• Journal of Digital Convergence
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• v.16 no.6
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• pp.241-250
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• 2018

This study was conducted to examine the correlations between recognition of the importance, self-directed learning ability(SDLA), and confidence in performance regarding basic nursing skills, and to identify the influencing factors on confidence in performing the basic nursing skills in nursing students. Data were collected from 171 nursing students from December 18 to 22, 2017. Data were analyzed using Pearson's correlation coefficients, and stepwise multiple regression. Confidence in performance of basic nursing skills showed significant positive correlations with SDLA and recognition of importance. SDLA, practice attitude, and recognition of importance, and grade in last semester accounted for 32% of the variance, were significant predictors influencing confidence in performance of basic nursing skills. Thus, various educational strategies are necessary to improve SDLA, practice attitude and recognition of importance in order to increase the confidence of nursing students in performance of basic nursing.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.75-84
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• 2017

The purpose of this study is to identify the relationships among Social maturity(SM), Self-directed learning(SDL) and Self-concept of professional nursing(SCPN), and influencing factors of SCPN of nursing students. The survey was performed on 221 nursing students in two universities. Data were collected using a structured questionnaires and analysed with PASW 22.0. Study findings revealed that SCPN has a significant correlation with SM and SDL. Major satisfaction(${\beta}=.179$), Meaning on nursing(${\beta}=.274$), SM(${\beta}=.118$) and SDL(${\beta}=.211$) about SCPN were significant predictive variables. This variables accounted for 34.4% of the variance in SCPN. The findings indicate the necessity of developing educational programs to enhance nursing students's SM and SDL for increasing positive SCPN.

• Journal of the Korean Society of Costume
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• v.45
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• pp.133-146
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• 1999

Because adolescents are much inflicted with the sense of obsession in endless competition with their feers the costume of the pop singers relieving this agony and anxiety is becoming the object of immitation more than that of any oother entertainer. In this perspective it was thought that it was very necessary to attempt to investigate what relationship sense of mental anxiety the costume singers recently have worn had with adolescents sense of anxiety. The anxiety questionnaire used by Chu Young-sook and Kim Jung-hui and the questionnaire drawn up as the result- of the pilot-test and the pre-test were used and the questionnaires drawn up by a total of 228 middle schoolgirls in seoul were used a sfinal data of annalysis. Of them the anxiety questionnaire was made up of 8 sub-scales such as classic·social·morbid·learning examination·school record·poverty·war and other to anxieties. in order to survey the overall content concerning the costume of the singers preferred by teenagers the other questionnaire presented three vaiables such as musicality fashionability and dance as the distinct characteristics of singers and hiphop style tidy style and sexy style of clothing as the style of clothing preferred of singers' clothing. The importance fashionability and imitability of clothing were investigated as variables in relation to singers' clothing behavior. The SPSS PC+ program was used as the analytic method of data which were tested by the frequency analysis Duncan's multiple anaylsis of variance t-test and so on. As a result of investigation middle schoolgirls having a high level of learning and examinatior anxieties preferred the singers excellent in musicality while middle schoolgirls having a lower level of classic anxiety preferred the singers excellent in fashionability and dance wearing the hiphop style of clothing. And it was shown that middle schoolgirls having a lower level of classic anxiety preferred the singers wearing the clothing of sexy style. In the light of these results it was shown that there was the correlation between anxiety and clothing preference. Accordingly it is thought that the purpose of this study was achieved to some exent.

• Toxicological Research
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• v.24 no.3
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• pp.183-188
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• 2008

The behavioral phenotypes of out-bred ICR mice were compared with those of in-bred C57BL/6 and BALB/c mice. In particular, this study examined the locomotor activity and two forms of hippocampus-dependent learning paradigms, passive avoidance and object recognition memory. The basal open-field activity of the ICR strain was greater than that of the C57BL/6 and BALB/c strains. In the passive avoidance task, all the mice showed a significant increase in the cross-over latency when tested 24 hours after training. The strength of memory retention in the ICR mice was relatively weak and measurable, as indicated by the shorter cross-over latency than the C57BL/6 and BALB/c mice. In the object recognition memory test, all strains had a significant preference for the novel object during testing. The index for the preference of a novel object was lower for the ICR and BALB/c mice. Nevertheless, the variance and the standard deviation in these strains were comparable. Overall, these results confirm the strain differences on locomotor activity and hippocampus-dependent learning and memory in mice.

