• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.3
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• pp.57-65
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• 1995

This paper presents a new classified vector quantization (VQ) technique using a neural network model in the transform domain. Prior to designing a codebook, the proposed approach extracts class features from a set of images using self-organizing feature map (SOFM) that has the pattern recognition characteristics and the same as VQ objective. Since we extract the class features from the training images unlike previous approaches, the reconstructed image quality is improved. Moreover, exploiting the adaptivity of the neural network model makes our approach be easily applied to designing a new vector quantizer when the processed image characteristics are changed. After the generalized BFOS algorithm allocates the given bits to each class, codebooks of each class are also generated using SOFM for the maximal reconstructed image quality. In experimental results using monochromatic images, we obtained a good visual quality in the reconstructed image. Also, PSNR is comparable to that of other classified VQ technique and is higher than that of JPEG baseline system.

• Journal for History of Mathematics
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• v.32 no.4
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• pp.195-213
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• 2019

This study sampled 6,535 grade 11 or 12 students in South Korea using a stratified random sampling method in order to identify the differences in the perception of students on what a good mathematics class is by school class and region. The results showed that four elements of a good mathematics class were significantly different among school classes and regions.

• Proceedings of the KACD Conference
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• 2003.11a
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• pp.564-564
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• 2003

In recent years, the use of Microtomograph in dentistry were proposed and its applications are increasing. The purposes of this study is to evaluate the micro leakage in Class V restorations by using Microtomograph and to compare with other leakage test methods. Using high speed round bur, Class V cavities were prepared to the buccal sides of sixty extracted human upper premolars and randomly distributed to 4 experimental groups and restored as follows.(중략)

• Imaging Science in Dentistry
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• v.49 no.4
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• pp.281-286
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• 2019

Purpose: The purpose of this study was to evaluate the morphology of the mandibular cortex in cases of medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis or bone metastases using a computer programme. Materials and Methods: Fifty-four patients with MRONJ (35 with osteoporosis and 19 with bone metastases) were examined using panoramic radiography. The morphology of the mandibular cortex was evaluated using a computer programme that scanned the mandibular inferior cortex and automatically assessed the mandibular cortical index (MCI) according to the thickness and roughness of the mandibular cortex, as follows: normal (class 1), mildly to moderately eroded (class 2), or severely eroded (class 3). The MCI classifications of MRONJ patients with osteoporosis or bone metastases were evaluated with the Pearson chi-square test. In these analyses, a 5% significance level was used. Results: The MCI of MRONJ patients with osteoporosis(class 1: 6, class 2: 15, class 3: 14) tended to be higher than that of patients with bone metastases(class 1: 14, class 2: 5, class 3: 0)(P=0.000). Conclusion: The use of a computer programme to assess mandibular cortex morphology may be an effective technique for the objective and quantitative evaluation of the MCI in MRONJ patients with osteoporosis or bone metastases.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2298-2309
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• 2000

This paper proposes the multidimensioa! nested attribute indexing techniques (MD- NAI) in object-oriented databases using a multidimensional index structure. Since most conventional indexing techniques for object oriented databases use a one-dimensional index stnlcture such as the B-tree, they do not often handle complex qUlTies involving both nested attributes and class hierarchies. We extend a tunable two dimensional class hierachy indexing technique(2D-CHI) for nested attributes. The 2D-CHI is an indexing scheme that deals with the problem of clustering ohjects in a two dimensional domain space that consists of a kev attribute dOI11'lin and a class idmtifier domain for a simple attribute in a class hierachy. In our extended scheme, we construct indexes using multidimensional file organizations that include one class identifier domain per class hierarchy on a path expression that defines the indexed nested attribute. This scheme efficiently suppoI1s queries that involve search conditions on the nested attribute represcnted by an extcnded path expression. An extended path expression is a one in which a class hierarchy can be substituted by an indivisual class or a subclass hierarchy in the class hierarchy.

• The korean journal of orthodontics
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• v.15 no.1
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• pp.85-92
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• 1985

This investigation was designed to compare the craniofacial morphology of Class III malocclusions with that of normal occlusions using PM line. The subjects consisted of forty-four normal occlusions (twenty-three males and twenty-one females) and sixty-nine Class III malocclusions (thirty males and thirty-nine females), aged eight through ten. Using the tracings of the standard lateral cephalograms, various angles, linear measurements and linear ratios of counter-part were recorded and analyzed by t-test. The following characteristics of craniofacial morphology of Class III malocclusion were obtained by this study. 1. Maxillary anteroposterior position was balanced with Nasion but was not balanced with mandible because maxillary bony arch was small and positioned posteriorly and mandibular corpus was large and positioned relatively anteriorly. 2. Upper and lover alveolar bony arch were not balanced each other in its size. 3. In counterpart analysis, Class III malocclusion was more horizontally unbalanced than normal occlusion. 4. Class III malocclusion was divided into 11 groups by maxillary and mandibular bony arch position, size and alveolar bony arch size. Unbalanced bony size of the maxilla and mandible was a major characteristics of Class III malocclusion.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.228-234
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• 2018

The aim of this study was to analyze the empathy represented in the learners' group journal of integrated English class using literature. 15 high school students participated in this class. In this study, integrated English class using literature was carried out by supplementing the point that amount of the English classes using literature had been focused on reading activities. In addition, not only communicative abilities but also learners' empathy to the main character in the literary was taught. In order to analyze the empathy expressed in learners' group journal, the integrated English class using literature was conducted in the second period and the class was recorded by video. The empathy was based on the community competence mentioned in the 2015 revised curriculum, and learners were asked to write the group journal. As a result of the research, the learners showed an understanding of the context in the novel and learners' group journal showed that their empathy to the main character in the novel. It is expected that the data on the empathy represented in the learner group journal of the integrated English class using literature will be used in English class.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.154-157
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• 2008

A class-E RF(Radio Frequency) power amplifier for wireless application is designed using standard CMOS technology. To drive the class-E power amplifier, a class-F RF power amplifier is used and the reliability characteristics are studied with a class-E load network. The reliability characteristic is improved when a finite-DC feed inductor is used instead of an RF choke with the load. After one year of operating, when the load is an RF choke the output current and voltage of the power amplifier decrease about 17% compared to initial values. But when the load is a finite DC-feed inductor the output current and voltage decrease 9.7%. The S-parameter such as input reflection coefficient(S11) and the forward transmission scattering parameter(S21) is simulated with the stress time. In a finite DC-feed inductor the characteristics of S-parameter are changed slightly compared to an RF-choke inductor. From the simulation results, the class-E power amplifier with a finite DC-feed inductor shows superior reliability characteristics compared to power amplifier using an RF choke.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.73-80
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• 2016

Most fault prediction models have class imbalance problems because training data usually contains much more non-fault class modules than fault class ones. This imbalanced distribution makes it difficult for the models to learn the minor class module data. Data imbalance is much higher when severity-based fault prediction is used. This is because high severity fault modules is a smaller subset of the fault modules. In this paper, we propose severity-based models to solve these problems using the three sampling methods, Resample, SpreadSubSample and SMOTE. Empirical results show that Resample method has typical over-fit problems, and SpreadSubSample method cannot enhance the prediction performance of the models. Unlike two methods, SMOTE method shows good performance in terms of AUC and FNR values. Especially J48 decision tree model using SMOTE outperforms other prediction models.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.43-58
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• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.