• The korean journal of orthodontics
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• v.26 no.1 s.54
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• pp.95-104
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• 1996

This study was undertaken to investigate the correlations bite force and the electromyographic activities of masticatory muscle in deepbite, using the T-Scan system and electromyograph. The subjects of this study consisted of two groups ; one of 20 individuals with normal occlusion, the other group of 30 with deepbite. The deepbite was composed of Class I deepbite(male 9, female 7) and Clas II div. 1 deepbite(male 8, female 6). The obtained results of this study were as follows : 1. The maximum bite force was 155.93 N in normal occlusion, 165.11 N in Class I deepbite group, 111.55 N in Class II div. 1 deepbite group. 2. The greater !he number of tooth contacts, the more the bite force increased in all groups. 3. During maximum clenching, masseter and ant. temporailsmuscle activity of normal and Class I deepbite group were significantly higher than that of Class II div. 1 deepbite group, and the activity of masseter muscle was higher than that of ant. temporalis muscle in all groups. 4. The greater the maximum bite force, the more the muscle activities increased in all groups.

• Journal of The Korean Association For Science Education
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• v.43 no.2
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• pp.87-98
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• 2023

In this study, we investigated the change in science core competency perception of high school students and the reason for change when science inquiry classes were conducted using eight 'skills' of the 2015 revised science curriculum. Fifteen first-year high school students in Jeollanam-do participated in the science inquiry class of this study, and the class was conducted for 20 hours (5 hours a day for four days). The inquiry activities used in the class consisted of four activity stages (research problems, research methods, research results, and conclusions) and each stage was constructed to include at least one 'skill (Problem Recognition, Model Development and Use, Inquiry Design and Performance, Data Collection, Analysis and Interpretation, Mathematical Thinking and Computer Application, Conclusion and Evaluation, Evidence-based Discussion and Demonstration, and Communication)'. As a result of the study, students' perception of the five science core competencies increased statistically significantly at the significance level of 0.01 through inquiry classes and more than 93% of students recognized that their science core competencies improved through the classes. However, since the class of this study was conducted for a small number of students, it is difficult to generalize the effect of the class, and so it is necessary to conduct a quantitative study for many students.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.25-37
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• 2017

The purpose of this study is to analyze the effects on particulate concept formation based on abductive reasoning model for elementary science class. For this study, an author selected two groups in the sixth grade. One group is an ordinary textbook-based control group (N=26) and the other group is an abductive reasoning model-based treatment group (N=26). After twelve lessons, the scores of Concepts Test for Gas were analyzed by t-test and two-way ANOVA. The result of t-test showed both the control and treatment groups have higher score than before they take the lesson. But after the lesson, an author found out that the treatment group had higher score than that of the control group. And compared to the number of particles expressed, the number of the treatment group were higher than that of the control class. The two-way ANOVA result revealed that the interaction effect between their cognitive level and treatment was not significant. And regardless of the level of cognition, the scores of treatment group are higher than those of control group. Therefore, abductive reasoning model-based elementary science class were found to be more effective for particulate concept formation. Based on the results, an author concluded that abductive reasoning model is very effective in teaching particulate concepts to elementary students.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.5
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• pp.165-173
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• 2017

In this research, based on the CBAM model, there is a research purpose to measure the degree of interest of the physical education teacher ahead of execution of the revised educational curriculum 2015 and to analyze the difference in degree of interest by teacher's background variation. For this reason, we conducted an an- nental survey of 200 physical education teachers in K-area middle school, high schools and analyzed the results. The conclusion of this research is as follows. Firstly, as a whole, the attention to the revised education curriculum has been very high in view, as a result, the physical education teacher at the middle / high school, 2015 about the influence and the result that the revised curriculum gives to the student in the physical education class Although the interest is very high, a systematic approach will be necessary for training and utilization schemes so that 2015 the revised educational curriculum can be executed appropriately. Secondly, since the interest in the revised educational curriculum by the whole number of classes 2015 is different, we need to develop and give a training program for revised curriculum 2015 customization by class number 2015. Training that departs from the one-way set form of transmission training and distribution method of materials depending on the class number may be different. Thirdly, in order to stabilize the revised educational curriculum 2015, it is necessary to develop a course example program corresponding to the level of interest of the physical education teacher.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.5
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• pp.55-67
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• 1982

