• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.179-186
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• 1997

In this paper we suggest a development of surface defect inspection algorithms for cold mill strip. The defects which exist in a surface of cold mill strip have a scattering or singular distribution. This paper consists of preprocessing, feature extraction and defect classification. By preprocessing, the binarized defect image is achieved. In this procedure, Top-hit transform, adaptive thresholding, thinning and noise rejection are used. Especially, Top-hit transform using local min/max operation diminishes the effect of bad lighting. In feature extraction, geometric, moment and co-occurrence matrix features are calculated. For the defect classification, multilayer neural network is used. The proposed algorithm showed 15% error rate.

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

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.1-6
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• 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 Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.171-176
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• 2013

X-ray cross-sectional image-based inspection technique is one of the most useful methods to inspect the brazing joints of heat exchanger. Through X-ray cross-sectional image acquisition, image processing, and defect inspection, the defects of brazing joints can be detected. This paper presents a method to judge the type of detected defects automatically, and to display them three-dimensionally. The defect type is classified as unconnected defect, void, and so on, based on location, size, and shape information of defect. Three-dimensional display which is realized using OpenGL (Open Graphics Library) will be helpful to understand the overall situation including location, size, shape of the defects in a test object.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.633-641
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• 2016

Automatic defect inspection system is composed of the step in the pre-processing, defect candidate detection, and classification. Polarizing films containing various defects should be minimized over-detection for classifying defect blobs. In this paper, we propose a defect detection algorithm using a skewness of histogram for minimizing over-detection. In order to detect up defects with similar to background pixel, we are used the characteristics of the local region. And the real defect pixels are distinguished from the noise using the probability density function. Experimental results demonstrated the minimized over-detection by utilizing the artificial images and real polarizing film images.

• Journal of Chest Surgery
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• v.12 no.1
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• pp.50-55
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• 1979

The development of aortic insufficiency radically alters the physical findings which are generally associated with ventricular septal defect that was not hemodynamically significant, and the combination of the two lesions produces a typical clinical picture, that may be serious and life-threatening when it is left untreated. Therefore, the selection of patients, type and timing of surgical treatment is considered to be important. Among 114 cases of ventricular septal defect treated surgically utilizing cardiopulmonary bypass in the Department of Thoracic Surgery, Seoul National University Hospital, 7 cases were associated with aortic insufficiency. 1. Five cases were male, and 2 cases were female. Ages were from 4 years to 24 years, and mean age was 11.9 years. 2. In all cases, ventricular septal defect was closed with Teflon patch. In a case, a aortic valvuloplasty and in another, a aortic valve replacement with Hancock valve 23 mm., 5 months after the closure of ventricular septal defect were done. 3. Four cases were type I ventricular septal defect by Kirklin`s classification, 3 cases were type II ventricular septal defect, and diameters of ventricular septal defect were from 3.5 cm. to 0.7 cm. A PDA. was combined to a type I ventricular septal defect. 4. In 5 cases, herniation of the aortic cusp through the ventricular septal defect and in a case, annulus dilatation on the aortic valve was noted. 5. Two cases with type I ventricular septal defect and severe pulmonary hypertension expired. A re-opened case with type II ventricular septal defect expired. 6. Four cases were alive, and all of them show decrease of pulse pressure and aortic insufficiency.

• The Journal of Korean Academy of Prosthodontics
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• v.55 no.2
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• pp.198-204
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• 2017

Maxillofacial defect comes from congenital defect, trauma and surgical resection. Patients with intraoral defect are commonly related to maxillary defect and they need prosthetic rehabilitation. Functional reconstruction of partially edentulous mandible has many limitations. However, if both condyles are intact, maxillofacial prosthesis using partial denture give competent results. In this case, a patient of 58 year-old male has a defect on palate and left mandibular posterior teeth from gunshot. The maxillary defect of this patient is Class IV according to Aramany classification and the mandibular one is Type V according to Cantor and Curtis classification. For retention of the obturator, remaining teeth are fully utilized and artificial teeth are arranged harmoniously to provide stable occlusion. Mandibular RPD covered limited range of deformed soft tissue derived from mandibular resection surgery. With these treatments, the patient in this case showed improvements in mastication, swallowing and speech.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2532-2537
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• 2009

For defect characterization in steam generator tubes in nuclear power plant, artificial neural network has been extensively used to classify defect types. In this paper, we study the effectiveness of Bagging for improving the performance of neural network for the classification of tube defects. Bagging is a method that combines outputs of many neural networks that were trained separately with different training data set. By varying the number of neurons in the hidden layer, we carry out computer simulations in order to compare the classification performance of bagging neural network and single neural network. From the experiments, we found that the performance of bagging neural network is superior to the average performance of single neural network in most cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1086-1103
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• 2020

The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.27-35
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• 2023

In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.