• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.61-68
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• 2018

In general, defects cause a lot of maintenance costs and serious damage to various stakeholders, such as the owners, contractors or occupants of apartments. For this reason, a systematic and efficient defect management method is needed to minimize defect disputes. This paper derives a defect classification framework and proposes a defect risk assessment model for different types of defects. For this purpose, 6,000 defect items are allocated to the defect classification framework; these items are associated with 34 apartment projects over ten years old. As a result of this analysis, it was confirmed that the defect risks are concentrated in the areas of RC and finishing work. Based on these results, it is necessary to prevent the major risks of defects according to their priority. Based on this research, it is judged that further research to develop a method of managing the risks of defects may be necessary.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.510-519
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• 2019

The aim of this study was to set a defect classification framework and evaluate the defect risks in apartment buildings For this, approximately 15,056 defect items for 133 apartment buildings were examined. As a result of the analysis, the major defect of the RC work was cracks, which were found mainly in public locations. Moreover, the RC work was found to exhibit a high defect risk of water problem and surface appearance, which are highly connected with cracks. Second, the finish work has a high defect risk because it is composed of various work types, and there are many kinds of materials and construction parts involved. Third, the major defects of the waterproof work were incorrect installation and missing tasks, which have high defect risks in the garage. This is because defects that require rework occur mainly in the underground garage. Based on these results, this study proposed countermeasures for defect risk management to be considered in the construction, handover, post-handover, and occupancy phases. These have been set in detail based on the three zones: low frequency high severity (LFHS), low frequency low severity (LFLS), and high frequency low severity (HFLS).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.1-12
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• 2023

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.167-178
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• 2020

The purpose of this paper is to establish a defect classification system for defects before inspection and to derive the pattern and characteristics of defects before inspection by examining about 3,110 defect items for 133 apartment buildings. The study analysis revealed a relatively high rate of defects before inspection that occurred in finishing work. Second, defects occurred such as cracking of external wall, which is a very important defect. However, defects before inspection were relatively rare on the external wall. Finally, defects before inspection occurred during waterproofing in the common area or garage. It is necessary to establish a reasonable basis or countermeasure to resolve differences between stakeholders as various issues may arise in the course of a dispute, as a result of identifying the details of defects within the top 20 of the defectives.

• Information Systems Review
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• v.8 no.1
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• pp.159-179
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• 2006

With the advances in information technologies and the wide acceptance of IT outsourcing practices, subscription-based information & telecommunications services(ITS) become more available. Convergence and intensified industry competition have made it an imperative for the ITS providers to keep their current customers and acquire new customers at the same time. In this study, we developed a framework for effective customer management based on the factors influencing the post-purchase customer choice: stay with the present provider or switch to another one. Specifically, we classified the factors into four categories: Holding factors, Defect factors, Inducement factors, and Hurdle factors depending on the characteristics of the influence and direction of the influence. Based on the classification, we developed a post-purchase customer choice model for the subscription-based ITS providers. Then, we illustrated a possible application of the model in the context of the broadband Internet access service. The model could be used to increase the competitive advantage of service providers through the effective customer management in the subscription-based ITS market.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.249-255
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• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.421-431
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• 2021

Purpose: For improving outgoing quality, this study presents a novel sampling framework based on predictive analytics. Methods: The proposed framework is composed of three steps. The first step is the variable selection. The knowledge-based and data-driven approaches are employed to select important variables. The second step is the model learning. In this step, we consider the supervised classification methods, the anomaly detection methods, and the rule-based methods. The applying model is the third step. This step includes the all processes to be enabled on real-time prediction. Each prediction model classifies a product as a target sample or random sample. Thereafter intensive quality inspections are executed on the specified target samples. Results: The inspection data of three Samsung products (mobile, TV, refrigerator) are used to check functional defects in the product by utilizing the proposed method. The results demonstrate that using target sampling is more effective and efficient than random sampling. Conclusion: The results of this paper show that the proposed method can efficiently detect products that have the possibilities of user's defect in the lot. Additionally our study can guide practitioners on how to easily detect defective products using stratified sampling

• Journal of Dental Rehabilitation and Applied Science
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• v.26 no.1
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• pp.33-38
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• 2010

Maxillectomy is a treatment option for maxillary cancer, which leaves the patient with a palatal defect. It may cause problems with facial deformation, swallowing, mastication, and speech. These functional problems and changes in appearance may result in psychological problems. To control these deficits after maxillectomy, surgical reconstruction or prosthodontic treatment can be chosen as a treatment option. Obturator prosthesis has been used as a preferred method of rehabilitation for most maxillectomy patients. This case is a patient who was classified Aramany classification II hemi-maxillectomy patient with residual teeth from #11-25, whose teeth had substantial labioversion and clinically lengthened from alveolar bone involution, thus making it hard to select proper framework design and resist to the rotational dislodging force of the obturator. Therefore we selected swing-lock attachment design to remain pre-existing crown and bridges and obtain retention and stability of obturator. The swing-lock RPD is economical than the conventional RPD because we can remain pre-existing crown and bridges. And residual teeth which have mobility and poor prognosis can be successfully retained through properly designed swing-lock RPD as it is functioning as a removable splint on the teeth.