• Journal of Korean Academy of Nursing
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• v.24 no.4
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• pp.678-695
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• 1994

The purpose of this study was to investigate knowledge, attitudes and practices of women toward breast self-examination and to identify factors that may influence compliance with breast examination. The subjects for this study were 282 women in three hospitals located in In-Chun. Data were collected during the period from October 15 to 30, 1993 by means of a structured questionnaire. The data were analyzed using the SAS program and include descriptive statistics, 1-test, ANOVA, Pearson correlation coefficient and stepwise multiple regression. The results of study are as follows : 1. The mean knowledge score for the total sample was 13.58. Factors affecting the women's knowledge of breast cancer and BSE were : age, level of education, experience with breast cancer patients, experience in learning BSE, information about BSE, self-practice of BSE, level of intention to perform BSE, and participation in a BSE class. 2. Elements related to attitude included : (a) perceived feeling of susceptibility to breast cancer, and (b) belief about the effectiveness of BSE. The mean perceived susceptibility score was 1.62 and the mean effectiveness score was 4.22. Factors affecting the women's perceived susceptibility to breast cancer were exercise for health, level of intention to perform BSE , intention to recommend to others and self-practice of BSE. The relation between the womens' belief about effectiveness of BSE and level of intention to perform BSE and intention to recommend to others were statistically significant. 3. The mean self-practice score for the total sample was 4.01. Factors affecting the women's practice were experience with breast cancer patients, information about BSE, experience in learning BSE, enlisting the help of significant peers, and level of intention to perform BSE. Results indicated 35.8% of the total sample practiced BSE. The most frequent reason women gave for not performing BSE was “Didn’t knew about BSE technique”, “Didn’t think do it”. 4. No relation was found between knowledge and attitudes and practices. 5. When all the variables were examined for their contribution to the variance in the practice of BSE, it was found that confidence in ability to detect a mass by BSE, knowledge about breast cancer and BSE, and experience with breast cancer patients were significant variables and explained 35.8% of the variance. From the results of this study it can be said that women need to be taught proper BSE technique so they can become more proficient in detecting breast abnormalities.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.884-889
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• 2010

Recently, a music genre classification has been studied. However, experts use different criteria to classify each of these classifications is difficult to derive accurate results. In addition, when the emergence of a new genre of music genre is a newly re-defined. Music as a genre rather than to separate search should be classified as emotional words. In this paper, the feelings of people on the basis of brightness and darkness tries to categorize music. The proposed classification system by applying VCM(Variance Considered Machines) is the contrast of the music. In this paper, we are using three kinds of musical characteristics. Based on surveys made throughout the learning, based on musical attributes(beat, timbre, note) was used to study in the VCM. VCM is classified by the trained compared with the results of the survey were analyzed. Note extraction using the MATLAB, sampled at regular intervals to share music via the FFT frequency analysis by the sector average is defined as representing the element extracted note by quantifying the height of the entire distribution was identified. Cumulative frequency distribution in the entire frequency rage, using the difference in Timbre and were quantified. VCM applied to these three characteristics with the experimental results by comparing the survey results to see the contrast of the music with a probability of 95.4% confirmed that the two separate.

• Journal of Educational Research in Mathematics
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• v.8 no.1
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• pp.11-29
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• 1998

The purpose of this thesis was to improve the plans and methods of teaching and learning activities and contribute to developing teachers` quality and reeducating them by investigating teachers' attitudes toward teaching and learning have a significant effect on the students' attitudes toward mathematics and the students' ability development at mathematics. The inventory was composed of 56 items : three main areas and eight sub-areas. Added seven background factors were sex, by whom was established (is it a public or nongovernmental \ulcorner), teaching career, age, what kind of school (is it general or vacational high school or middle school\ulcorner), region, college. For this analysis of materials used SAS program. And analysis of variance was applied on the seven background factors. All subjects in this study were 341 secondary school mathematics teachers in pusan city and Kyungsangnam-do were surveyed by the questionnaire of Likert type to which the respondents' seven background elements were added. Main results this study were as follows : 1. The overall attitude of the measured secondary school mathematics teachers tends to be positive but a little indifferent. Also attitude toward the students was a little more positive than the other attitudes. 2. There were significant differences (1%) among the sub-level areas except three of them. (r = 0.17~0.60) 3. There were significant differences (5%) by the result of Multiple comparison test among the schools in learning and teaching. So the teachers working at middle schools and general high schools were more positive than those working at vocational high schools. 4. The result of comparison among region was that teachers working in towns and cities were more positive than those working in the country. But there was no significant difference between the teachers working in large cities and those working in other region. 5. There was no significant difference in the overall attitudes toward teaching and learnig among the sex, by whom was established(is it public or nongovernmental\ulcorner), teaching career, age, college. The study left much deficiency to be desired and has to be followed by a continuing study to make it better. For the following study, it is necessary to examine the validity and reliability of the measuring tools more thoroughly and investigate the attitudes with sufficient samples all over the country.

