Software Defect Model Based on Similarity and Association Rule

Wan Jiang Han,He Yang Jiang,Tian Bo Lu,Xiao Yan Zhang,Weijian Li 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.7

In order to detect defects efficiently and improve the quality of products, this paper puts forward the concept about defect classification model and defect association model by a lot of defect data. The technology of similarity is applied to defect classification model, and the idea of Knowledge Discovery in Database is applied to defect association model. Defect classification model can analyze the defect efficiently and provides the basis of solving problems quickly while defect association model can be used to detect early and prevent problem, which can make effective improvements for testing and development. This paper summed up GUI defect model based on a large number of interface defects. The model is useful to improve the accuracy of forecast and be used for test planning and implementation through the practice of several projects.

Pattern Classification for Small-Sized Defects Using Multi-Head CNN in Semiconductor Manufacturing

Yunseon Byun,Jun-Geol Baek 한국정밀공학회 2021 International Journal of Precision Engineering and Vol.22 No.10

To improve the quality of semiconductor manufacturing, defects need to be detected and their root causes controlled. Because the root causes can vary depending on defect patterns, classifying the patterns accurately is important. Several recent studies have investigated automatic defect classification using a convolutional neural network (CNN) with wafer map images. CNNs are excellent tools for classifying images of different shapes and sizes. However, the detection of small-sized defects that have small clusters and linear patterns is difficult. Therefore, this study focuses on patterns that are difficult to detect. We propose three steps for pattern classification. First, modified median filtering is used to preserve the original shapes of patterns. Second, a rotated defects (RoD) transform is performed by applying the rotational properties of wafer maps. The RoD transform augments the defect proportion and improves the detection of small-sized defects. Third, a multi-head CNN is used to extract and combine the features from the original and transformed maps. The combined features are then used to classify the defect patterns. Overall classification performance of defects can be improved by accurately classifying small clusters and linear patterns. The proposed model was evaluated using WM-811K wafer maps, and small-sized defects were accurately classified. Such an accurate defect classification model will enable effective root cause analysis and quality improvement in semiconductor manufacturing.

명암도 분포 및 형태 분석을 이용한 효과적인 TFT-LCD 필름 결함 영상 분류 기법

노충호(Chung-Ho Noh),이석룡(Seok-Lyong Lee),조문신(Moon-Shin Zo) 한국멀티미디어학회 2010 멀티미디어학회논문지 Vol.13 No.8

TFT-LCD 생산 과정에서 발생하는 결함을 정확하게 분류하여 결함 유형에 따라 폐기, 사용가능 등의 의사결정을 적절하게 내리는 것은 수율 증가 및 생산성 향상에 필수적인 요소이다. 본 논문에서는 TFT-LCD 생산 라인에서 획득한 결함 영상에 대하여 명암도 분포(intensity distribution) 및 결함 영상의 형태 특징(shape feature)을 분석하여 효과적으로 필름 결함 유형을 분류하는 기법을 제시한다. 본 연구에서는 먼저 필름 결함 영상을 결함 영역과 결함이 아닌 배경 영역으로 이진화하고, 결함 영역에서 결함의 선형성(linearity), 명암도 분포를 고려한 형태 특징 등의 여러 가지 특징을 분석하여 기준 영상(referential image) 데이터베이스를 구축하였으며, 분류하고자 하는 결함 영상과 데이터베이스에 저장된 기준 영상과의 매칭 비용 함수(matching cost function)를 정의하여 적절히 매칭시킴으로써 결함의 유형을 결정하였다. 제시한 기법의 성능을 검증하기 위하여 실제 TFT-LCD 생산 라인에서 획득한 결함 영상들을 대상으로 분류 실험을 수행하였으며, 실험 결과 생산 라인에서 이용할 수 있을 정도의 상당한 수준의 분류 정확도를 달성하였음을 보여주었다. In order to increase the productivity in manufacturing TFT-LCD(thin film transistor-liquid crystal display), it is essential to classify defects that occur during the production and make an appropriate decision on whether the product with defects is scrapped or not. The decision mainly depends on classifying the defects accurately. In this paper, we present an effective classification method for film defects acquired in the panel production line by analyzing the intensity distribution and shape feature of the defects. We first generate a binary image for each defect by separating defect regions from background (non-defect) regions. Then, we extract various features from the defect regions such as the linearity of the defect, the intensity distribution, and the shape characteristics considering intensity. and construct a referential image database that stores those feature values. Finally, we determine the type of a defect by matching a defect image with a referential image in the database through the matching cost function between the two images. To verify the effectiveness of our method, we conducted a classification experiment using defect images acquired from real TFT-LCD production lines. Experimental results show that our method has achieved highly effective classification enough to be used in the production line.

