• 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.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• The Journal of Sustainable Design and Educational Environment Research
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• v.4 no.4
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• pp.25-38
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• 2004

Schools have focused so far on a student-oriented education. As the roles of schools, however, have been increasingly emphasized in the information society, community-centered functions are now additionally required. Beyond simply allowing communities to utilize selected facilities, schools can conduct re-education programs for community residents and actively use their facilities for such purposes. As explained above, schools must continuously evolve to meet current needs and demands, such as by offering special classes and utilizing learning facilities in the elementary levels to promote learning in ever-changing societies. This study analyzed the functions of school facilities to communities, as well as the educational functions involved in teaching-learning processes, in light of the advent of a knowledge and information society. Through analysis, the types of information facilities in elementary schools were derived. On the basis of such derived types, systematic and reasonable methods to estimate the scope were suggested.

• Journal of The Korean Society of Integrative Medicine
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• v.2 no.4
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• pp.83-90
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• 2014

Purpose : PBL is a teaching method to learn problem-solving process. Present study was to investigate the predictors of academic achievement when PBL is applied to students of physical therapy. Method : We Performed in-depth interviews and analyzed using the qualitative analysis by randomly assigning 5 of twenty four students who attended the class. Result : The results are classified into two categories and six sub-subjects. Based on two system of classification, PBL showed the learning effect through problem-solving methods because students directly participated in these processes. Also, students need to clearly comprehend communication method and decision-making process in order to progress the class smoothly. Conclusion : Therefore, futher studies will be continuously needed on how we apply PBL to various curriculums of physical therapy.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.436-440
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• 1992

As an approach to design the intelligent controller, this paper proposes a new FNN(Fuzzy Neural Network) control method using the hybrid combination of fuzzy logic control and neural network. The proposed FNN controller has two important capabilities, namely, adaptation and learning. These functions are performed by the following process. Firstly, identification of the parameters and estimation of the states for the unknown plant are achieved by the MNN(Model Neural Network) which is continuously trained on-line. And secondly, the learning is performed by FNN controller. The error back propagation algorithm is adopted as a learning technique. The effectiveness of the proposed method will be demonstrated by computer simulation of a two d.o.f. robot manipulator.

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.79-84
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• 2018

The government developed National Competency Standards and expands field friendly education for innovation of industrial field based education training. NCS is the national level of standards that the government systemized knowledge, skills and attitudes required to work in industrial fields by each industry and each level. This study was intended to research NCS education contents of an introduction of dental hygienics, which is a basic major subject among subjects of dental hygiene course, to present learning goals accordingly, and to be used as a basic resource of NCS field oriented classes of dental hygienists through the comparison before and after. In case of the dental hygiene course, dental hygienists are performing important core tasks as clinicians at dental offices. Therefore, such comprehensive and professional performance abilities as scaling, oral prophylaxis and oral health education are required at the fields. The education process and education contents for this should be researched continuously.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.12
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• pp.691-696
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. When induction motor is continuously used long time, its electrical and mechanical Parameters will change, which degrade the Performance of PID controller considerably. This Paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• Journal of Engineering Education Research
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• v.24 no.6
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• pp.79-85
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• 2021

In a future society where cutting-edge science technology such as artificial intelligence becomes commonplace, the demand for talented people with basic knowledge of mathematics and science is expected to increase continuously, and the educational infrastructure suitable for the characteristics of future generations is still insufficient. In particular, in the case of students taking convergence courses including practical training, there was a problem in communication with the instructor. In this study, we looked at the current status of distance learning at domestic universities that came suddenly due to the global pandemic of COVID-19. In addition, a case study of the use of technology was conducted to facilitate the interaction between instructors and learners through case analysis of distance classes in convergence subjects. Therefore, this study aims to introduce the case of developing lecture contents for smooth convergence education in a non-face-to-face educational environment targeting the developed AI convergence courses and applying them to the education of enrolled students.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.247-253
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• 2021

The outbreak of the deadly virus COVID-19 is said to infect 17.3Cr people around the globe since 2019. This outbreak is continuously affecting a lot of new people till this day and, most of it is said to under control. However, vaccines introduced around the world can help mitigate the risk of the virus. Apart from medical professionals, prediction models are also said to combinedly help predict the risk of infection based on given datasets. This paper is based on publication of a machine learning approach using regression models to predict the output based on dataset which have indictors grouped based on active, tested, recovered and critical cases along with regions and cities covering most of it from Dubai. Hence, the active cases are tested based on the other indicators and other attributes. The coefficient of the determination (r²) is 0.96, which is considered promising. This model can be used as an frame work, among others, to predict the resources related to the dangerous outbreak.