• 대한전기학회논문지:시스템및제어부문D
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• 제52권9호
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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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• 제29권5호
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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.

• 한국학교ㆍ지역보건교육학회지
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• 제12권3호
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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.

• 한국기계가공학회지
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• 제20권12호
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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.

• 디지털콘텐츠학회 논문지
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• 제11권2호
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• pp.265-276
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• 2010

이 연구의 목적은 지역 단위의 인적자원개발 정책 추진 주체인 지방자치단체를 중심으로 성인교육 부문의 지역 교육격차 실태에 대한 인식 수준을 분석하고, 이 연구에서 지역 교육격차 해소의 대안으로 제시하고 있는 e-Learning과 관련하여 지역별로 보유하고 있는 인프라 구축 실태와 활용 현황에 대하여 살펴보고자 하였다. 이를 위하여 지역 대비 수도권으로 분류되는 서울 경기를 제외한 전체 12개의 지방자치단체에서 운영하고 있는 인적자원개발센터를 대상으로 연구를 실시하였으며 다음과 같은 결과를 도출하였다. 첫째, 지방자치단체에서는 교육의 양적, 질적 측면에서 지역 간 교육 격차를 크게 인식하고 있는 것으로 나타났으며 양적 측면보다 질적 측면에서 지역 격차가 상대적으로 큰 것으로 분석되었다. 둘째, 지역 간 교육 프로그램 개설 수, 교 강사의 전문성, 교육훈련 효과성 측면의 차이가 매우 크다고 인식하였다. 셋째, 집합교육이 e-Learning 보다 지역 간, 지역 내 교육격차가 더 크게 발생하고 있는 것으로 나타났다. 넷째, e-Learning의 주요 영역별 기반 구축 수준은 하드웨어가 상대적으로 높은 반면 나머지 영역은 미흡한 것으로 분석되었다.

• 동아시아식생활학회지
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• 제20권5호
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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.

• 한국콘텐츠학회논문지
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• 제12권6호
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• pp.518-529
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• 2012

유럽의 통합은 1991년 12월 마스트리히트에서 유럽연합조약을 처음 체결하면서 시작되었다. 근대 민족 국가의 요람이라고 할 수 있는 유럽은 현재 각국들의 사회경제적 통합은 물론 정치적 분야에서도 단일 유럽을 실현해 왔다. 이러한 가운데 유럽연합의 새로운 통합성장동력을 개발하기 위한 평생교육정책은 무엇보다도 사회경제적 환경 변화에 대비 할 수 있는 교육적 대응이 필요하다고 보고 있다. 특히 평생교육분야의 협력과 통합발전에 있어서는 다양성과 상호 조절을 통한 효율성의 조화를 이루어야 하는 중요한 과제를 안고 있다고 본다. 그러나 그들의 노력에도 불구하고 2008년 이후 유럽연합의 몇몇 국가들은 경제 침체로 인하여 국가재정의 위기를 맞이하였으며, 급기야 유럽전체의 재정위기로 까지 영향을 미치고 있다. 이와 같은 위기는 현재 유럽연합의 사회경제적 통합을 흔들고 있다. 본 연구에서는 유럽연합의 사회경제적 통합을 위한 평생교육 체제구축 및 정책 추진 현황을 살펴보고, 유럽연합에 속한 국가들 간의 성인평생교육 참여율이 1인당 국민소득과 어떠한 관련성이 있는지 분석하고자 하였다. 그리고 유럽연합의 경제위기 속에서 회원국 간의 사회경제적 통합이 지속 가능할 수 있을지를 통합적 평생교육체제의 문제점을 중심으로 알아보았다. 또한 이를 통하여 우리나라 평생교육의 새로운 패러다임 전환과 정책 수립 및 발전 방향 제시에 시사점을 도출하고자 하였다.

• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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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.

• 가족자원경영과 정책
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• 제24권1호
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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.

• 전자통신동향분석
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• 제38권3호
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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.