• Communications of the Korean Mathematical Society
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• v.9 no.4
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• pp.773-782
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• 1994

Investigation of the number of representations as well as of projective representations of a finite group has been important object since the early of this century. The numbers are very related to the number of conjugacy classes of G, so that this gives some informations on finite groups and on group characters. A generally well-known fact is that the number of non-equivlaent irreducible representations, which we shall write as n.i.r. of G is less than or equal to the number of conjugacy classes of G, and the equality holds over an algebraically closed field of characteristic not dividing $\mid$G$\mid$. A remarkable result on the numbers due to Reynolds can be stated as follows.

• The Korean Journal of Ecology
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• v.17 no.4
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• pp.453-461
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• 1994

Size class structure and vegetative growth of a perennial herb of the temperate deciduous forests, Symplocarpus renifolius Schott, were studied from 1991 to 1994 in Namhansansung, Kyonggi Province, Korea. The size class structures of leaf number and leaf area per individual followed bell-shape curve, i.e. frequency of middle class was relatively high. The leaf area increased from the late-March to mid-May. At the end of the growing season, leaf area(length X breadth) was proportional to biomass, especially aboveground biomass. The leaf number and leaf area per individual increased at the rate of 0.08 leaf/year and 9.7 $cm^2/year$, respectively. The size of the individuals in large-sized classes, in leaf number and leaf area, decreased in next year, while the size of the individuals in small-sized classes increased. Therefore, it was concluded that the size class structure of S. renifolius population was largely determined by the growth form.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.371-377
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• 2010

In this study, we propose two informal hypothesis tests which may be useful in determining the number of trees in a random forest for use in classification. The first test declares that a case is 'easy' if the hypothesis of the equality of probabilities of two most popular classes is rejected. The second test declares that a case is 'hard' if the hypothesis that the relative difference or the margin of victory between the probabilities of two most popular classes is greater than or equal to some small number, say 0.05, is rejected. We propose to continue generating trees until all (or all but a small fraction) of the training cases are declared easy or hard. The advantage of combining the second test along with the first test is that the number of trees required to stop becomes much smaller than the first test only, where all (or all but a small fraction) of the training cases should be declared easy.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.2
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• pp.29-40
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• 2005

In this research, we propose packet scheduling algorithm considering different QoS characteristics of real-time traffic and non-real-time traffic in the next generation wireless communication system serving the multimedia traffic and a new efficient framing method cooperated with this packet scheduler. When the selected traffic classes of the selected users are transmitted, our proposed framing method can increase the number of serviced traffic classes by mixing the many different traffic classes within one frame considering data rate decided by the allocated AMC (Adaptive Modulation and Coding) option. Using this proposed method, the fairness among the traffic classes can be achieved and the system performance for total throughput and delay can be enhanced. Simulations are performed to analyze the performance of the proposed framing method. Our proposed packet scheduler and framing method will be applied to the next generation multimedia wireless communication system serving many traffic classes.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.847-859
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• 2011

cDNA microarray-based comparative genomic hybridization(CGH) data includes low-intensity spots and thus a statistical strategy is needed to detect subtle differences between different cancer classes. In this study, genes displaying a high frequency of alteration in one of the different classes were selected among the pre-selected genes that show relatively large variations between genes compared to total variations. Utilizing copy-number changes of the selected genes, this study suggests a statistical approach to predict patients' classes with increased performance by pre-classifying patients with similar genetic alteration scores. Two-stage logistic regression model(TLRM) was suggested to pre-classify homogeneous patients and predict patients' classes for cancer prediction; a decision tree(DT) was combined with logistic regression on the set of informative genes. TLRM was constructed in cDNA microarray-based CGH data from the Cancer Metastasis Research Center(CMRC) at Yonsei University; it predicted the patients' clinical diagnoses with perfect matches (except for one patient among the high-risk and low-risk classified patients where the performance of predictions is critical due to the high sensitivity and specificity requirements for clinical treatments. Accuracy validated by leave-one-out cross-validation(LOOCV) was 83.3% while other classification methods of CART and DT performed as comparisons showed worse performances than TLRM.