Utility of Radiographs, Computed Tomography, and Three Dimensional Computed Tomography Pelvis Reconstruction for Identification of Acetabular Defects in Residency Training

( Johannes F. Plate ),( John S. Shields ),( Maxwell K. Langfitt ),( Michael P. Bolognesi ),( Jason E. Lang ),( Thorsten M. Seyler ) 대한고관절학회 2017 Hip and Pelvis Vol.29 No.4

Purpose: The Paprosky classification system of acetabular defects is complex and its reliability has been questioned. The purpose of this study was to evaluate the effectiveness of different radiologic imaging modalities in classifying acetabular defects in revision total hip arthroplasty (THA) and their value of at different levels of training. Materials and Methods: Bone defects in 8 revision THAs were classified by 2 fellowship-trained adult reconstruction surgeons. A timed presentation with representative images for each case (X-ray, two-dimensional computed tomography [CT] and three-dimensional [3D] reconstructions) was shown to 35 residents from the first postgraduate year of training year of training (PGY-1 to PGY-5), 2 adult reconstruction fellows and 2 attending orthopaedic surgeons. The Paprosky classification of bone defects was recorded. The influence of image modality and level of training on classification were analyzed using chi-square analysis (alpha=0.05). Results: Overall correct classification was 30%. The level of training had no influence on correct classification (P=0.531). Using X-ray led to 37% correctly identified defects, CT scans to 33% and 3D reconstructions to 20% of correct answers (P<0.001). There was no difference in correct classification based defect type (P<0.001). Regardless of level of training or imaging, 64% of observers recognized type 1 defects, compared to only 16% correct recognition of type 3B defects. Conclusion: Using plain X-rays led to an increased number of correct classification, while regular CT scan and 3D CT reconstructions did not improve accuracy. The classification system of acetabular defects can be used for treatment decisions; however, advanced imaging may not improve its utilization.

공동주택 하자분류체계 기반 하자위험 평가

장호면(Ho-Myun Jang) 한국산학기술학회 2018 한국산학기술학회논문지 Vol.19 No.3

공동주택 하자는 유지보수에 막대한 비용이 들어가게 되며, 발주자, 시공자 그리고 입주자 등에게 심각한 피해를 입힌다. 이에 따라 하자분쟁을 최소화하고 철저한 품질관리를 통한 체계적이고 효율적인 하자관리를 위한 토대를 마련할 필요가 있다. 본 연구에서는 하자분쟁사례를 활용하여 공동주택의 공종/부위/현상에 따른 하자분류체계를 도출하고, 이를 기반으로 하자유형별 하자위험을 평가할 수 있는 방안을 제시하였다. 이를 위하여 본 논문에서는 경과년수 10년 이상 공동주택 하자분쟁사례 34건, 약 6000여개의 하자항목 자료를 토대로 분석을 실시하였다. 분석 결과를 정리하면, 하자분류체계는 하자공종, 하자부위 및 하자현상으로 크게 분류한 후 세부적으로 총 157개 항목으로 세분화하였다. 하자분류체계를 토대로 하자빈도, 하자비용 및 하자위험을 분석한 결과, RC공사 및 마감공사에 하자위험이 상당히 집중되어 있는 것으로 확인되었다. 이에 따라 이러한 하자위험에 대한 하자예방 활동이 우선적으로 고려되어야 할 것으로 판단된다. 본 연구를 토대로 하자위험을 관리할 수 있는 방안에 대한 추가적인 연구가 필요할 것으로 판단된다. 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.

공동주택 사용검사 전 하자 특성 분석

이상효(Sang-Hyo Lee),한만천(Man-Cheon Han),김재준(Jae-Jun Kim),이정석(Jeong-Seok Lee) 한국산학기술학회 2020 한국산학기술학회논문지 Vol.21 No.5

