, goal achievement, satisfaction. For this, introduce the game-based learning model to apply education and training, and trends development of serious games.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.3
• /
• pp.17-24
• /
• 2023

Based on the problem of 'Tatanic - Machine Learning from Disaster', a representative competition of Kaggle that presents challenges related to data science and solves them, we want to see how data preprocessing and model construction affect prediction accuracy and score. We compare and analyze the features by selecting seven top-ranked solutions with high scores, except when using redundant models or ensemble techniques. It was confirmed that most of the pretreatment has unique and differentiated characteristics, and although the pretreatment process was almost the same, there were differences in scores depending on the type of model. The comparative analysis study in this paper is expected to help participants in the kaggle competition and data science beginners by understanding the characteristics and analysis flow of the preprocessing methods of the top score participants.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.4
• /
• pp.635-640
• /
• 2021

In this paper, we describe a parking space identification system that can automatically identify empty parking lot spaces from a parking lot photo. This system is based on a deep learning technique, and the accuracy of the identification result is good by learning various existing parking lot images. It could be applied to the existing parking management system. This system was also developed as a smartphone application for easy testing. Therefore, if you take a picture of a parking lot through a smartphone camera, the captured image is automatically recognized and an empty parking space can be automatically identified.