• Korean Journal of Radiology
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• v.19 no.6
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• pp.1077-1088
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• 2018

Objective: To correlate CT parameters on detector-based dual-energy CT enterography (DECTE) with Crohn's disease activity index (CDAI) and externally validate quantitative CT parameters. Materials and Methods: Thirty-nine patients with CD were retrospectively enrolled. Two radiologists reviewed DECTE images by consensus for qualitative and quantitative CT features. CT attenuation and iodine concentration for the diseased bowel were also measured. Univariate statistical tests were used to evaluate whether there was a significant difference in CTE features between remission and active groups, on the basis of the CDAI score. Pearson's correlation test and multiple linear regression analyses were used to assess the correlation between quantitative CT parameters and CDAI. For external validation, an additional 33 consecutive patients were recruited. The correlation and concordance rate were calculated between real and estimated CDAI. Results: There were significant differences between remission and active groups in the bowel enhancement pattern, subjective degree of enhancement, mesenteric fat infiltration, comb sign, and obstruction (p < 0.05). Significant correlations were found between CDAI and quantitative CT parameters, including number of lesions (correlation coefficient, r = 0.573), bowel wall thickness (r = 0.477), iodine concentration (r = 0.744), and relative degree of enhancement (r = 0.541; p < 0.05). Iodine concentration remained the sole independent variable associated with CDAI in multivariate analysis (p = 0.001). The linear regression equation for CDAI (y) and iodine concentration (x) was y = 53.549x + 55.111. For validation patients, a significant correlation (r = 0.925; p < 0.001) and high concordance rate (87.9%, 29/33) were observed between real and estimated CDAIs. Conclusion: Iodine concentration, measured on detector-based DECTE, represents a convenient and reproducible biomarker to monitor disease activity in CD.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.547-555
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• 2019

The purpose of this study was to evaluate the usefulness of routine liver ultrasonography on the basis of the scoring system according to the morphological parameters of liver ultrasound images and the histopathological results of liver biopsy. The morphological parameters of the liver through ultrasonography were divided into liver surface, liver edge and liver parenchyma. Pathologic results of liver biopsy were classified as mild fibrosis(F1), significant fibrosis(F2), severe fibrosis(F3), and cirrhosis(F4). In conclusion, routine ultrasound examination showed a sensitive predictive factor for fibrosis with mild fibrosis (F1) to severe fibrosis (F3) were liver edge>liver parenchyma>liver surface. However, the predictive factors for detecting cirrhosis (F4) were liver parenchyma>liver surface>liver edge. The use of three variable combinations rather than individual variables in routine ultrasonography may be useful in evaluating the degree and progress of liver fibrosis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.127-132
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• 2019

In this paper, we propose a visual inspection method of automotive parts for injection molding to improve the appearance quality and productivity of automotive parts. Exterior inspection of existing injection molding automobile parts was generally done by manual sampling inspection by human. First, we applied the edge-tolerance vision inspection algorithm ([1] - [4]) for vision inspection of electronic components (TFT-LCD and PCB) And we propose a new visual inspection method to overcome the problem. In the proposed visual inspection, the inspection images of the parts to be inspected are aligned on the basis of the reference image of good quality. Then, after partial adaptive binarization, the binary block matching algorithm is used to compare the good binary image and the test binary image. We verified the effectiveness of the edge-tolerance vision check algorithm and the proposed appearance vision test method through various comparative experiments using actual developed equipment.

• The korean journal of orthodontics
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• v.51 no.3
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• pp.145-156
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• 2021

Objective: The aim of this retrospective study was to assess the midfacial soft tissue changes following maxillary expansion using micro-implant-supported maxillary skeletal expanders (MSEs) in young adults by cone-beam computerized tomography (CBCT) and to evaluate the correlations between hard and soft tissue changes after MSE usage. Methods: Twenty patients (mean age, 22.4 years; range, 17.6-27.1) with maxillary transverse deficiency treated with MSEs were selected. Mean expansion amount was 6.5 mm. CBCT images taken before and after expansion were superimposed to measure the changes in soft and hard tissue landmarks. Statistical analyses were performed using paired t-test and Pearson's correlation analysis on the basis of the normality of data. Results: Average lateral movement of the cheek points was 1.35 mm (right) and 1.08 mm (left), and that of the alar curvature points was 1.03 mm (right) and 1.02 mm (left). Average forward displacement of the cheek points was 0.59 mm (right) and 0.44 mm (left), and that of the alar curvature points was 0.61 mm (right) and 0.77 mm (left) (p < 0.05). Anterior nasal spine (ANS), posterior nasal spine (PNS), and alveolar bone width showed significant increments (p < 0.05). Changes in the cheek and alar curvature points on both sides significantly correlated with hard tissue changes (p < 0.05). Conclusions: Maxillary expansion using MSEs resulted in significant lateral and forward movements of the soft tissues of cheek and alar curvature points on both sides in young adults and correlated with the maxillary suture opening at the ANS and PNS.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.684-694
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• 2021