In this Paper new results relating to the acquisition behavior of a class of first-and secondorder digital phase-locked loops (DPLL) originally proposed by Reddy and Cupta are presented in the absence of noise. It has been found that the number of quantization levels L and the number of phase error states N play important roles in acquisition. For a given L-level quantizer, as N increases, the acquisition time increases, and the lock range decreases. However, the deviation of the steady state phase error decreases in this case. When L increases, the acquisition time decreases, and the lock range increases. However, variation of L affects little for the steady state phase error. In addition, the effects of a loop filter on acquisition have also been considered. One can get smaller acquisition time and larger lock range as the filter parameter value becomes larger. However, deviation of the steady state phase error increases in that case. Analytical results have been verified by computer simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.328-333
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• 2002

The deeper the hierarchy of a inheritance structure is, the better the reusability of the structure is, but the more difficult the understandability and the maintainability of it is. On the contrary, the shallower the hierarchy is, the worse the abstraction of the inheritance structure is, but the better the understandability and modifiability of it is. Therefore, it is to be desired that a deep hierarchy of a inheritance structure should be split to be shallow for the maintainability of a system. This paper proposed a complexity metric that is based on DIT and NOC of Chidamber and Kemerer, and solved the ambiguity of the metrics of them, which was pointed out by Li. The metric is a simple and heuristic one for measuring the complexity of class inheritance structures by considering the number of ancestor classes and descendant classes and the depth of inheritance hierarchy. This provides a quantitative information for assessing the complexity of a inheritance structure in splitting it.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.1-15
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• 2009

In a classification problem, data imbalance occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. This paper proposes a Geometric Mean-based Boosting (GM-Boost) to resolve the problem of data imbalance. Since GM-Boost introduces the notion of geometric mean, it can perform learning process considering both majority and minority sides, and reinforce the learning on misclassified data. An empirical study with bankruptcy prediction on Korea companies shows that GM-Boost has the higher classification accuracy than previous methods including Under-sampling, Over-Sampling, and AdaBoost, used in imbalanced data and robust learning performance regardless of the degree of data imbalance.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.9
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• pp.965-970
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• 2014

In an effort to improve the convergence speed in k-means clustering, we introduce the notion of the birds' movement in a flock. Their motion is characterized by the observation that each bird runs after his nearest neighbor. We utilize this feature in clustering procedure. Once the class of a vector is determined, then a number of vectors in the vicinity of it are assigned to the same class. Experiments have shown that the required number of iterations for termination is significantly lower in the proposed method than in the conventional one. Furthermore, the time of calculation per iteration is more than 5% shorter in the proposed case. The quality of the clustering, as determined from the total accumulated distance between the vector and its centroid vector, was found to be practically the same. It might be phrased that we may acquire practically the same clustering result with shorter computational time.

• The Journal of the Korea institute of electronic communication sciences
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• v.4 no.4
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• pp.265-269
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• 2009

A conference key agreement system is a scheme to generate a session key in a contributory manner in order to communicate with each other securely among participants. In this paper an efficient conference key agreement system is proposed by employing symmetric balanced incomplete block design(SBIBD), one class of block designs. The protocol presented not only minimizes the message overhead and message exchanging rounds but also makes every participant contribute evenly for generating a conference key. Our protocol constructs a conference key which takes modified Diffe-Helman form of ${\prod}_{i=0}^{v-1}R_i$, where v is the number of participants and $R_i$ is a random number generated from member i. In a special class of SBIBD, it takes only 3 rounds message exchange and message overhead is $O(v{\sqrt{v}})$. Our protocol can be proved as computationally difficult to calculate as discrete logarithms.