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

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• The Journal of Korean Society for School & Community Health Education
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• v.12 no.3
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• pp.17-28
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• 2011

Backgroud & Objectives: The purpose of the study was to develop an innovative blended learning method on life style and health promotion and evaluate the educational effects for university students. Methods: The blended learning was developed to combine face-to-face lecture(off-line lecture) and on-line lecture that applied the subject of life style and health promotion. This course is a coordinated effort towards providing 5 topics of lifestyle such as smoking, alcohol, exercise, diet, and stress management. This has been verified by an expert in the field of nursing, education, e-learning technician and students. Participants were different part of university students (n=28) with major enrolled in a general culture course for 2 credits which composed of 8 sessions of each 2-hour in the first semester of 2010. The study was a one group posttest design. A self-report about health knowledge, attitude, and health behavior was organized by content analysis after the sessions. Results: Positive feedbacks from students were reflected in the outcome. Student regarded good lifestyle as being the most important. Student concerned those on-line lectures are not only available at most time and site, but also good for individualization, visual understanding and interest. Face-to-face lecture provided student a chance to integrate with knowledge and experience and had desire to improve good lifestyle and health promotion. Conclusions: The blended learning method on good lifestyle and health could make a best use of improvement for knowledge, attitude and behavior concerning. It is needed to identify the long term effects of a blended learning for further study.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.85-91
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• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

• Journal of Digital Contents Society
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• v.11 no.2
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• pp.265-276
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• 2010

The purpose of this study was to investigate the Korean Local Government's recognition on the educational gap among regions in the filed of adult learning including vocational education and life long-learning. The study also tried to figure out the local government's recognition and infrastructure of e-Learning which is suggested as one of the solutions on the regional gap of educational opportunities and quality. This study took 12 HRD(Human Resources Development) centers funded and operated by the Korean Local Governments except Seoul and Kyong-Gi classified by the metropolitan areas in Korea. As a result, firstly it was found that the local governments had perception on the difference and gap of educational opportunities and quality among regions in the area of adult education. Especially, the perception was relatively more serious on quality than quantity. Secondly, the result showed the large gap among regions on the area of opening educational and training programs, the quality of teachers and tutors, the effectiveness and outcomes of educational programs. Thirdly, they perceived more serious educational gap on face-to-face classes rather than e-Learning in the context of educational methodology. It also revealed that the local governments had relatively better foundations on physical systems than other infrastructures and resources such as human-ware, culture-ware and soft-ware(contents, programs etc.). It was recommended to consider these findings in developing and implementing future educational policies to solve the problem of regional gap on education.

• Journal of the East Asian Society of Dietary Life
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• v.20 no.5
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• pp.776-784
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• 2010

This study investigated causal relationships between goal orientation, sales and performance towards increasing the effectiveness of up-selling in internal promotion methods in family restaurants and provided implications about the hiring and training of sales people. The subjects were 232 sales people in family restaurants. The data were collected by self-administered questionnaires and analyzed by exploratory factor analysis, reliability analysis, comparative analysis of the average, and regression analysis. Results, showed that variations in goal orientation, sales, and performance depended on the age and experience of salespeople and that goal orientation makes adaptive selling more effective. It turned out that effort selling affects up-selling result than adaptive selling. Long-term workers were better than short-term workers in goal orientation, selling, and up-selling results, so human resource management needs to implement a long-term plan to enhance these effects. And, because effort selling is more effective than adaptive selling in up-selling results in family restaurants, effort selling requires training.

