• Korean Journal of Radiology
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• v.21 no.9
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• pp.1104-1113
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• 2020

Objective: To assess the regional ventilation in patients with asthma-chronic obstructive pulmonary disease (COPD) overlap syndrome (ACOS) using xenon-ventilation dual-energy CT (DECT), and to compare it to that in patients with COPD. Materials and Methods: Twenty-one patients with ACOS and 46 patients with COPD underwent xenon-ventilation DECT. The ventilation abnormalities were visually determined to be 1) peripheral wedge/diffuse defect, 2) diffuse heterogeneous defect, 3) lobar/segmental/subsegmental defect, and 4) no defect on xenon-ventilation maps. Emphysema index (EI), airway wall thickness (Pi10), and mean ventilation values in the whole lung, peripheral lung, and central lung areas were quantified and compared between the two groups using the Student's t test. Results: Most patients with ACOS showed the peripheral wedge/diffuse defect (n = 14, 66.7%), whereas patients with COPD commonly showed the diffuse heterogeneous defect and lobar/segmental/subsegmental defect (n = 21, 45.7% and n = 20, 43.5%, respectively). The prevalence of ventilation defect patterns showed significant intergroup differences (p < 0.001). The quantified ventilation values in the peripheral lung areas were significantly lower in patients with ACOS than in patients with COPD (p = 0.045). The quantified Pi10 was significantly higher in patients with ACOS than in patients with COPD (p = 0.041); however, EI was not significantly different between the two groups. Conclusion: The ventilation abnormalities on the visual and quantitative assessments of xenon-ventilation DECT differed between patients with ACOS and patients with COPD. Xenon-ventilation DECT may demonstrate the different physiologic changes of pulmonary ventilation in patients with ACOS and COPD.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.687-701
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• 2020

In the production of industrial fabric, it needs automatic real-time system to detect defects on the fabric for assuring the defect-free products flow to the market. At present, many visual-based methods are designed for detecting the fabric defects, but they usually lead to high false alarm. Base on this reason, we propose a Sobel operator combined with patch statistics (SOPS) algorithm for defects detection. First, we describe the defect detection model. mean filter is applied to preprocess the acquired image. Then, Sobel operator (SO) is applied to deal with the defect image, and we can get a coarse binary image. Finally, the binary image can be divided into many patches. For a given patch, a threshold is used to decide whether the patch is defect-free or not. Finally, a new image will be reconstructed, and we did a loop for the reconstructed image to suppress defects noise. Experiments show that the proposed SOPS algorithm is effective.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.307-310
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• 1995

In the conventional systems, a human must have knowledge of machines and of their special language in communicating with machines. In one side, it is desirable for a human but in another side, it is true that achieving it is very elaborate and is also a significant cause of human error. To reduce this sort of human load, an intelligent man-machine interface is desirable to exist between a human operator and machines to be operated. In the ordinary human communication, not only linguistic information but also visual information is effective, compensating for each others defect. From this viewpoint, problem of translating verbal expressions to some visual image is discussed here in this paper. The location relation between any two objects in a visual scene is a key in translating verbal information to visual information, as is the case in Fig.l. The present translation system advances in knowledge with experience. It consists of Japanese Language processing, image processing, and Japanese-scene translation functions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4763-4775
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• 2020

This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.449-456
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• 2009

Image processing has been applied to steel bridge defect recognition since 1990s. Compare to human visual inspection, image processing provides a more objective and accurate way of assessment. Since shade and shadow may sometimes occur when taking bridge coating images, non-uniform illumination problems should be considered. By means of color image processing, this paper aims to mitigate the illumination effect for bridge coating assessment. Furthermore, the adaptable ellipse method (AEM) is proposed to recognize mild rust colors. Finally, AEM will be compared to the K-Means algorithm, a popular recognition method, to show its advantage.

