• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.77-90
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• 2018

Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.157-166
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• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.1
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• pp.63-70
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• 2010

In this paper, we study the structure of neural network for predicting defect size of steam generator tube. After extracting the features from the eddy current testing (ECT) signals, multi-layer neural networks are used to predict the defect size. In order to maximize the prediction performance for the defect size, we should carefully choose the structure of neural networks, especially the number of neurons in the hidden layer. In this paper, it is shown that, for the prediction of defect size, the number of neurons in the hidden layer can be efficiently determined by using cross-validation.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.257-266
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• 2014

The semiconductor manufacturing industry is managed by a number of parameters from the FAB which is the initial step of production to package test which is the final step of production. Various methods for prediction for the quality and yield are required to reduce the production costs caused by a complicated manufacturing process. In order to increase the accuracy of quality prediction, we have to extract the significant features from the large amount of data. In this study, we propose the method for extracting feature from the cell level data of probe test process using OPTICS which is one of the density-based clustering to improve the prediction accuracy of the quality of the assembled chips that will be placed in a package test. Two features extracted by using OPTICS are used as input variables of quality prediction model because of having position information of the cell defect. The package test progress for chips classified to the correct quality grade by performing the improved prediction method is expected to bring the effect of reducing production costs.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.10a
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• pp.169-172
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• 2009

Multiple scale modeling has been applied to predict defect shape change during the wire rod rolling process. The size difference between bloom and defect prevent using usual FEM approaches due to the enormous number of elements required to depict the defect. The newly developed multiple scale model can visualize defect shape changes during the multi stands rolling process. The defect positioned at the top and side of bloom are smoothed out but the one at the middle evolved as folding or remained as crack. This approach can be used for defect control with roll shape design and initial bloom shape.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.10
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• pp.1492-1499
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• 2004

In this paper, defect formation in ring rolling is revealed by computer simulation of ring rolling processes. The rigid-plastic finite element method is employed for this study. An analysis model having relatively fine mesh system near the roll gap is used for reducing the computational time and a scheme of minimizing the volume change is applied. The formation of the central cavity formation defect in ring rolling of a taper roller bearing outer race and the polygonal shape defect in ring rolling of a ball bearing outer race has been simulated. It has been seen that the results are qualitatively good with actual phenomena.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.2
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• pp.170-176
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• 2012

In the nuclear industry, wall thinning defect of straight pipe occur the enormous loss in life evaluation and safety evaluation. To use non-destructive technique, we measure deformation, vibration, defect evaluation. But, this techniques are a weak that is the measurement of the wide area is difficult and the time is caught long. In the secondary side of nuclear power plants mostly used steel pipe, artificiality wall thinning defect make in the side and different thickness make to the each other, wall thinning defect part of deformation measure by using shearography. In addition, optical measurement through deformation, vibration, defect evaluation evaluate pipe and thickness defects of pressure vessel is to evaluate quantitatively. By shearography interferometry to measure the pipe's internal wall thinning defect and the variation of pressure use the proposed technique, the quantitative defect is to evaluate the thickness of the surplus. The amount of deformation use thickness of surplus prediction of the actual thickness defect and approximately 7 percent error by ensure reliability. According to pressure the amount of deformation and the thickness of the surplus through DB construction, nuclear power plant pipe use wall thinning part soundness evaluation. In this study, pressure vessel of thickness defect measure proposed nuclear pipe of wall thinning defect prediction and integrity assessment technology development. As a basic research defected theory and experiment, pressure vessel of advanced stability and soundness and maintainability is expected to contribute foundation establishment.

• Proceedings of the KWS Conference
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• 2000.10a
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• pp.274-276
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• 2000

• Proceedings of the Korean Magnestics Society Conference
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• 1997.11a
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• pp.124-125
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• 1997

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.73-81
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• 2015

Most of the software fault prediction studies focused on the binary classification model that predicts whether an input entity has faults or not. However the ability to predict entity fault-proneness in various severity categories is more useful because not all faults have the same severity. In this paper, we propose fault prediction models at different severity levels of faults using traditional size and complexity metrics. They are ternary classification models and use four machine learning algorithms for their training. Empirical analysis is performed using two NASA public data sets and a performance measure, accuracy. The evaluation results show that backpropagation neural network model outperforms other models on both data sets, with about 81% and 88% in terms of accuracy score respectively.