• The Journal of Society for e-Business Studies
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• v.25 no.2
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• pp.15-27
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• 2020

This study starts with the idea that the process of creating a Gantt Chart for schedule planning is similar to Tetris game with only a straight line. In Tetris games, the X axis is M machines and the Y axis is time. It is assumed that all types of orders can be worked without separation in all machines, but if the types of orders are different, setup cost will be incurred without delay. In this study, the game described above was named Gantris and the game environment was implemented. The AI-scheduling table through in-depth reinforcement learning compares the real-time scheduling table with the human-made game schedule. In the comparative study, the learning environment was studied in single order list learning environment and random order list learning environment. The two systems to be compared in this study are four machines (Machine)-two types of system (4M2T) and ten machines-six types of system (10M6T). As a performance indicator of the generated schedule, a weighted sum of setup cost, makespan and idle time in processing 100 orders were scheduled. As a result of the comparative study, in 4M2T system, regardless of the learning environment, the learned system generated schedule plan with better performance index than the experimenter. In the case of 10M6T system, the AI system generated a schedule of better performance indicators than the experimenter in a single learning environment, but showed a bad performance index than the experimenter in random learning environment. However, in comparing the number of job changes, the learning system showed better results than those of the 4M2T and 10M6T, showing excellent scheduling performance.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.475-481
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• 2023

Softwares, especially like machine learning applications, affect human's life style tremendously. Accordingly, the importance of the cost model for softwares increases rapidly. As cost models, LOC(Line of Code) and M/M(Man-Month) estimates the quantitative aspects of the software. Differently from them, FP(Function Point) focuses on estimating the functional characteristics of software. FP is efficient in the aspect that it estimates qualitative characteristics. FP, however, has a limit for evaluating machine learning softwares because FP does not evaluate the critical factors of machine learning software. In this paper, we propose an extended function point(ExFP) that extends FP to adopt hyper parameter and the complexity of its optimization as the characteristics of the machine learning applications. In the evaluation reflecting the characteristics of machine learning applications. we reveals the effectiveness of the proposed ExFP.

• Journal of The Korean Society of Integrative Medicine
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• v.4 no.1
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• pp.65-72
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• 2016

Purpose : This study was carried out to find out the relationship between emotion intelligence, learning flow and career stress of nursing students and influence factors for career stress. Methods : This study targeted 197 university students in their freshman-senior year attending College of Nursing located in P Metropolitan City. For collected data, real numbers and percentage, mean and standard deviation and multiple regression analysis were carried out by using PASW 21.0 program and the correlation between emotion intelligence, learning flow and career stress was analyzed with Pearson's correlation coefficients. Results : Emotional self-awareness(M=3.80, SD =.71), clear goals(M=3.39, SD=.90) and school environment stress(M=2.97, SD=.96) were found to be high in the degree of emotion intelligence, learning flow and career stress of the subjects. The relationship between emotion intelligence and learning flow showed a positive correlation(r=.489, p<.01) in the correlation between emotion intelligence, learning flow, career stress and emotion intelligence showed a negative correlation with career stress(r=-.204, p<.01). Emotion intelligence and learning flow show that career stress is predicted significantly (${\beta}$ =-.15, p < .01) and explained a career stress variate as 18%(F = 24.5, p < .01). Conclusion : Emotion intelligence of nursing students was found to be very influential on the degree of learning flow or career stress. Based on the results of this study, replication studies on emotion intelligence and career stress are needed and the development of intervention programs to increase emotion intelligence is needed.

• Journal of The Korean Association of Information Education
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• v.16 no.3
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• pp.309-318
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• 2012

Smart learning is a new trend in education following E-learning, U-Learning, and M-Learning. In June 2011, the Korean government announced the education policy on promoting smart learning, and presented the vision and the direction for the smart learning. However, it seems that the current government-directed education policy on smart learning has promoted the unconditional implementation of the policy without taking into consideration of a variety of factors, such as the reality of the classroom, educational environment, educators' competencies to use smart learning, and so on. The aims of this study are to examine the reality of the classroom and the educational environments for smart learning, and to take a survey on the elementary teachers' use of the smart learning. In addition, the study attempted to investigate the teachers' understanding of the various factors regarding the use of smart learning. On the basis of the results of the survey, the problems of implementing smart learning in the classroom were analyzed, and then some suggestions were made to pave the way for the more improved and systematic smart learning.

• Educational Technology International
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• v.9 no.1
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• pp.1-23
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• 2008

Despite considerable commitment to the application of action learning as an organization development intervention, no identified systematic investigation of action learning practices has been reported. Based on a systematic literature review, the purpose of this paper is to identify whether researchers strike a balance between action and learning in their studies of action learning. Research findings in this study included: (1) only 32 empirical studies were found from the electronic database search; (2) based on the hypothesized continuum of Revans' original proposition of balancing action and learning, the author categorized 32 studies into three groups: action-oriented, learning-oriented, and balanced action learning; (3) there were only nine studies on balanced action learning among 32 empirical studies, whose insights included an effective use of project teams, applications of action learning for organization development, and key success factors such as time, reflection, and management support; (4) case study was among the most frequently used research method and only six quality studies met key methodological traits; and (5) therefore, more rigorous empirical research employing quantitative methods as well as case studies is needed to determine whether researchers strike a balance between action and learning in studies on action learning.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.339-339
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• 2006

