• Journal of Convergence for Information Technology
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• v.8 no.4
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• pp.247-253
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• 2018

This study was designed to analyze the difference in attendance and academic achievement based on spatio-temporal data relate to e-Learning patterns of university e-Learning learners. This study collected e-Learning data from 68 e-Learning classes, 13,611 learners during 3 years. Collected data were analyzed by t-test and two-way ANOVA. Major study findings were as follows. Firstly, e-Learning learners in school received higher than those of learners outside school both in attendance and academic achievement, while that academic achievement showed statistical significance. Secondly, the attendance and academic achievement by the day was in the order of e-Learning learners mainly in the morning, those in the afternoon and those at night, in addition there was statistical significance. Lastly e-Learning learners in the weekdays appeared higher than those of learners in the weekends both in attendance and academic achievement, also both of them showed statistical significance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.557-562
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• 2020

In the non-face-to-face learning environment forced into effect by the COVID-19 pandemic, online learning is being adopted as a major educational technique. Given the lack of research on how online learning patterns affect academic performance, this study focuses on the number and duration of online video learning sessions as a major factor based on midterm and final exams, and with a formative assessment for each type of learning. The correlation of the learning effects was analyzed. The analysis focused on computer programming subjects, which are among the most difficult liberal arts subjects for arts and science students at the university level. The analysis of cases of actual students showed no correlation among weekly formative assessments, the number of learning sessions, and the learning duration. On the other hand, the number of learning sessions (r=.39 p<0.05) and learning duration (r=.42 p<0.05) were correlated with the midterm and final exams. Elements, such as SMS text, bulletin board, and e-mail, were excluded from the analysis because not all students have access to them. Therefore, the results can be improved if future analysis of the students' learning patterns in a non-face-to-face lecture environment is performed considering more factors/elements and the learners' needs.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.6-42
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• 2002

Kohonen's self organizing feature map (SOFM) converts arbitrary dimensional patterns into one or two dimensional arrays of nodes. Among the many competitive learning algorithms, SOFM proposed by Kohonen is considered to be powerful in the sense that it not only clusters the input pattern adaptively but also organize the output node topologically. SOFM is usually used for a preprocessor or cluster. It can perform dimensional reduction of input patterns and obtain a topology-preserving map that preserves neighborhood relations of the input patterns. The traditional SOFM algorithm[1] is a competitive learning neural network that maps inputs to discrete points that are called nodes on a lattice...

• Proceedings of the Korea Contents Association Conference
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• 2004.05a
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• pp.56-62
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• 2004

In the case that we do not have enough number of training patterns because of limitation such as time consuming, economic problem, and so on, we geneterate a new patterns using the brain-style Information processing algorithm, that is, supervised and unsupervised learning methods.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.7
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• pp.718-724
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• 1991

In this paper, we proposed an algorithm to improve the memory efficiency by means of learning thresholds in spite of correlations among input patterns to be memorized. The proposed algorithm does not need preprocess correlations among input patterns but processes them with a threshold on a neural network. When memory contents are destroyed by correlation, nearly all patterns can be properly recovered with past learning. Through experiments we show how out algorithm can improve the memory efficiency.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.195-200
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• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.459-461
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• 1992

This paper presents the new method to recognize the 2D patterns dynamically by rotating the input patterns according to the difference vector. Generally neural network with many patterns leads to various recognition ratio. The dynamic management of input patterns means that we can move pixels to desired locations controlled by the difference vector. We divide dual neural network model into two parts at learning phase, respectively. And then we combine them to construct the total network. Our model has some good results such that it has less number of patterns and reduced learning time. At present, we only discuss the four way movement of input patterns. The research for the complex movement will be fulfilled later.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.159-167
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• 1996

In this paper, a new competitive learning algorithm called dynamic competitive learning (DCL) is presented. DCL is a supervised learning mehtod that dynamically generates output neuraons and nitializes weight vectors from training patterns. It introduces a new parameter called LOG (limit of garde) to decide whether or not an output neuron is created. In other words, if there exist some neurons in the province of LOG that classify the input vector correctly, then DCL adjusts the weight vector for the neuraon which has the minimum grade. Otherwise, it produces a new output neuron using the given input vector. It is largely learning is not limited only to the winner and the output neurons are dynamically generated int he trining process. In addition, the proposed algorithm has a small number of parameters. Which are easy to be determined and applied to the real problems. Experimental results for patterns recognition of remote sensing data and handwritten numeral data indicate the superiority of dCL in comparison to the conventional competitive learning methods.

• Journal of Korean Biological Nursing Science
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• v.21 no.3
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• pp.231-238
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• 2019

Purpose: In 2015, South Korea had the highest global smartphone penetration (88%). However, smartphone addiction can seriously disrupt daily life and have a major negative impact on academic achievement. Methods: A structured questionnaire was completed by 250 nursing students for this descriptive study. Results: Students who were older, more satisfied with their major, exercised, and used their smartphone for less than 30 minutes before sleeping had higher learning engagement than those who were younger, less satisfied, did not exercise and used their smartphone for more than three hours. Quality of sleep and smartphone addiction were negatively correlated as was quality of sleep and daytime sleepiness. Interestingly, sleep pattern did not impact learning engagement directly. Conclusion: Smartphone usage influences learning engagement of nursing students rather than their sleeping patterns, which suggests a need to develop self-disciplining strategies for smartphone use to enhance learning engagement.

• Journal of Korean Society for Quality Management
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• v.52 no.1
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• pp.77-93
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• 2024

Purpose: The purpose of this study is to examine machine learning use cases in manufacturing companies from a digital quality management (DQM) perspective and to analyze and present machine learning research patterns from a quality management perspective. Methods: This study was conducted based on systematic literature review methodology. A comprehensive and systematic review was conducted on manufacturing papers covering the overall quality management process from 2015 to 2022. A total of 3 research questions were established according to the goal of the study, and a total of 5 literature selection criteria were set, based on which approximately 110 research papers were selected. Based on the selected papers, machine learning research patterns according to quality management were analyzed. Results: The results of this study are as follows. Among quality management activities, it can be seen that research on the use of machine learning technology is being most actively conducted in relation to quality defect analysis. It suggests that research on the use of NN-based algorithms is taking place most actively compared to other machine learning methods across quality management activities. Lastly, this study suggests that the unique characteristics of each machine learning algorithm should be considered for efficient and effective quality management in the manufacturing industry. Conclusion: This study is significant in that it presents machine learning research trends from an industrial perspective from a digital quality management perspective and lays the foundation for presenting optimal machine learning algorithms in future quality management activities.