• The Korean Journal of Community Living Science
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• v.15 no.2
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• pp.43-54
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• 2004

The purpose of this study is to examine the job satisfaction of teachers in urban and rural day care centers. The subjects of this study were 134 teachers in urban(n=68) and rural(n=66) areas. Participants completed an early childhood job satisfaction survey. The statistics used for this data were t-test, one-way ANOVA (Duncan's test), and multiple regression analysis. The study showed significant differences between urban and rural teachers' job satisfaction in the job itself, human relationships, and working environment. The job satisfaction of the teachers in urban day care centers was influenced significantly by the related variables: age of teacher, marital status, education level, educational career, capacity, number of classes, class size, working hours, and the age of the children in class. The job satisfaction of the teachers in rural day care centers was influenced significantly by the above variables. The important factors on predicting the job satisfaction of the urban day care teachers were found to be the age of teachers, educational career, education level, and number of classes. The important factors for predicting the job satisfaction of the rural day care center teachers were found to be the age of teacher, educational career, education level, number of classes and working hours.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.341-348
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• 2021

With the advent of the pandemic era, non-face-to-face classes are being conducted, and many studies are being conducted on the effects of non-face-to-face classes in many subjects. Since the programming class is a subject that combines knowledge transfer and practice. In the case of non-face-to-face classes, I wanted to know how students perceive them through a survey. The number of students who answered that traditional face-to-face classes were good in terms of ease of question and answer, understanding of class content and immersion in class, and more students answered that non-face-to-face classes were good in terms of securing practice time and class control. It is expected that using the results of this study will help plan effective programming lessons.

• Journal of The Korean Association For Science Education
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• v.25 no.7
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• pp.794-800
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• 2005

The purpose of this study was to examine the state of management and science teachers perception of science classes and clubs in secondary schools. There ware 125 participants (75 middle school teachers and 50 high school teachers) from eight metropolitan cities and local areas, and more than 80% of the participants had managed science classes or clubs. Results found that a significant number of science teachers felt it necessary to manage science classes and/or clubs in secondary schools. They believed the classes and/or clubs to be beneficial in the activation of students' interest in science, even though they might not be effective ways of improving scientific achievement. In addition, it was found that secondary science teachers desire financial support, in-service programs, and efficacious programs supporting science class and club management.

• Journal of Korean Elementary Science Education
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• v.36 no.4
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• pp.394-404
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• 2017

This study aimed to investigate the effects of science classes applying round robin strategy on scientific communicative competence, science learning motivation and academic achievement of elementary students. The number of participants were 126, 4 classes of 6th grade in C elementary school in B city. The experimental group, 2 classes including 63 participants, had science classes applying round robin strategy. While the comparative group, 2 classes including 63 participants, took ordinary teacher-driven lessons using teacher's guidebook. Pre and post tests were done before and after intervention to assess the changing in each group's scientific communicative competence, science learning motivation, and academic achievement. The results of this study can be summarized as follows: The science class applying round robin strategy showed significant effect on improving scientific communicative competence and science learning motivation and academic achievement of elementary students.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.1-8
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• 2023

Monitoring and prediction of water quality are essential for effective river pollution prevention and water quality management. In this study, a multi-classification model was developed to predict chlorophyll-a (Chl-a) level in rivers. A model was developed using CatBoost, a novel ensemble machine learning algorithm. The model was developed using hourly field monitoring data collected from January 1 to December 31, 2015. For model development, chl-a was classified into class 1 (Chl-a≤10 ㎍/L), class 2 (10<Chl-a≤50 ㎍/L), and class 3 (Chl-a>50 ㎍/L), where the number of data used for the model training were 27,192, 11,031, and 511, respectively. The macro averages of precision, recall, and F1-score for the three classes were 0.58, 0.58, and 0.58, respectively, while the weighted averages were 0.89, 0.90, and 0.89, for precision, recall, and F1-score, respectively. The model showed relatively poor performance for class 3 where the number of observations was much smaller compared to the other two classes. The imbalance of data distribution among the three classes was resolved by using the synthetic minority over-sampling technique (SMOTE) algorithm, where the number of data used for model training was evenly distributed as 26,868 for each class. The model performance was improved with the macro averages of precision, rcall, and F1-score of the three classes as 0.58, 0.70, and 0.59, respectively, while the weighted averages were 0.88, 0.84, and 0.86 after SMOTE application.