• Journal of East Asia Management
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• v.4 no.2
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• pp.39-62
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• 2023

Online and offline mixed teaching mode has become an important way to promote the connotative development of higher education. Under the background that offline teaching has become mature, in order to further promote the development of online education, and promote the implementation of the mixed teaching mode, to mix and to provide basis for the construction of the mixed teaching mode, this study takes the online learning effect as the evaluation basis, adopts the online questionnaire survey to conduct statistical analysis of the online learning behavior of 2213 college students, and discusses the differentiation phenomenon of online learning groups from the micro, meso and macro perspectives. It is found that there are significant differences in the online learning effect of college students in terms of the type of learning platform, whether the school implements the online offline mixed teaching mode, education background, grade (bachelor's degree), and region. Colleges and universities should strengthen the promotion of online and offline mixed teaching mode; The online learning platform should improve the platform function and strengthen the functional differentiation design of learning resources for students. Education departments pay attention to the learning effect of online learners in different regions, and bridge the gap in regional education.

• International Journal of Quality Innovation
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• v.8 no.2
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• pp.57-68
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• 2007

Benchmarking is more than just a comparison of measures about different company's performance in a wider sense. It is a methodology of learning-comparing-learning, at least within small and medium sized enterprises. This learning is not just limited to learn by copying successful concepts from other enterprises or competitors. It starts in learning more about the own company, about its structure and processes causing its own success or its failure. This kind of learning is necessary before the enterprise starts watching for a suitable Benchmarking partner. Learning from each other's strengths and weaknesses is the main goal of the European research project Quality beyond Borders! By using the Benchmarking methodology, small and medium sized enterprises get the opportunity to take part in a Benchmarking study and can learn more about the different strengths and weaknesses of other enterprises on both sides of the border. The results of such a Benchmarking can help to identify potentials for future cooperations among German and Polish enterprises in the same market or business. These potentials can lie in different ways of realising the same success or top-position. The Benchmarking study is not focused on an special business or region. That helps to find out trends for different kinds of top-positions, which can be claimed in all markets within a country. Every trend is characterised by different success factors which are responsible for the success in this top-position. In a first overview, the results of the Benchmarking study show 5 different groups of top-positions within a market which all have different profiles regarding to the importance of their success factors. By the end of the Benchmarking study it will be possible, to give answer about the special reasons for different kind of successes of these groups. These answers can be related to a special region within a country, a special business or of course related to possible differences in the expression of the group success factors in comparison of both countries.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.226-231
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• 2019

A WTCI is an important criteria for evaluating an mount of patient's tongue coating in tongue diagnosis. However, Previous WTCI tongue coating evaluation methods is a most of quantitatively measuring ration of the extracted tongue coating region and tongue body region, which has a non-objective measurement problem occurring by exposure conditions of tongue image or the recognition performance of tongue coating. Therefore, a WTCI based on deep learning is proposed for classifying an amount of tonger coating in this paper. This is applying the AI deep learning method using big data. to WTCI for evaluating an amount of tonger coating. In order to verify the effectiveness performance of the deep learning in tongue coating evaluating method, we classify the 3 types class(no coating, some coating, intense coating) of an amount of tongue coating by using CNN model. As a results by testing a building the tongue coating sample images for learning and verification of CNN model, proposed method is showed 96.7% with respect to the accuracy of classifying an amount of tongue coating.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.1
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• pp.80-90
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• 2021

Purpose: This study aimed to investigate nursing students' learning agility and confirm the mediating effect of self-leadership in the relationship between achievement motivation and learning agility. Methods: The study design was a descriptive survey design. The subjects were third- and fourth-year nursing students attending three universities in one region. Data were collected from November 28, 2019, to May 25, 2020, and a total of 202 data were collected using the scale of achievement motivation, self-leadership, and learning agility. Data analysis included frequency analysis, descriptive statistics, and Pearson's correlation coefficient using SPSS 25.0 statistics 25.0 software. The mediating effect of self-leadership was analyzed through regression analysis and bootstrapping using process macro ver. 3.4.1. Results: Self-leadership's partial mediating effect was confirmed in achievement motivation and learning agility. Achievement motivation was found to affect directly learning agility, with an indirect effect through self-leadership. Conclusion: The study results showed that nursing students could increase their learning agility through self-leadership improvement. Future research should focus on identifying the factors influencing nursing students' learning agility and develop and apply programs to improve learning agility.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.4
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• pp.90-104
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• 2023

This study analyzes the perceptions of college students majoring in aviation services according to the field learning program conducted during their major studies in order to reflect the educational value and academic awareness of the experience of the experiential field learning program. A survey is conducted targeting college students who experience a field learning program conducted by the Aviation Service Department of J University, a four-year university in the Chungcheong region. ANOVA (one-way analysis of variance) is conducted to analyze differences in perceptions of field learning properties, learning satisfaction, academic self-efficacy, and intention to continue studying. Additionally, text mining is conducted using 'Voyant Tools' to analyze students' field trip logs regarding field trip learning program activities. I hope that the results will be used as evidence to build an efficient and systematic learning strategy for operating field learning programs.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.273-277
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• 2002

This paper describes an optimal synthesis method of binary neural network(BNN) for an approximation problem of a circular region and synthetic image having four class using a newly proposed learning algorithm. Our object is to minimize the number of connections and neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm(NETLA) based on the multilayer BNN. The synthesis method in the NETLA is based on the extension principle of Expanded and Truncated Learning (ETL) learning algorithm using the multilayer perceptron and is based on Expanded Sum of Product (ESP) as one of the boolean expression techniques. The number of the required neurons in hidden layer can be reduced and fasted for learning pattern recognition.. The superiority of this NETLA to other algorithms was proved by simulation.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.105-109
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• 2001

This paper discuss the real-time self-tuning learning control based on evolutionary computation, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.9
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• pp.1463-1468
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• 2003

This paper presented the real-time self-tuning learning control based on evolutionary computation, which proves its superiority in finding of the optimal solution at the off-line learning method. The individuals of the populations are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations is proposed. It is possible to control the control object slightly varied as time changes. As the state value of the control object is generated, evolutionary strategy is applied each sampling time because the learning process of an estimation, selection, mutation is done in real-time. These algorithms can be applied; the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.