• Journal of Information Technology Services
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• v.12 no.2
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• pp.361-371
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• 2013

Public domain defect data sets, such as NASA data sets which are available from the NASA MDP and PROMISE repositories, make it possible to compare the results of different defect prediction models by using the same data sets. This means that repeatable and general prediction models can be built. However, some recent studies have raised questions about the quality of two versions of NASA data set, and made new cleaned data sets by applying their data cleaning processes. We find that there are two ways in the NASA MDP versions to determine the defectiveness of a module, 0 or 1, and the two results are different in some cases. This serious problem, to our knowledge, has not been addressed in previous studies. To handle this ambiguity problem, we define two kinds of module defectiveness and two conditions that can be used to determine the ambiguous cases. We meticulously analyze 5 projects among the 13 NASA projects by using our ambiguity analysis method. The results show that JM1 and PC4 are the best projects with few ambiguous cases.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.11a
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• pp.88-89
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• 2017

Curling is caused by the shrinkage difference between surface and bottom side of concrete, and the cracks can be occurred by vehicle load after curling. It is important to investigate and predict the curling behavior to minimize the quality defect of concrete due to the curling. Therefore, the experimental and analytical investigation was carried out.

• Smart Structures and Systems
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• v.13 no.6
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.168-173
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• 1999

It is investigated that internal defect of planetary gear which consists of two gears with different number of teeth on both side. The internal defect, central burst, begin to form at the place of adiabatic shear band which usually has maximum ductile fracture value during the forming operation, forward and backward extrusion. It makes the plastic forming of planetary gear difficult. The prediction of defect to minimize the cost to produce the planetary gear. The finite element simulation code DEFORM is applied to analyze the defects. In the analysis, the toothed gears are assumed as axisymmetric cylinders whose diameters are equal to those of pitch circles of the each gears. Experiments were carried out with the SCM415 alloy steel as billet material and AIDA 630-ton knuckle-joint press. The calculated results and experimental inspections are compared to design a die and blank without defects and the results are useful to predict the internal defect.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.290-291
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• 2021

Defects in the coating process of plastic automotive components are caused by various causes and phenomena. The correlation between defect occurrence rate and meteorological and environmental conditions such as temperature, humidity, and fine dust was analyzed. The defect rate data categorized by type and cause was collected for a year from a automotive parts coating company. This data and its correlation with environmental condition was acquired and experimented by machine learning techniques to predict the defect rate at a certain environmental condition. Correspondingly, the model predicted 98% from fine dust and 90% from curtaining (runs, sags) and hence proved its reliability.

• Journal of the Korea Institute of Building Construction
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• v.11 no.5
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• pp.459-467
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• 2011

The work of constructing apartment housing involves various fields of industry that are linked to each other, and is based on a design document prepared by multiple technicians and architects. Consequently, design errors, material flaws or faulty construction works can cause defects, which sometimes overlap with each other. Construction companies should repair any defects found in a completed building within a specified period of time, and to do this, should establish a business plan by efficiently predicting the cost of defect repair. As it is very difficult for companies to accurately predict the occurrence of defects, historical performance data is used as a base. For domestic apartment housing units, data on the cost of defect repair is insufficient, so there are hardly any methods that can be used to make precise predictions. Therefore, the intent of this study is to develop a model that can predict the cost of defect repair by supply type and area, based on historical performance data with ten years worth of post-completion.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.139-147
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• 2023

The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.3
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• pp.131-142
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• 2001

A wrinkling is the instability phenomenon influenced by material properties, shape geometry, forming conditions, stress state, etc. The wrinkling is considered as a critical defect in appearance of product. Many wrinkling prediction methods using thickness strain distribution and farming analysis have been proposed. The wrinkling, however, is not easily predicted precisely by these methods. In this study, the region in the biaxial plane stress state is modeled with a rectangular plate introducing the effective dimension, and critical stress values for the wrinkling are calculated. Prediction index for the wrinkling is then evaluated by normalizing the actual stress with respect to the critical stress. In order to show the validity and efficiency of the method proposed, the wrinkling prediction for a squared sheet in the uniaxial tensile stress and auto-body front finder panel is performed.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.1
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• pp.13-19
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• 2010

In this paper, we study a new estimation system for the prediction of steam generator tube defects. In the previous research works, defect size estimators were independently designed for each defect types in order to estimate the defect size. As a result, the structure of estimation system is rather complex and the estimation performance gets worse if the classification performance is degraded for some reason. This paper studies a new estimation system that does not require the classification of defect types. Although the previous works are expected to achieve much better estimation performance than the proposed system since it uses the estimator specialized in each defect, the performance difference is not so large. Therefore, it is expected that the proposed estimator can be effectively used for the case where the defect type classification is imperfect.

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.6
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• pp.12-19
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• 2003

This paper presents the effect of boundary conditions in various failure pressure models published for the estimation of failure pressure. Furthermore, this approach is extended to the failure prediction with the aid of a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with each corrosion defect in buried pipelines for long exposure period with unit of years. A failure probability model based on the von-Mises failure criterion is adapted. The log-normal and standard normal probability functions for varying random variables are adapted. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically investigated for the corrosion pipeline by using an adapted failure probability model and varying failure pressure model.