• Proceedings of the KSPS conference
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• 2000.07a
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• pp.117-134
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• 2000

Kim, Key-Seop(2000). English Restructuring and A Use of Music in Teaching English Pronunciation. JSEP 2000 voU This study has two-fold aims: one is to clarify the restructuring of English in utterance, and the other is to relate it to teaching English pronunciation for listening and speaking with a use of music and song by suggesting a model of 10-15 minute pronunciation class syllabus for every period in class. Generally, English utterances are restructured by stress-timed rhythm, irrespective of syntactic boundaries. So the rhythmic units are arranged in isochronous groups, of which the making is to attach clitic(s) to a host or head often leftwards and sometimes rightwards, which results in linking, contraction, reduction, sound change and rhythm adjustment in utterance, just as in music and song. With English restructuring focused on, a model of English pronunciation class syllabus is proposed to be put forward in class for every period of a lesson or unit. It tries to relate the focused factor(s) in pronunciation to the integrated, with teaching techniques and music made use of.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.343-356
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• 2022

As a part of round robin analysis to develop a finite element elastic-plastic seismic analysis procedure for nuclear safety class 1 components, a series of parametric analyses was carried out on the simulated pressurizer surge line system model to investigate sensitivity of the analysis results to finite element analysis variables. The analysis on the surge line system model considered dynamic effect due to the seismic load corresponding to PGA 0.6 g and elastic-plastic material behavior based on the Chaboche combined hardening model. From the parametric analysis results, it was found that strains such as accumulated equivalent plastic strain and equivalent plastic strain are more sensitive to the analysis variables than von Mises effect stress. The parametric analysis results also identified that finite element density and ovalization option in the elbow elements have more significant effect on the analysis results than the other variables.

• The korean journal of orthodontics
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• v.51 no.6
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• pp.407-418
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• 2021

Objective: To investigate differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and Class II malocclusions. Methods: Forty Korean adult twin pairs were divided into Class I (C-I) group (0° ≤ angle between point A, nasion, and point B [ANB]) ≤ 4°; mean age, 40.7 years) and Class II (C-II) group (ANB > 4°; mean age, 43.0 years). Each group comprised 14 monozygotic and 6 dizygotic twin pairs. Thirty-three cephalometric variables were measured using lateral cephalograms and were categorized as the anteroposterior, vertical, dental, mandible, and cranial base characteristics. The ACE model was used to calculate heritability (A > 0.7, high heritability). Thereafter, principal component analysis (PCA) was performed. Results: Twin pairs in C-I group exhibited high heritability values in the facial anteroposterior characteristics, inclination of the maxillary and mandibular incisors, mandibular body length, and cranial base angles. Twin pairs in C-II group showed high heritability values in vertical facial height, ramus height, effective mandibular length, and cranial base length. PCA extracted eight components with 88.3% in the C-I group and seven components with 91.0% cumulative explanation in the C-II group. Conclusions: Differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and II malocclusions might provide valuable information for growth prediction and treatment planning.

• Bulletin of the Korean Chemical Society
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• v.9 no.1
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• pp.5-9
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• 1988

Pattern recognition techniques have been applied for the extraction of some regularities of water samples under a wide variety of locations related to Han-River. For that purpose, an eigenvector analysis has been applied for defining each class so as to use the class as a training set for class analogy model of SIMCA. The models thus obtained have been used for the allocation of test samples between groups.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.145-152
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• 2015

We consider an online selective-sample learning problem for sequence classification, where the goal is to learn a predictive model using a stream of data samples whose class labels can be selectively queried by the algorithm. Given that there is a limit to the total number of queries permitted, the key issue is choosing the most informative and salient samples for their class labels to be queried. Recently, several aggressive selective-sample algorithms have been proposed under a linear model for static (non-sequential) binary classification. We extend the idea to hidden Markov models for multi-class sequence classification by introducing reasonable measures for the novelty and prediction confidence of the incoming sample with respect to the current model, on which the query decision is based. For several sequence classification datasets/tasks in online learning setups, we demonstrate the effectiveness of the proposed approach.

• Journal of Engineering Education Research
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• v.21 no.4
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• pp.28-34
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• 2018

Design class through team activities is increasing in engineering education. Team-based education has been known to improve students' creativity, problem solving ability, cooperative ability, self-directed learning ability, and communication ability. How to organize a team is an important issue that affects the performance of team activities as well as student satisfaction. However, previous studies have focused on the causal relationship between team formation and the team's performance. This paper deals with how to organize a balanced team in a real class. When the basic characteristic values of students are givens, the aim is to make the sum of the characteristic values as fair as possible for each team. We propose a mathematical team formation model and show how to apply it through case studies.

• Korean Journal of Health Education and Promotion
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• v.32 no.5
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• pp.17-31
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• 2015

Objectives: This paper aims to identify the health related behaviors patterns and its associated factors among marriage immigrant women in Korea, and discusses their application to health promotion strategies. Methods: The study participants were 7,591 immigrant wives in Gyeonggi province who participated in health examinations conducted by the Korea Association of Health Promotion in 2011-2013. The participants completed self-administered questionnaires on sociodemographics, psychological characteristics, health status and health care factors, and health related behaviors. Results: A 3-latent-class model of health behaviors was identified related to 'lack of physical activity', 'abnormal diet', and 'not experienced medical check-up': 'high risk class', 'middle risk class', and 'low risk class'. Most of the participants belong to 'middle risk class'. Country of origin, age, length of stay, number of children, work status, health insurance status, and unmet health care needs were associated with problematic health behaviors in middle risk health behavior class. Conclusions: Health promotion and intervention programs for marriage immigrant women and their family members need to consider the health behavior patterns of physical inactivity, abnormal diet and no medical check-up and develop multiple behavior intervention with pre-existing program modification.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• 대한공업교육학회지
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• v.40 no.1
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• pp.180-200
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• 2015

The problems current robotic technology education class has are the students with different basic background knowledge levels and the class based on the instructional teaching method. This study shows the implementation of the student teams achievement divisions (STAD) learning model into an introductory robotic technology education class to resolve the problems in the current robotic technology class. The STAD learning model focuses on the ability of each team member with different knowledge levels and make team members help each other through class activities such as assignments and a project. All members get rewarded by their performance output as a team in a course grade. The outputs of STAD learning models were measured by paired sample t-test as pre-test and post-test in terms of students's transition on basic knowledge for robotic technology, students' attitudinal transition on teaching robotic technology class, and students' competencies and self-efficacy on related subject areas. The study participants were 22 pre-service technology teachers at a university. The results show that all four measured areas were improved significantly, compared to pre-test with respect to the means scores of each measurement area. The STAD learning model could be an alternate for the current robotic technology class to deliver the better class outcomes for students under the specific circumstances.