• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.225-230
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• 1992

In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.183-189
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• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.31-37
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• 2022

The purpose of this study was to identify the effects of family strength as a parameter on the relationship between grit, and learning satisfaction of university students. The grit scale, family strength scale and learning satisfaction scale were applied to data from surveys conducted on 194 students recruited from a university in G gun, C province. This study examined the mediating effects of family strength in relation to grit and learning satisfaction using the hierarchical regression analysis. Results showed that family strength had partial mediating effects in the between grit and learning satisfaction. Therefore, it seems that grit directly and indirectly affect learning satisfaction through family strength. This result indicates that the importance of family strength for learning satisfaction and suggest that family strength should be included in developing learning satisfaction improvement programs.

• Journal of the Korean Society of School Health
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• v.35 no.1
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• pp.11-21
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• 2022

Purpose: This study investigated the relationship between satisfaction with fundamental nursing skills practicum, confidence in fundamental nursing skills performance and learning flow, and examined factors influencing satisfaction with practicum programs of fundamental nursing skills in face-to-face and online classes for nursing students amid COVID-19. Methods: The subjects of the study were 229 junior nursing students from two colleges of nursing located in D and C city, respectively. The collected data were analyzed with descriptive statistics, independent t-test, ANOVA, Kruskal-Wallis test, Pearson's correlation and hierarchical multiple regression, using SPSS/WINdows 23.0. Results: The subjects' satisfaction with practicum showed a high positive correlation with confidence in performance (r=.55, p<.001) and learning flow (r=.70, p<.001) in face-to-face classes, and their satisfaction with practicum showed a high positive correlation with confidence in performance (r=.56, p<.001) and learning flow (r=.73, p<.001) in online classes. The factors affecting the subjects' satisfaction with practicum were learning flow (β=.51, p<.001) and confidence in performance (β=.30, p<.001) for face-to-face classes, and motivation for application (β=.14, p=.034), learning flow (β=.58 p<.001) and confidence in performance (β=.19, p=.015) for online classes. These factors explained 53% and 60% of the satisfaction with practicum in face-to-face classes (F=23.07, p<.001) and online classes (F=20.66, p<.001), respectively. Conclusion: Learning flow and confidence in performance should be considered when developing learning strategy programs to improve nursing students' satisfaction with fundamental nursing skills practicum in both face-to-face and online classes.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.785-794
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• 2002

Automatic stock trading systems should be able to solve various kinds of optimization problems such as market trend prediction, stock selection, and trading strategies, in a unified framework. But most of the previous trading systems based on supervised learning have a limit in the ultimate performance, because they are not mainly concerned in the integration of those subproblems. This paper proposes a stock trading system, called R-Trader, based on reinforcement teaming, regarding the process of stock price changes as Markov decision process (MDP). Reinforcement learning is suitable for Joint optimization of predictions and trading strategies. R-Trader adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing other trading parameters respectively. Technical analysis is also adopted to devise the input features of the system and value functions are approximated by feedforward neural networks. Experimental results on the Korea stock market show that the proposed system outperforms the market average and also a simple trading system trained by supervised learning both in profit and risk management.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.475-483
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• 2018

The purpose of this study based on descriptive research is to examine the convergent relationship of interpersonal competence, self-determination motivation and self-regulated learning ability in nursing students. Data collected from 251 nursing students at a university located in C province from June, 2018 to July, 2018 are analyzed using SPSS/WIN 21.0. As a result, there are positive correlation between interpersonal competence(r=.361, p<.001) and self-determination motivation(r=.466, p<.001) and self-regulated-learning-ability. Interpersonal competence and self-determination motivation have not meaningful relationship(r=.091, p=.148). It is turned out that interpersonal competence and self-determination motivation are influenced on the self-regulated learning ability respectively. Based on these results, research should be continued on the developing new educational methods through convergence of factors by analyzing relevant factors to enhance interpersonal competence and self-determination motivation that affect self-regulated learning ability.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.367-373
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• 2021

