• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.675-677
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• 2005

This study proposes a fuzzy classification using EM algorithm. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes.

• Journal of Electrical Engineering and information Science
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• 제1권1호
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• pp.164-172
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• 1996

The partitioning of related objects should be performed before clustering for an efficient access in object-oriented databases. In this paper, a horizontal partition of related objects in object-oriented databases is presented. All subclass nodes in a class inheritance hierarchy of a schema graph are shrunk to a class node in the graph that is called condensed schema graph because the aggregation hierarchy has more influence on the partition than the class inheritance hierarchy. A set function and an accessibility function are defined to find a maximal subset of related objects among the set of objects in a class. A set function maps a subset of the domain class objects to a subset of the range class objects. An accessibility function maps a subset of the objects of a class into a subset of the objects of the same class through a composition of set functions. The algorithm derived in this paper is to find the related objects of a condensed schema graph using accessibility functions and set functions. The existence of a maximal subset of the related objects in a class is proved to show the validity of the partition algorithm using the accessibility function.

• International Journal of Contents
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• 제2권1호
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• pp.34-38
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• 2006

In this paper, an overview of anti-collision algorithm for RFID system of a standard EPC Class1 protocol is presented, and the binslotted dynamic search algorithm (BDS) based upon the slotted ALOHA and binary tree procedure is proposed and analyzed. Also, the performance is evaluated as comparing the BDS algorithm with the standard bin-slotted algorithm (BSA) through the simulation program. The performance of the proposed BDS algorithm is improved by dynamically identifying the collided-bit position and the collided bins stored in the stack of the reader. As the results, the number of request command that a reader send to tags in the reader s interrogation zone and the total recognition time are decreased to 59% as compared with BSA algorithm. Therefore, the tag identification performance is fairly improved by resolving a collision problem using the proposed BDS algorithm.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 한국지능정보시스템학회
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• pp.309-320
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• 2007

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. It causes low prediction accuracy of the minority class because classifiers tend to assign instances to major classes and ignore the minor class to reduce overall misclassification rate. In order to solve the data imbalance problem, there has been proposed a number of techniques based on resampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.

• 정보교육학회논문지
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• 제20권2호
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• pp.161-170
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• 2016

최근 소프트웨어 활용교육 중심에서 프로그래밍을 비롯한 컴퓨터과학을 기반으로 한 사고력 향상 중심의 컴퓨터 교육이 강조되고 있다. 시각화 자료를 이용하거나 활동 중심의 언플러그드 활동 중심의 알고리즘 교육을 기반으로 초등 알고리즘 교육에 대한 여러 연구가 이루어지고 있다. 그러나 아직 초등학생의 특성을 살린 학습자료나 수업 방법이 부족하여 실제 학교 현장에 도입하기에 여러 가지 어려움이 있다. 이에 본 연구에서 관련 선행 연구를 분석하여, 초등학생들의 발달단계에 적합한 활동중심의 검색 알고리즘 수업을 설계하였다. 본 연구에서 개발한 수업 설계를 통하여 학생들의 사고력을 향상시킬 수 있는 알고리즘 관련 수업이 더욱 확대되기를 기대한다.

• 대한산업공학회지
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• 제40권4호
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• pp.404-414
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• 2014

Yield prediction is important to manage semiconductor quality. Many researches with machine learning algorithms such as SVM (support vector machine) are conducted to predict yield precisely. However, yield prediction using SVM is hard because extremely imbalanced and big data are generated by final test procedure in semiconductor manufacturing process. Using SVM algorithm with imbalanced data sometimes cause unnecessary support vectors from major class because of unselected support vectors from minor class. So, decision boundary at target class can be overwhelmed by effect of observations in major class. For this reason, we propose a under-sampling method with minor class based SVM (MCSVM) which overcomes the limitations of ordinary SVM algorithm. MCSVM constructs the model that fixes some of data from minor class as support vectors, and they can be good samples representing the nature of target class. Several experimental studies with using the data sets from UCI and real manufacturing process represent that our proposed method performs better than existing sampling methods.

• Journal of applied mathematics & informatics
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• 제29권5_6호
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• pp.1285-1293
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• 2011

We establishes the polynomial convergence of a new class of path-following methods for semidefinite linear complementarity problems (SDLCP) whose search directions belong to the class of directions introduced by Monteiro [9]. Namely, we show that the polynomial iteration-complexity bound of the well known algorithms for linear programming, namely the predictor-corrector algorithm of Mizuno and Ye, carry over to the context of SDLCP.

• 대한수학회논문집
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• 제18권3호
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• pp.459-468
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• 2003

In this paper, we introduce and study a new class of the generalized set-valued mixed variational inequalities. Using the resolvent operator technique, we construct a new iterative algorithm for solving this class of the generalized set-valued mixed variational inequalities. We prove the existence of solutions for the generalized set-valued mixed variational inequalities and the convergence of the iterative sequences generated by the algorithm.

• Korean Journal of Mathematics
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• 제25권1호
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• pp.19-35
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• 2017

In this communication, we introduce an Ishikawa type iterative algorithm for finding the approximate solutions of a class of nonlinear set valued variational inclusion problems. We also establish a characterization of strong convergence of this iterative techniques.

• East Asian mathematical journal
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• 제27권5호
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• pp.585-595
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• 2011

In this paper, we introduce and study a new class of strongly nonlinear variational-like inequalities. Under suitable conditions, we prove the existence of solutions for the class of strongly nonlinear variational- like inequalities. By making use of the auxiliary principle technique, we suggest an iterative algorithm for the strongly nonlinear variational-like inequality and give the convergence criteria of the sequences generated by the iterative algorithm.