• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.71-80
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• 2001

Object-Oriented (OO) methodology to use the concept like encapsulation, inheritance, polymorphism, and message passing demands metrics that are different from structured methodology. There are many studies for OO software metrics such as program complexity or design metrics. But the metrics for the analysis class need to decrease the complexity in the analysis phase so that greatly reduce the effort and the cost of system development. In this paper, we propose new metrics to measure the complexity of analysis classes which draw out in the analysis phase based on Unified Process. By the collaboration complexity, is denoted by CC, we mean the maximum number of the collaborations can be achieved with each of the collaborator and detennine the potential complexity. And the interface complexity, is denoted by IC, shows the difficulty related to understand the interface of collaborators each other. We prove mathematically that the suggested metrics satisfy OO characteristics such as class size and inheritance. And we verify it theoretically for Weyuker' s nine properties. Moreover, we show the computation results for analysis classes of the system which automatically respond to questions of the it's user using the text mining technique. As we compared CC and IC to CBO and WMC, the complexity can be represented by CC and IC more than CBO and WMC. We expect to develop the cost-effective OO software by reviewing the complexity of analysis classes in the first stage of SDLC (Software Development Life Cycle).

• Journal of KIISE:Software and Applications
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• v.28 no.10
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• pp.720-731
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• 2001

Complexity metrics have been developed for the structured paradigm of software development are not suitable for use with the object-oriented(OO) paradigm, because they do not support key object-oriented concepts such as inheritance, polymorphism. message passing and encapsulation. There are many researches on OO software metrics such as program complexity or design metrics. But metrics measuring the complexity of classes at the OO analysis phase are needed because they provide earlier feedback to the development project. and earlier feedback means more effective developing and less costly maintenance. In this paper, we propose the new metrics to measure the complexity of analysis classes which draw out in the analysis based on RUP(Rational Unified Process). By the collaboration complexity, is denoted by CC, we mean the maximum number of the collaborations can be achieved with each of the collaborator and determine the potential complexity. And the interface complexity, is denoted by IC, shows the difficulty related to understand the interface of collaborators each other. We verify theoretically the suggested metrics for Weyuker's nine properties. Moreover, we show the computation results for analysis classes of the system which automatically respond to questions of the user using the text mining technique. As a result of the comparison of CC and CBO and WMC suggested by Chidamber and Kemerer, the class that have highly the proposed metric value maintain the high complexity at the design phase too. And the complexity can be represented by CC and IC more than CBO and WMC. We can expect that our metrics may provide us the earlier feedback and hence possible to predict the efforts, costs and time required to remainder processes. As a result, we expect to develop the cost-effective OO software by reviewing the complexity of analysis classes in the first stage of SDLC(Software Development Life Cycle).

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.551-559
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• 2017

In this paper, a real-time training method to improve the performance of bearing fault diagnosis. The traditional bearing fault diagnosis cannot classify a condition which is not trained by the classifier. The proposed 4-step method trains and recognizes new condition in real-time, thereby it can classify the condition accurately. In the first step, we calculate the maximum distance value for each class by calculating a Euclidean distance between a feature vector of each class and a centroid of the corresponding class in the training information. In the second step, we calculate a Euclidean distance between a feature vector of new acquired data and a centroid of each class, and then compare with the allowed maximum distance of each class. In the third step, if the distance between a feature vector of new acquired data and a centroid of each class is larger than the allowed maximum distance of each class, we define that it is data of new condition and increase count of new condition. In the last step, if the count of new condition is over 10, newly acquired 10 data are assigned as a new class and then conduct re-training the classifier. To verify the performance of the proposed method, bearing fault data from a rotating machine was utilized.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.57-62
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• 2004

We have proposed an efficient method of word separation in a handwritten legal amount on bank check based on the spatial gaps between the connected components. The previous gap measures all suffer from the inherent problem of underestimation or overestimation that causes a deterioration in separation performance. In order to alleviate such burden, we have developed a modified version of each distance measure. Also, 4 class clustering based method of integrating three different types of distance measures has been proposed to compensate effectively the errors in each measure, whereby further improvement in performance of word separation is expected. Through a series of word separation experiments, we found that the modified distance measures show a better performance with over 2 - 3% of the word separation rate than their corresponding original distance measures. In addition, the proposed combining method based on 4-class clustering achieved further improvement by effectively reducing the errors common to two of three distance measures as well as the individual errors.