• The Journal of the Korea Contents Association
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• v.17 no.4
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• pp.468-477
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• 2017

The purpose of this study was to exam nursing students to verify the effects of self-directed learning readiness, teacher-student interaction, educational performance, stress and learner motivation on learner satisfaction. The study population consisted of second and third year nursing students at E university located in S city. Data were collected between June 15, 2016 to June 24, 2016, and questionnaire comprised items to measure general characteristics, learner motivation, teacher-student interaction, self-directed learning readiness, educational performance, and learner satisfaction. A total of 132 students were included for the final analysis. Learner satisfaction was positively correlated with self-directed learning readiness (r= .21, p= .018), teacher-student interaction (r= .39, p<.001), educational performance (r= .21, p= .014), and learner motivation (r= .75, p<.001). In addition, learner motivation was positively correlated with self-directed learning readiness (r= .24, p= .005), teacher-student interaction (r= .38, p <.001), and educational performance (r= .21, p= .018). Finally, learner motivation and teacher-student interaction were found to explain 59.7% of the variance of learner satisfaction. Our findings suggest strategies and interventions that boost learner motivation and teacher-student interaction which are required to improve learner satisfaction in nursing education.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.433-441
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• 2020

This study was conducted to identify the factors that influence the other behaviors taken by nursing students during online lectures. The study subjects were 304 nursing students in three universities. Data were collected between April 20 and 30, 2020, using by completing structured self report questionnaires. Data were analyzed using T-test, ANOVA, Pearson's correlation coefficient, and multiple regression using SPSS 26.0 program. In correlation analysis, significant negative correlations were found between other behaviors, interest(r=-17, p<.01), understanding(r=-19, p<.01), needs(r=-12, p<.05), learning motivation(r=-12, p<.05), self-regulation efficacy(r=-11, p<.05), learning confidence(r=-14, p<.05), lecture satisfaction(r=-22, p<.01), lecture flow(r=-24, p<.01). In the multiple regression analysis, learning confidence, prefer to discuss & present (β=.19), lecture flow(β=-.15), lecture satisfaction(β=-.15) were statistically significant factors that explained 10.6% of variance of other behaviors taken by nursing students during online lectures. Thus, we suggest to develop that teaching methods and programs to reduce other behaviors taken by nursing students during online lectures.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.405-413
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• 2021

The purpose of this study was to investigate empathic ability, Nunchi, learning flow and clinical competency, and to identify the influencing factors on clinical competency of nursing students. Method: There were 179 nursing students as participants in K city, who were surveyed between May 10 and May 14, 2021, using a self-report questionnaire. Data were analyzed by frequencies, t-test, ANOVA, Pearson's correlation, multiple regression using SPSS Win 21.0. Result: There was a positive correlation between empathic ability and Nunchi(r=.148, p=.048) and between learning flow and clinical competency(r=.605, p<.001). In the multiple regression, Academic achievement(𝛽=.129, p=.049), Age(𝛽=-.116, p=.014) and satisfaction of clinical practice(𝛽=-.242, p<.001) were associated with clinical competency. These factors accounted for 38.4% of the total variance in clinical competency. Therefore, to improve clinical competency of online nursing practice of nursing college students, it is necessary to develop a specific strategy to improve academic achievement and to develop various activities and programs to immerse students in satisfaction of online practice.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12C
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• pp.1250-1257
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• 2003

Generally images are classified into two regions: edge and flat region. While low frequency components are popular in the flat region, high frequency components are quite important in the edge region. Therefore, deinterlacing algorithm that considers the characteristic of each region can be more efficient. In this paper, an image is divided into edge region and flat region by the local variance. And then, for each region, frequency modularized neural network is assigned. Using this structure, each modularized neural network can learn only its region intensively and avoid the complexity of learning caused by the data of different region. Using the local AC data for the input of neural network can prevent the degradation of the performance of teaming due to the average intensity values of image that disturbs the effective learning. The proposed method shows the improved performance compared with previous algorithms in the simulation.

• Journal of the Korean School Mathematics Society
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• v.23 no.3
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• pp.239-257
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• 2020

This study analyzes the degree of freedom with three aspects: as academic knowledge, in the curriculum focused on textbooks, and students' understanding of the degree of freedom. The results provide five critical points. First, we need discussions on whether to include the degree of freedom in the curriculum. Second, we need to reconsider the current way textbooks are described. Third, there should be a didactical analysis to advance students' understanding of the concept of the degree of freedom. Fourth, there are limitations in learning the concept of the degree of freedom in the current textbook learning environment. Fifth, a didactical check of sampling distribution such as sample mean, sample variance, and sample standard deviation is required. The implications were drawn that the emphasis on statistical reasoning education in the curriculum and careful consideration of introducing the t-distribution curriculum was required.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.35-42
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• 2018

Deep learning is known as a method of high accuracy among several methods for image classification. In this paper, we propose a method of enhancing the accuracy of image classification using CNN with a batch normalization method for classification of images using deep CNN (Convolutional Neural Network). In this paper, we propose a method to add a batch normalization layer to existing neural networks to enhance the accuracy of image classification. Batch normalization is a method to calculate and move the average and variance of each batch for reducing the deflection in each layer. In order to prove the superiority of the proposed method, Accuracy and mAP are measured by image classification experiments using five image data sets SHREC13, MNIST, SVHN, CIFAR-10, and CIFAR-100. Experimental results showed that the CNN with batch normalization is better classification accuracy and mAP rather than using the conventional CNN.