In this study, deep learning model was set up to predict the wave heights inside a harbour. Various machine learning techniques were applied to the model in consideration of the transformation characteristics of offshore waves while propagating into the harbour. Pohang New Port was selected for model application, which had a serious problem of unloading due to swell and has lots of available wave data. Wave height, wave period, and wave direction at offshore sites and wave heights inside the harbour were used for the model input and output, respectively, and then the model was trained using deep learning method. By considering the correlation between the time series wave data of offshore and inside the harbour, the data set was separated into prevailing wave directions as a pre-processing method. As a result, It was confirmed that accuracy and stability of the model prediction are considerably increased.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.14 no.4
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• pp.412-419
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• 2007

Purpose: This study was done to analyze core competencies affecting critical thinking ability of student nurses. Core competencies investigated in this study were problem solving ability, communication ability and self-directed learning ability. Method: Data were collected from a convenience sample of 322 student nurses in 2 provinces during the period from May 21 to June 8, 2007. Critical thinking, problem solving ability, communication ability and self-directed learning ability were measured using the Disposition towards Critical Thinking Scale by Park(1999-a) and the Core Competencies Scale by Lee(2003). Descriptive statistics and correlation coefficients with the SPSS WIN 12.0 program were used to analyze the data. Results: There were significant differences in the critical thinking according to grade, type of high school, experience with PBL, preference for lecture-based learning, preference for discussion and perceived logicality. The score for critical thinking showed significantly positive correlations with scores for problem solving ability(r=0.54, p=0.00), communication ability(r=0.56, p=0.00) and self-directed learning ability(r=0.54, p=0.00). Conclusion: The results of this study suggest that problem solving ability, communication ability and self-directed learning ability are significant factors affecting critical thinking in student nurses.

• The Journal of Korean Academic Society of Nursing Education
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• v.26 no.2
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• pp.185-197
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• 2020

Purpose: The purpose of this study was to investigate the mediating effect of self-regulated learning ability on the relationship between experiencing a good class and problem solving ability in nursing college students. Methods: A structured self-report questionnaire was used to measure experiencing a good class, self-regulated learning ability, and problem solving ability. During June, 2019, data were collected from 130 nursing students in D city. Data were analyzed using t-test, One-way ANOVA, Pearson's correlation coefficients, and hierarchical multiple linear regression with SPSS/WIN 23.0. Results: Importance of good class (r=.50, p<.001), satisfaction of good class (r=.42, p<.001), and self-regulated learning ability (r=.71, p<.001) were positively correlated with the problem solving ability of participants. Also, self-regulated learning ability had a partial mediating effect on the relationship between experiencing a good class and problem solving ability. Conclusion: Considering the findings of this study, developing programs that can improve the self-regulated learning ability of nursing students who experience a good class are needed to increase their level of problem solving ability.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.4
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• pp.359-368
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• 2021

Purpose: The purpose of this study was to investigate the mediating effect of self-leadership on the relationship between media literacy and learning agility in nursing students based on their experiences in online classes during the Coronavirus Disease-19 pandemic. Methods: A descriptive survey was conducted among 165 nursing students from four universities in Busan. Data were collected from June 2 to 13, 2021, and was analyzed using a t-test, one-way ANOVA, Pearson's correlation coefficients, and stepwise multiple regression with SPSS/WIN 26.0. Results: Significant relationships were found between learning agility and media literacy (r=.62, p<.001), between learning agility and self-leadership (r=.58, p<.001), and between media literacy and self-leadership (r=.53, p<.001). Additionally, self-leadership had a partial mediating effect on the relationship between media literacy and learning agility (Z=4.30, p<.001); its explanatory power was 46.0%. Conclusion: These results indicate that interventions to increase the level of media literacy, along with self-leadership, are necessary to improve the level of learning agility of nursing students who will be essential human resources in a rapidly changing healthcare field.