• 시스템엔지니어링워크숍
• /
• s.1
• /
• pp.127-133
• /
• 2003

시나리오 형태의 요구사항 분류는 ATAM, SAAM, Software Quality Metric 과 같은 품질 요구사항 분석 및 평가 방법 등 많은 분야에 응용된다. 이들 기법들은 소프트웨어 시스템의 품질 요구사항을 분석, 평가하기에 앞서 초기 수집된 요구사항들을 분류하게 된다. 그러나 요구사항을 분류하는 일은 수작업을 통해 이루어지게 되고, 따라서 미 분류, 중복분류, 등의 결함을 가질 수 있다. 결함의 가능성을 요구사항의 수가 많은 대형 프로젝트 일수록 높아지게 된다. 따라서 본 논문에서는 요구사항 분류언어를 통한 품질 요구사항 자동 분류 기법을 제안한다. 제안된 기법은 분류언어와 유사도를 이용한 2 단계 분류기법을 이용하였다. 분류언어는 각 도메인별로 개발되어 비슷한 도메인일 경우 재사용될 수 있다. 이를 검증하기 위해, 본 논문에서는 15 여개의 프로젝트로부터 수집된 요구사항을 이용해 실험을 수행하고 그 결과를 분석, 평가 하였다.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.1
• /
• pp.1-6
• /
• 2013

For predicting defect-prone module in software, SVM classifier showed good performance in a previous research. But there are disadvantages that SVM parameter should be chosen differently for every kernel, and algorithm should be performed iteratively for predict results of changed parameter. Therefore, we find these parameters using Genetic Algorithm and compare with result of classification by Backpropagation Algorithm. As a result, the performance of GA-SVM model is better.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.2
• /
• pp.153-159
• /
• 2009

The steel balls are core elements between inner part and outer part in a bearing system. The degree of goodness of the steel balls has been visually processed by human beings. In this paper we propose a new method that uses image processing algorithm and fuzzy logic theory. We use fuzzy inference engine and fuzzy Choquet integral algorithm in the proposed system. We first distinguish the defects of the steel balls by an image processing algorithm. And then the degree of the defects is classified by a fuzzy logic system. We perform some simulations to show the effectiveness and feasibility of the proposed system.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.7
• /
• pp.205-212
• /
• 2020

Defects are an important factor in the quality of software developments. In order to manage defects, we propose additional information of search and classification. Additional information suggests a systematic classification scheme and method of operation. In this study, we propose additional information at the requirements analysis stage for defect management.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.15 no.2
• /
• pp.395-406
• /
• 1995

Classification of flaws in weldments from their ultrasonic scattering signals is very important in quantitative nondestructive evaluation. This problem is ideally suited to a modern ultrasonic pattern recognition technique. Here brief discussion on systematic approach to this methodology is presented including ultrasonic feature extraction, feature selection and classification. A stronger emphasis is placed on probabilistic neural networks as efficient classifiers for many practical classification problems. In an example probabilistic neural networks are applied to classify flaws in weldments into 3 classes such as cracks, porosity and slag inclusions. Probabilistic nets are shown to be able to exhibit high performance of other classifiers without any training time overhead. In addition, forward selection scheme for sensitive features is addressed to enhance network performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.3
• /
• pp.151-157
• /
• 2018

Most previous studies of software fault prediction have focused on supervised learning models for binary classification that determines whether an input module has faults or not. However, binary classification model determines only the presence or absence of faults in the module without considering the complex characteristics of the fault, and supervised model has the limitation that it requires a training data set that most development groups do not have. To solve these two problems, this paper proposes severity-based ternary classification model using unsupervised learning algorithms, and experimental results show that the proposed model has comparable performance to the supervised models.

• Proceedings of the KAIS Fall Conference
• /
• 2000.10a
• /
• pp.121-123
• /
• 2000

섬유의 자동 검사에서는 섬유 패턴과 연관이 있는 결함과 패턴과 연관이 없는 결함의 2 부류를 검사하게 된다 본 논문에서는 이들 결함의 검사를 2 단계에 거쳐서 하게 되는데, 섬유 패턴에 독립적인 결함을 프로파일 분석을 통해 우선 검출하고, 섬유 패턴에 종속적인 결함을 co-occurrence 행렬을 이용해 검출하는 기법을 소개한다. 이렇게 해서 검출된 결함들은 Back-propagation 알고리즘을 사용해 분류된다. 이 기법을 통한 실험에서 백색 유광택 타포린에서 발생하는 이물질유입 및 위사빠짐을 97.1%이상 검출할 수 있었다.

• Journal of Advanced Navigation Technology
• /
• v.18 no.4
• /
• pp.304-311
• /
• 2014

In this paper, we propose a ultra sound inspection technique for automatic defect detection and classification in aircraft composite materials. Using local maximum values of ultra sound wave, we choose peak values for defect detection. Distance data among peak values are used to construct histogram and to determine surface and back-wall echo from the floor of composite materials. C-scan image is then composed through this method. A threshold value is determined by average and variance of the peak values, and defects are detected by the values. PCA(principal component analysis) and QDA(quadratic discriminant analysis) are carried out to classify the types of defects. In PCA, 512 dimensional data are converted into 30 PCs(Principal Components), which is 99% of total variances. Computational cost and misclassification rate are reduced by limiting the number of PCs. A decision boundary equation is obtained by QDA, and defects are classified by the equation. Experimental result shows that our proposed method is able to detect and classify the defects automatically.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.21 no.1
• /
• pp.54-61
• /
• 2001

A software package to classify acoustic emission (AE) signals using the wavelet transform and the neural network was developed Both of the continuous and the discrete wavelet transforms are considered, and the error back-propagation neural network is adopted as m artificial neural network algorithm. The signals acquired during the 3-point bending test of specimens which have artificial defects on weld zone are used for the classification of the defects. Features are extracted from the time-frequency plane which is the result of the wavelet transform of signals, and the neural network classifier is tamed using the extracted features to classify the signals. It has been shown that the developed software package is useful to classify AE signals. The difference between the classification results by the continuous and the discrete wavelet transforms is also discussed.

• Proceedings of the Korean Information Science Society Conference
• /
• 2000.10c
• /
• pp.677-679
• /
• 2000

결함의 검출은 결함 허용 시스템의 결함 허용성과 신뢰도 분석에 있어서 기초가 된다. 결함 검출 기법들은 각기 다른 특성을 가지고 있어 결함의 종류에 따라 다른 검출 능력을 가지기 때문에 효율적으로 시스템의 신뢰도를 향상시키기 위해서는 결함의 종류에 따라 적절한 기법들을 선별하여 적용해야 할 필요가 있다. 하지만 기존의 연구에서는 결함 검출 기법들에 대해 비교 검토에 대한 연구가 미흡하다. 따라서 결함의 종류에 따른 결함 검출 기법들의 성능을 평가하기 위한 테스트베드가 요구된다. 본 논문에서는 결함 검출을 위해 사용되고 있는 기법들의 종류를 분류하고 특성을 서술한다. 그리고, 리눅스 환경에서 소프트웨어로 구현된 결함 삽입 도구를 이용하여 각 결함 검출 기법들의 성능을 비교하기 위한 테스트베드를 설계한다.