• Journal of the Korean Society for information Management
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• v.27 no.2
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• pp.7-20
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• 2010

This study aims to provide a method of extracting the most recent information on US patent documents. An HTML paring technique that can directly connect to the US Patent and Trademark Office (USPTO) Web page is adopted. After obtaining a list of 50 documents through a keyword searching method, this study suggested an algorithm, using HTML parsing techniques, which can extract a patent number, an applicant, and the US patent class information. The study also revealed an algorithm by which we can extract both patents and subsequent patents using their closely connected relationship, that is a very distinctive characteristic of US patent documents. Although the proposed method has several limitations, it can supplement existing databases effectively in terms of timeliness and comprehensiveness.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.460-462
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• 1999

소프트웨어의 경제성, 시장 경쟁력 확보를 위한 소프트웨어의 재사용은 소프트웨어 공학의 주요 이슈가 되고 있다. 그중 컴포넌트와 컴포넌트 기반의 소프트웨어 개발은 재사용성을 확보할 수 있는 가장 주목 받는 방안으로 제시되고 있으며 많은 기법이나 지침들이 제안되고 있다. 본 논문에서는 컴포넌트 개발에서 UML에 기반하여 컴포넌트의 인터페이스를 추출하는 기법을 제시하고자 한다. 분석 단계에서 컴포넌트의 식별이 이루어졌다고 가정하고 분석 단계에서 나온 산출물 중, Use Case 모델과 클래스 다이어그램을 이용하여 컴포넌트의 메소드들을 식별하고, 인터페이스로 정의한다. 그리고 사용자요구사항에 근거하여 Hot Spot을 식별한 후, 컴포넌트의 커스터마이즈를 위한 메소드와 인터페이스를 정의한다.

• Journal of KIISE
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• v.44 no.7
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• pp.686-691
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• 2017

Telemarketing has become the center of marketing action of the industry in the information society. Recently, machine learning has emerged in many areas, especially, financial prediction. Financial data consists of lots of unlabeled data in most parts, and therefore, it is difficult for humans to perform their labeling. In this paper, we propose a fusion method of semi-supervised learning for automatic labeling of unlabeled data to predict telemarketing. Specifically, we integrate labeling results of label propagation and co-training with a decision tree. The data with lower reliabilities are removed, and the data are extracted that have consistent label from two labeling methods. After adding them to the training set, a decision tree is learned with all of them. To confirm the usefulness of the proposed method, we conduct the experiments with a real telemarketing dataset in a Portugal bank. Accuracy of the proposed method is 83.39%, which is 1.82% higher than that of the conventional method, and precision of the proposed method is 19.37%, which is 2.67% higher than that of the conventional method. As a result, we have shown that the proposed method has a better performance as assessed by the t-test.

• Journal of the Korea Computer Industry Society
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• v.4 no.10
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• pp.631-642
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• 2003

We propose an approach to extract and to classify facial features into some classes from one's photo as prepared classification standards to generate one's avatar. Facial Feature Extraction and Classification was executed at eyes, nose, lips, jaw separately and I presented each facial features and classification standards. Extracted Facial Features are used for calculation to features of professional designer's facial component images. Then, most similar facial component images are mapped onto avatar's vector face.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.520-522
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• 1998

기존의 소프트웨어에서 함수나 클래스와 같은 부분을 추출해 재사용하는 방법은 비용면에서 상당히 효율적이다. 그러나 이러한 방법을 사용한 기존의 연구들은 추출해 낸 부분이 표준 컴포넌트의 형태를 갖추지 못해서 일반적인 개발환경에서 활용되지 못하고 있다. 본 연구에서는 기존의 객체지향 소프트웨어에서 재사용성 높은 부분을 추출하여 이를 표준 컴포넌트로 변환하는 재사용 컴포넌트 생성 시스템을 설계 및 구현하였다. 구현환경 Solaris에서 자바를 이용하였고 대상 컴포넌트 모델은 자바빈즈이다. 본 연구에서는 추출되는 컴포넌트의 높은 재사용성을 보장하기 위하여 객체지향 재사용 메트릭스 WMC, LCOM과 독립성을 적용한다. 특히, 생성된 컴포넌트는 자신의 서브컴포넌트 추출을 지원함으로써 높은 재사용성과 유사성을 보장하고, 개발환경에서 적절히 활용될 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.517-519
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• 2001

클래스 라이브러리, 설계 패턴, 프레임워크 등의 재사용 기술과 더불어 근래 들어 소프트웨어 컴포넌트 기술이 크게 각광을 받았다. 산업계에서도 Microsoft사 중심의 COM+와 Sun사 중심의 Enterprise Java Bean (EJB) [1]이 대두되 있으며 있따른 제품들이 출시되 있다. 소프트웨어의 재사용 단위가 객체에서 컴포넌트로 이동함으로써 기존 객체 중심의 개발 방법론, 모델링 및 개발도구들이 컴포넌트 개념을 포함하며 발전하 있다. 본 논문은 컴포넌트 추출에 초점을 두 있다. 서블릿[2] 프로그램에서 EJB 컴포넌트 추출을 위한 기법을 제공한다. 서블릿기반으로 개발된 웹 프로그램을 분석하여 재사용 가능한 EJB 컴포넌트를 추출하여 차후 유사만 어플리케이션 개발 시 활용하고자 한다. 서블릿의 메소드들을 분석하여 메소드 특성에 따라 session bean [1] 또는 entity bean [11의 메소드로 변환한다. 제안된 컴포넌트 추출 과정을 기술하 Unified Modeling Language(UML)[3] 기반의 추출 모듈에 대한 분석, 설계 다이어그램을 제공한다.