• KIISE Transactions on Computing Practices
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• v.20 no.10
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• pp.549-554
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• 2014

Handwritten character recognition data is usually imbalanced because it is collected from the natural language sentences written by different writers. The imbalanced data can cause seriously negative effect on the performance of most of machine learning algorithms. But this problem is typically ignored in handwritten character recognition, because it is considered that most of difficulties in handwritten character recognition is caused by the high variance in data set and similar shapes between characters. We propose the oversampling-based ensemble learning methods to solve imbalanced data problem in handwritten character recognition and to improve the recognition accuracy. Also we show that proposed method achieved improvements in recognition accuracy of minor classes as well as overall recognition accuracy empirically.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.457-464
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• 2009

The weak classifier of AdaBoost algorithm is a central classification element that uses a single criterion separating positive and negative learning candidates. Finding the best criterion to separate two feature distributions influences learning capacity of the algorithm. A common way to classify the distributions is to use the mean value of the features. However, positive and negative distributions of Haar-like feature as an image descriptor are hard to classify by a single threshold. The poor classification ability of the single threshold also increases the number of boosting operations, and finally results in a poor classifier. This paper proposes a weak classifier that uses multiple criterions by adding a probabilistic criterion of the positive candidate distribution with the conventional mean classifier: the positive distribution has low variation and the values are closer to the mean while the negative distribution has large variation and values are widely spread. The difference in the variance for the positive and negative distributions is used as an additional criterion. In the learning procedure, we use a new classifier that provides a better classifier between them by selective switching between the mean and standard deviation. We call this new type of combined classifier the "Mixed Weak Classifier". The proposed weak classifier is more robust than the mean classifier alone and decreases the number of boosting operations to be converged.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.251-262
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• 2020

The study was conducted to investigate factors influencing satisfaction in major on nursing students. A convenience sample 159 nursing student was selected from G do and C do, between 17 June and 24 June 2019. Data were analyzed t-test, one-way ANOVA, Pearson's correlation, and multiple regression analysis. Factors that influenced satisfaction in major included gender, age, religion, perceived academic achievement, acquisition of fundamental nursing skills(FNS), perceived health state. Satisfaction in major showed a positive correlation with learning self-efficacy(LSE), confidence in performance of FNS and satisfaction with practicum. These variables revealed regression analysis that significant factor and explained 63.0% of the variance. It is necessary to develop and test programs to ensure an improvement in LSE, confidence in performance of FNS and satisfaction with practicum among nursing students to increase their satisfaction in major.

• Journal of Korean Clinical Health Science
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• v.8 no.2
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• pp.1398-1407
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• 2020

Purpose: The purpose of this study is a technical study to understand the relationship between the recognition and performance of core basic nursing skills of nursing college students who are ahead of clinical practice, and satisfaction with their major. Method: The subjects were 208 second-year students enrolled in the four-year nursing department located in J City and C City. General characteristics, characteristics of clinical practice, and recognition of the importance of core basic nursing skills, performance, and satisfaction with majors were investigated. Descriptive statistics, t-test, analysis of variance, multiple regression analysis and IPA are performed for data analysis Results: The results are follows. The results are follows. First, the performance was lower than that of the core basic nursing skills (p<.001). As a result of comparing the importance recognition and performance of the core basic nursing items, the importance recognition was significantly compared to the performance level in all 20 items. It showed high results. Second, it was found that there was a significant positive correlation (r=.40, p<.01) with major satisfaction in core basic nursing performance. Conclusion: These results highlight the need to develop education. It is necessary to establish a learning strategy through various learning guidance methods and self-directed learning that can improve the performance of items with low performance, although recognized as important through the Core Basic Nursing IPA for nursing students who are about to practice clinical practice. It is suggested to do repeated research applied.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.437-444
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• 2022

Recently, research on mobile edge services has been conducted to handle computationally intensive and latency-sensitive tasks occurring in wireless networks. However, MEC, which is fixed on the ground, cannot flexibly cope with situations where task processing requests increase sharply, such as commuting time. To solve this problem, a technology that provides edge services using UAVs (Unmanned Aerial Vehicles) has emerged. Unlike ground MEC servers, UAVs have limited battery capacity, so it is necessary to optimize energy efficiency through load balancing between UAV MEC servers. Therefore, in this paper, we propose a load balancing technique with consideration of the energy state of UAVs and the mobility of vehicles. The proposed technique is composed of task offloading scheme using genetic algorithm and task migration scheme using Q-learning. To evaluate the performance of the proposed technique, experiments were conducted with varying mobility speed and number of vehicles, and performance was analyzed in terms of load variance, energy consumption, communication overhead, and delay constraint satisfaction rate.