• The Journal of the Korea Contents Association
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• v.12 no.6
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• pp.518-529
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• 2012

Integration of Europe was started when European Union Treaty was concluded at Maastricht for the first time on December, 1991. Europe which may be called as a cradle of modern national state has realized a single Europe not only in the socio-economic integration field but also in the political field. Under this background, it is considered that life-long education policy for developing a new integrated growth engine of EU requires educational response that may get ready for socio-economic environmental transformation more than anything else. In particular, this policy is faced with an important task of having to achieve harmony of efficiency through diversity and mutual coordination in pursuing cooperation and integrated development of life-long education field. However, notwithstanding their efforts, since 2008, some countries of EU were faced with economic crisis due to economic recession and this situation starts to drive the whole Europe even to the point of their financial crisis at last. This crisis is currently shaking socio-economic integration of EU. This study intends to observe a status of establishing life-long education system and promoting a policy for socio-economic integration of EU and to analyze as to what kind of relevance adult participation rate of life-long learning among the countries belonged to EU has with per capita income and to explore as whether socio-economic integration among member countries could be sustained based on problems of integrative life-long education system under the economic crisis of EU. In addition, through this study, an implication required for presenting a new paradigm conversion, policy establishment and development direction for the life-long education of our country is intended to be deduced.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.909-918
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• 2020

This study, not only investigates the important role of lifelong learning in shaping young people's knowledge and in maximizing their potential, but also aims to shed light on the influencing factors of lifelong learning of young people in Vietnam. The author applied STATA and SPSS to analyze quantitative data collected from questionnaires with 332 respondents aged between 19 years old and 24 years old. Based on a holistic review of literature, this study concludes that four driver factors affect young people's lifelong learning ability, comprising: organizational culture, motivation, human resource development, and domestic private type of enterprise. The results emphasize the positivity of organizational culture, human resource development, and the nature of work, especially organizational culture and human resource development, which are dominant reasons for young people to maintain lifelong learning. The relationship between demographics and lifelong learning was tested and it indicated that male has a stronger interest in learning than female. The result of the study also shows the impact of different types of business sectors on employees' learning intentions. It points out that the domestic private type of enterprise is the most effective factor that has a positive relationship with the lifelong learning of the individual.

• Journal of Family Resource Management and Policy Review
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• v.24 no.1
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• pp.41-53
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• 2020

The purpose of this study was to identify the factors that affect middle-old aged housewives' participation in lifelong learning as a part of human resource development. Through purposive sampling, the study recruited 163 full-time housewives over age 40 years who live in C City. As a result, first, 87.1 percent of all respondents, or 142, said they were willing to participate in lifelong learning in the future. There was no statistically significant difference in the results of cross-checking by age, educational background and monthly household income variables. Additionally, we used cluster analysis to measure differences in participation intentions according to the perception of human resource development of middle-old aged full-time housewives. The perception variable of lifelong learning is: First, Cognitive degree, second, importance, third, activation awareness. Cluster 1(n=16) was divided into generally low-perception types, such as cognitive degree, importance, and life-long learning activation of the C city, while Cluster 2(n=61) was classified as a type of person who thinks that lifelong learning is important to life and Cluster 3(n=86) was generally classified as a type with a higher lifelong learning perception. and we found that there was no difference in the intention to participate in lifelong learning by all cluster Lastly, we found that participants who valued human resource development scored significantly higher on measures of cognition than those who did not value it. Based on these results, we advocates social change that encourages the cultivation of talent through lifelong learning programs that can positively affect one's unique identity, not just wife and mother, and provide opportunities for self-development.

• Electronics and Telecommunications Trends
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• v.38 no.3
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• pp.38-46
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• 2023

The value of living a long and healthy life without suffering has increased owing to aging populations, transition to welfare societies, and global interest in health deriving from the novel coronavirus disease pandemic. New drug development has gained attention as both a tool to improve the quality of life and high-value market, with blockbuster drugs potentially generating over 10 billion dollars in annual revenue. However, for newly discovered substances to be used as drugs, various properties must be verified over a long period in a time-consuming and costly process. Recently, the development of artificial intelligence technologies, such as deep and reinforcement learning, has led to significant changes in drug development by enabling the effective identification of drug candidates that satisfy desired properties. We explore and discuss trends in artificial intelligence for de novo drug design.