본 논문에서는 사용검사 전 하자로 분류되는 미시공과 변경시공을 대상으로 하자분류체계를 설정하여 세부적인 사용검사 전 하자발생 패턴과 특징을 도출하는 것을 목적으로 한다. 이를 위하여 본 논문에서는 실제로 하자 분쟁이 발생한 공동주택 사례 133건, 약 3,110건의 사용검사 전 하자항목을 활용하여 분석을 실시하였다. 주요 분석결과를 살펴보면, 첫째, 하자중요도 관점에서 마감공사에서 발생하는 사용검사 전 하자들이 상대적으로 높은 순위를 차지하였다. 둘째, 일반적으로 외벽 균열 등과 같은 하자도 매우 중요한 하자로 인식되고 있으나, 사용검사 전 하자들의 경우, 외벽에는 상대적으로 나타나지 않는 것으로 확인되었다. 셋째, 방수공사의 경우, 기전실이나 주차장 등에서 사용검사 전 하자가 주로 발생하는 것으로 확인되었다. 또한 하자중요도 상위 20위 내에 포함되는 하자에 대한 세부 내역을 확인한 결과, 분쟁 소송 과정에서 다양한 쟁점이 발생할 수 있음에 따라 이해관계자 간 이견을 합리적으로 해결할 수 있는 기준이나 대응책 마련이 필요하다. 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.

Metal Surface Defect Detection and Classification using EfficientNetV2 and YOLOv5

ALIBEK RUSTAMOVICH ESANOV,김강철 한국전자통신학회 2022 한국전자통신학회 논문지 Vol.17 No.4

Detection and classification of steel surface defects are critical for product quality control in the steel industry. However, due to its low accuracy and slow speed, the traditional approach cannot be effectively used in a production line. The current, widely used algorithm (based on deeplearning) has an accuracy problem, and there are still rooms for development. This paper proposes a method of steel surface defect detection combining EfficientNetV2 for image classification and YOLOv5 as an object detector. Shorter training time and high accuracy are advantages of this model. Firstly, the image input into EfficientNetV2 model classifies defect classes and predicts probability of having defects. If the probability of having a defect is less than 0.25, the algorithm directly recognizes that the sample has no defects. Otherwise, the samples are further input into YOLOv5 to accomplish the defect detection process on the metal surface. Experiments show that proposed model has good performance on the NEU dataset with an accuracy of 98.3%. Simultaneously, the average training speed is shorter than other models.

웨이블렛변환과 서포트벡터머신을 이용한 저대비·불균일· 무특징 표면 결함 분류에 관한 연구

김성주,김경범 한국반도체디스플레이기술학회 2020 반도체디스플레이기술학회지 Vol.19 No.3

In this paper, a method for improving the defect classification performance in steel plate surface has been studied, based on DWT(discrete wavelet transform) and SVM(support vector machine). Surface images of the steel plate have low contrast, uneven, and featureless, so that the contrast between defect and defect-free regions is not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. In order to improve the characteristics of these images, a synthetic images based on discrete wavelet transform are modeled. Using the synthetic images, edge-based features are extracted and also geometrical features are computed. SVM was configured in order to classify defect images using extracted features. As results of the experiment, the support vector machine based classifier showed good classification performance of 94.3%. The proposed classifier is expected to contribute to the key element of inspection process in smart factory.

병렬 구조의 다중 필터 CNN을 이용한 골절합용 판의 불량 탐지 모델에 관한 연구

이송연,허용정 한국정밀공학회 2023 한국정밀공학회지 Vol.40 No.9

Bone plates are a medical device used for fixing broken bones, which should not have a crack and hole defect. Defect detection is very important because bone plate defect is very dangerous. In this study, we proposed a defect detection model based on a parallel type convolution neural network for detecting bone plate crack and pore deformation. All size filters were different according to the defect shape. A convolution neural network detected pore defects. Another convolution neural network detected the crack. Two convolution neural networks simultaneously detected different defect types. The performance of the defect detection model was measured and used for the F1-score. We confirmed that performance of the defect detection model was 98.4%. We confirmed that the defect detection time was 0.21 seconds.

SVM Based Defect Classification of Electronic Board Using Bag of Keypoints

Hidenobu Inoue,Yuji Iwahori,Boonserm Kijsirikul,M. K. Bhuyan 대한전자공학회 2015 ITC-CSCC :International Technical Conference on Ci Vol.2015 No.6

This paper proposes a new approach for the defect classification of electronic board using Bag of Keypoints and SVM. The main purpose of this paper is not to use the reference image which can be used to extract the difference region of defect. The approach represents histogram features of Bag of keypoints based on extracting features from data set images. Feature vectors are used for SVM learning and classification. The effectiveness of the approach is evaluated with accuracy of defect classification for images with actual defects in comparison with the previously proposed approaches.