• Archives of Plastic Surgery
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• v.34 no.5
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• pp.663-666
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• 2007

Purpose: Autologous fat injection into the facial area is a frequently used technique in aesthetic plastic surgery for augmentation of the soft tissue. Fat injection is a very safe procedure because of the advantage of being autologous tissue. Minimal foreign body reaction or infections are noted after fat injection. However, there may be some complications including those as severe as blindness. There have been some case reports on visual disturbances after autologous fat injection reported in the literature. Methods: A 21-year-old female patient underwent autologous fat injection into left eyebrow area to correct depression of soft tissue. Immediately after injection of autologous fat, she complained sudden visual loss on the left eye. She had come to our emergency room and ophthalmologic evaluation showed that the patient could only recognize hand motion. There was no abnormality of the optic nerve on magnetic resonance imaging. Suspecting an ischemic optic neuritis from fat embolism of the central retinal artery, the patient was treated conservatively with occular massage, antiglaucomatic agent, anti-inflammatory drugs and antibiotics. Visual field examination showed visual defect of half the lower hemisphere. Results: While maintaining antiglaucomatic agents and non steroidal anti inflammatory drugs, fundoscopic examination showed no abnormalities on the second day of admission. Visual field examination showed an improvement on the fourth day along with decreased eyeball pain. Significant improvement of vision was noted and the patient was discharged on the fifth day of admission. The patient was followed-up 2 days afterwards with improved vision and visual field defect. Conclusion: We describe an unusual case of sudden unilateral visual disturbance following autologous fat injection into periorbital area.

• Annals of Clinical Neurophysiology
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• v.22 no.1
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• pp.48-49
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• 2020

• Journal of Korean Neurosurgical Society
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• v.57 no.1
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• pp.23-31
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• 2015

Objective : The aims of this study were to investigate the quantitative relationship between pituitary macroadenoma size and degree of visual impairment, and assess visual improvement after surgical resection of the tumor. Methods : The medical records of patients with pituitary adenoma, who had undergone trans-sphenoidal adenectomy between January 2009 and January 2011, were reviewed. Patients underwent an ocular examination and brain MRI before and after surgery. The visual impairment score (VIS) was derived by combining the scores of best-corrected visual acuity and visual field. The relationship between VIS and tumor size/tumor type/position of the optic chiasm was assessed. Results : Seventy-eight patients were included (41 male, 37 female). Thirty-two (41%) patients experienced blurred vision or visual field defect as an initial symptom. Receiver operating characteristic curve analysis showed that tumors <2.2 cm tended to cause minimal or no visual impairment. Statistical analysis showed that 1) poor preoperative vision is related to tumor size, displacement of the optic chiasm in the sagittal view on MRI and optic atrophy, and 2) poorer visual prognosis is associated with greater preoperative VIS. In multivariate analysis the only factor significantly related to VIS improvement was increasing pituitary adenoma size, which predicted decreased improvement. Conclusion : Results from this study show that pituitary adenomas larger than 2 cm cause defects in vision while adenomas 2 cm or smaller do not cause significant visual impairment. Patients with a large macroadenoma or giant adenoma should undergo surgical resection as soon as possible to prevent permanent visual loss.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.6
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• pp.1157-1162
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• 2001

This paper deals with inspect algorithm using visual system. One of the major problems that arise during polymer production is the estimation of the noise of the color product.(bad pallets) An erroneous output can cause a lot of losses (production and financial losses). Therefore new methods for real-time inspection of the noise are demanded. For this reason, we have presented a development of vision system algorithm for the defect inspection of PE color pallets. First of all, in order to detect the edge of object, the differential filter is used. And we apply to the labelling algorithm for feature extraction. This algorithm is designed for the defect inspection of pallets. The labelling algorithm permits to separate all of the connected components appearing on the pallets. Labelling the connected regions of a image is a fundamental computation in image analysis and machine vision, with a large number of application. Also, we suggested vision processing program in window environment. Simulations and experimental results demonstrate the performance of the proposal algorithm.