• IE interfaces
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• v.25 no.2
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• pp.170-177
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• 2012

Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.

• Journal of Korean Society for Quality Management
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• v.48 no.4
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• pp.553-566
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• 2020

Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network. Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.

• The Journal of Information Systems
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• v.5
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• pp.149-173
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• 1996

This paper focus on the development of object-oriented model bases for Structured Modeling. For the model base organization, object modeling techniques and model typing concept which is similar to data typing concept are used. Structured modeling formalizes the notion of a definitional system as a way of dscribing models. From the object-oriented concept, a structured model can be represented as follows. Each group of similar elements(genus) is represented by a composite class. Other type of genera can be represented in a similar manner. This hierarchical class composition gives rise to an acyclic class-composition graph which corresponds with the genus graph of structured model. Nodes in this graph are instantiated to represent the elemental graph for a specific model. Taking this class composition process one step further, we aggregate the classes into higher-level composite classes which would correspond to the structured modeling notion of a module. Finally, the model itself is then represented by a composite class having attributes each of whose domain is a composite class representing one of the modules. The resulting class-composition graph represent the modular tree of the structured.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.87-100
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• 2011

In this paper, we address a video summarization task as generating both visually salient and semantically important video segments. In order to find salient data points, one can use the OC-SVM (One-class Support Vector Machine), which is well known for novelty detection problems. It is, however, hard to incorporate into the OC-SVM process the importance measure of data points, which is crucial for video summarization. In order to integrate the importance of each point in the OC-SVM process, we propose a fuzzy version of OC-SVM. The Importance-based Fuzzy OC-SVM weights data points according to the importance measure of the video segments and then estimates the support of a distribution of the weighted feature vectors. The estimated support vectors form the descriptive segments that best delineate the underlying video content in terms of the importance and salience of video segments. We demonstrate the performance of our algorithm on several synthesized data sets and different types of videos in order to show the efficacy of the proposed algorithm. Experimental results showed that our approach outperformed the well known traditional method.

• Korean Journal of Childcare and Education
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• v.12 no.6
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• pp.123-137
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• 2016

The purpose of this study was to forecast demand of childcare teachers based ion four different scenarios. In order to, the demand for childcare teachers from 2015 to 2024 were forecasted using time series techniques with data on the number of childcare teachers from 2003 to 2014. Results were as followings. Firstly, the demand for childcare teachers was expected to increase until 2019, but after 2020 steadily decreased in terms of scenario 1(child teacher ratio regulation). According to scenario 2(child teacher ratio based on 17 cities and provinces), the demand for childcare teachers was expected to need 440 teachers more until 2016. Then, according to scenario 3(two teachers each class), Scenario 4-1(one teacher and one staff each 2 toddler class and 3 older class) and scenario 4-2(one teacher and one staff each class), the demand of childcare teachers and staffs were estimated. These results implicated that childcare teachers and staffs supply policy would be established according to forecast demand.

• Bulletin of the Korean Mathematical Society
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• v.45 no.2
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• pp.375-384
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• 2008

Let $k={\mathbb{F}}_q(T)$ and ${\mathbb{A}}={\mathbb{F}}_q[T]$. Fix a prime divisor ${\ell}$ q-1. In this paper, we consider a ${\ell}$-cyclic real function field $k(\sqrt[{\ell}]P)$ as a subfield of the imaginary bicyclic function field K = $k(\sqrt[{\ell}]P,\;(\sqrt[{\ell}]{-Q})$, which is a composite field of $k(\sqrt[{\ell}]P)$ wit a ${\ell}$-cyclic totally imaginary function field $k(\sqrt[{\ell}]{-Q})$ of class number one. und give various conditions for the class number of $k(\sqrt[{\ell}]{P})$ to be one by using invariants of the relatively cyclic unramified extensions $K/F_i$ over ${\ell}$-cyclic totally imaginary function field $F_i=k(\sqrt[{\ell}]{-P^iQ})$ for $1{\leq}i{\leq}{\ell}-1$.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1260-1270
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• 2005

Support Vector Machines (SVM) is well known for a representative learner as one of the kernel methods. SVM which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, SVM is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with a binary SVM. One-Per-Class (OPC) and All-Pairs have been applied to solve the face recognition problem, which is one of the multi-class problems, with SVM. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. In this paper, we introduce the output coding methods as an approach for extending binary SVM to multi-class SVM and propose new output coding schemes based on the Error-Correcting Output Codes (ECOC) which is a dominant theoretical foundation of the output coding methods. From the experiment on the face recognition, we give empirical results on the properties of output coding methods including our proposed ones.

• Journal of KIISE
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• v.42 no.6
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• pp.817-822
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• 2015

Relation extraction is an important information extraction technique that can be widely used in areas such as question-answering and knowledge population. Previous studies on relation extraction have been based on supervised machine learning models that need a large amount of training data manually annotated with relation categories. Recently, to reduce the manual annotation efforts for constructing training data, distant supervision methods have been proposed. However, these methods suffer from a drawback: it is difficult to use these methods for collecting negative training data that are necessary for resolving classification problems. To overcome this drawback, we propose a one-class classification model that can be trained without using negative data. The proposed model determines whether an input data item is included in an inner category by using a similarity measure based on lexical information and syntactic patterns in a vector space. In the experiments conducted in this study, the proposed model showed higher performance (an F1-score of 0.6509 and an accuracy of 0.6833) than a representative one-class classification model, one-class SVM(Support Vector Machine).

• Journal of Digital Convergence
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• v.10 no.8
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• pp.317-326
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• 2012

The study aims at analyzing and comparing the effect of video teleconferencing English class with native speakers on class satisfaction and self-efficacy of elementary school students according to grades. Revised Kim's self efficacy and researcher's class satisfaction measuring tools were used for this. Grade 3 got the highest score and grade 6 had the lowest one in class satisfaction. In post-testing on self efficacy, average score was lower than in pre-testing except grade 3. Grade 3 got the highest score and grade 6 had the lowest one in self efficacy. I conclude that as students' grade goes up, their interests decrease in teleconferencing class and native speaker teachers.

• Journal of the Korean School Mathematics Society
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• v.4 no.2
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• pp.103-113
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• 2001

The teaching-learning method utilizing Web makes it possible for the students take the initiative in any field and offers the teaching strategy, methodology and teaching-learning materials suitable for students' ability and standard. The purpose of this study is to investigate the characteristics of WBI in mathematics class for the effective teaching and learning focusing on the unit 'Function' in the 1st grade of middle school and verify its effectiveness by developing the WBI programs which can progress learning achievement and applying them to math class. Two hypotheses were established for this study. Hypothesis 1 : There will be meaningful difference between the group that studies under WBI and the one that doesn't, Hypothesis 2 : There will be meaningful difference in the attitude and interest toward learning between the group that studies under WBI and the one that doesn't. In order to find out the result, I have made a comparative analysis through t-verification on the object of two classes of the 1st grade in P middle school that I have been working for. The result shows that the class utilizing WBI is more effective than the traditional lecture-oriented class since there is a meaningful difference between the control group and experimental one and also that the class based on WEB has a great influence on students' interest and positive attitude toward math class.