• Knowledge Management Research
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• v.20 no.4
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• pp.149-168
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• 2019

In the era of the Fourth Industrial Revolution, digital transformation is emerging in the education and learning fields. As the use of the mobile Internet and mobile devices has become a daily life, mobile learning that supports a variety of learning in a mobile environment is drawing attention. Mobile learning applications (apps) are expected to expand their use by providing a convenient learning environment anytime, anywhere. This study investigates the use of mobile learning apps in English education, which is one of the most popular learning areas, and empirically examines the factors that influence the continuous use of mobile learning apps. In particular, it analyzes the differences between Chinese and Korean users. The results of this study provide theoretical and practical implications to promote the development of mobile apps suitable for mobile learning environments and the sustainable user growth in mobile learning.

• The Mathematical Education
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• v.31 no.2
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• pp.93-107
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• 1992

The purpose of this study is to make out teaching-learning method for developing mathematical abilities of the 1st grade children in elementary school by investigating cognitive effects which mathematical pre-experiences given intentionally by teachers have on children's learning mathematics. The research questions for this purpose are as follows: In learning effects through mathematical pre-experiences given intentionally by teachers. 1) is there any differences between children with pre-experiences and children without them in Mathematics Achievement Test\ulcorner 2) is there any differences between children with pre-experiences and children without them in Transfer Test for learning effects\ulcorner For this study, a class with 41 children in H elementary school located in a Myon near Chong-ju was selected as an experimental group and a class with 43 children in G elementary school in the same Myon was selected as a control group. Nonequivalent Control Group Design of Quasi-Experimental Design was applied to this study. To give pre-experiences to the children in experimental group, their classroom was equipped with materials for pre-experiences, so children could always observe the materials and play with them. The materials were a round-clock on the wall, two pairs of scales, fifty dice, some small pebbles, two pairs of weight scales, two rulers on the wall, and various cards for playing games. Pre-experiences were given to the children repeatedly through games and observations during free time in the morning (00:20-09:00) and intervals between periods. There was a pretest for homogeneity of mathematics achievement between the two groups and were Mathematics Achievement Test (30 items) and Transfer Test (25 items) for learning effects as post-tests. The data were collected from the pretest on April 8 (control group), on April 11 (experimental group) and from the Mathematics Achievement Test and Transfer Test on July 15 (experimental group) and on July 16 (control group). T-test was used to analyze if there were any differences in the results of the test. The results of the analysis were as follows: (1) As the result of pretest, there was not a significance difference between the experimental group (M=17.10. SD=7.465) and the control group (M=16.31, SD=6.974) at p<.05 (p=0.632). (2) For the question 1. in the Mathematics Achievement Test, there was a significant difference between the experimental group (M=26.08, SD=4.827) and the control group (M=22.28. SD=5.913) at p<.01 (p=.003). (3) For the question 2. in the Transfer Test for learning effects. there was a significant difference between the experimental group (M=16.41, SD=5.800) and the control group (M=11.84, SD=4.815) at p<001, (p=.000). From the results of the analyses obtained in this study. the following conclusions can be drawn: First, mathematical pre-experiences given by teachers are effective in increasing mathematical achievement and transfer in learning mathematics. Second, games. observations, and experiments given intentionally by teachers can make children's mathematical experiences rich and various, and are effective in adjusting individual differences for the mathematical experiences obtained before they entered elementary schools. Third, it is necessary for teachers to give mathematical pre-experiences with close attention in order to stimulate children's mathematical interests and intellectual curiosity.

• ETRI Journal
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• v.44 no.5
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• pp.837-848
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• 2022

Analog circuit design is comparatively more complex than its digital counterpart due to its nonlinearity and low level of abstraction. This study proposes a novel low-level hybrid of the sine-cosine algorithm (SCA) and modified grey-wolf optimization (mGWO) algorithm for machine learning-based design automation of CMOS analog circuits using an all-CMOS voltage reference circuit in 40-nm standard process. The optimization algorithm's efficiency is further tested using classical functions, showing that it outperforms other competing algorithms. The objective of the optimization is to minimize the variation and power usage, while satisfying all the design limitations. Through the interchange of scripts for information exchange between two environments, the SCA-mGWO algorithm is implemented and simultaneously simulated. The results show the robustness of analog circuit design generated using the SCA-mGWO algorithm, over various corners, resulting in a percentage variation of 0.85%. Monte Carlo analysis is also performed on the presented analog circuit for output voltage and percentage variation resulting in significantly low mean and standard deviation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.517-523
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• 2019

PM₁₀ concentration is a spatiotemporal phenomenta and capturing data for such continuous phenomena is a difficult task. This study designed a model that enhances spatiotemporal resolution of PM₁₀ concentration levels using satellite imagery, atmospheric and meteorological sensor data, and multiple deep learning models. The designed deep learning model was trained using input data whose factors may affect concentration of PM₁₀ such as meteorological conditions and land-use. Using this model, PM₁₀ images having 15 minute temporal resolution and 30m×30m spatial resolution were produced with only atmospheric and meteorological data.