Resting-state Functional Magnetic Resonance Imaging(fMRI) data detects the temporal correlations in Blood Oxygen Level Dependent(BOLD) signal and these temporal correlations are regarded to reflect intrinsic cortical connectivity, which is deactivated during attention demanding, non-self referential tasks, called Default Mode Network(DMN). The relationship between fMRI and anatomical connectivity has not been studied in detail, however, the preceded studies have tried to clarify this relationship using Diffusion Tensor Imaging(DTI) and fMRI. These studies use method that fMRI data assists DTI data or vice versa and it is used as guider to perform DTI tractography on the brain image. In this study, we hypothesized that functional connectivity in resting state would reflect anatomical connectivity of DMN and the combined images include information of fMRI and DTI showed visible connection between brain regions related in DMN. In the previous study, functional connectivity was determined by subjective region of interest method. However, in this study, functional connectivity was determined by objective and advanced method through Independent Component Analysis. There was a stronger connection between Posterior Congulate Cortex(PCC) and PHG(Parahippocampa Gyrus) than Anterior Cingulate Cortex(ACC) and PCC. This technique might be used in several clinical field and will be the basis for future studies related to aging and the brain diseases, which are needed to be translated not only functional connectivity, but structural connectivity.

• Design & Manufacturing
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• v.14 no.4
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• pp.71-77
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• 2020

Various manufacturing technologies, including over-molding and insert-injection molding, are used to produce hybrid plastics and metals. However, there are disadvantages to these technologies, as they require several steps in manufacturing and are limited to what can be reasonably achieved within the complexities of part geometry. This study aims to determine a practical approach for producing metal/plastic hybrid components by combining plastic injection molding and metal die casting to create a new hybrid metal/plastic molding process. The integrated metal/plastic hybrid injection molding process developed in this study uses the proven method of multi-component technology as a basis to combine plastic injection molding with metal die casting into one integrated process. In this study, the electrical conductivity and ampacity were verified to qualify the new process for the production of parts used in electronic devices. The electrical conductivity was measured, contacting both sides of the test sample with constant pressure, and the resistivity was measured using a micro ohmmeter. Also, the specific conductivity was subsequently calculated from the resistivity and contact surface of the conductor path. The ampacity defines the maximum amount of current a conductive path can carry before sustaining immediate or progressive deterioration. The manufactured hybrid multi-components were loaded with increasing currents, while the temperature was recorded with an infrared camera. To compare the measured infrared images, an electro-thermal simulation was conducted using commercial CAE software to predict the maximum temperature of the power loaded parts. Overall, during the injection molding process, it was demonstrated that multifunctional parts can be produced for electric and electronic applications.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.1-9
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• 2021

Deep learning in computer vision has made accelerated improvement over a short period but large-scale learning data and computing power are still essential that required time-consuming trial and error tasks are involved to derive an optimal network model. In this study, we propose a similar image classification performance improvement method based on CR (Confusion Rate) that considers only the characteristics of the data itself regardless of network optimization or data reinforcement. The proposed method is a technique that improves the performance of the deep learning model by calculating the CRs for images in a dataset with similar characteristics and reflecting it in the weight of the Loss Function. Also, the CR-based recognition method is advantageous for image identification with high similarity because it enables image recognition in consideration of similarity between classes. As a result of applying the proposed method to the Resnet18 model, it showed a performance improvement of 0.22% in HanDB and 3.38% in Animal-10N. The proposed method is expected to be the basis for artificial intelligence research using noisy labeled data accompanying large-scale learning data.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.61-71
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• 2022

Pool boiling heat transfer is widely applied in nuclear engineering fields. The influence of heating surface orientation on the pool boiling heat transfer has received extensive attention. In this study, the heating surface with different roughness was adopted to conduct pool boiling experiments at different inclination angles. Based on the boiling curves and bubble images, the effects of inclination angle on the pool boiling heat transfer and critical heat flux were analyzed. When the inclination angle was bigger than 90°, the bubble size increased with the increase of inclination angle. Both the bubble departure frequency and critical heat flux decreased as the inclination angle increased. The existing theoretical models about pool boiling heat transfer and critical heat flux were compared. From the perspective of bubble agitation model and Hot/Dry spot model, the experimental phenomena could be explained reasonably. The enlargement of bubble not only could enhance the agitation of nearby liquid but also would cause the bubble to stay longer on the heating surface. Consequently, the effect of inclination angle on the pool boiling heat transfer was not conspicuous. With the increase of inclination angle, the rewetting of heating surface became much more difficult. It has negative effect on the critical heat flux. This work provides experimental data basis for heat transfer and CHF performance of pool boiling.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.257-261
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• 2022

Diseases of agricultural plants in recent years have spread greatly across the regions of the Kyrgyz Republic and pose a serious threat to the yield of many crops. The consequences of it can greatly affect the food security for an entire country. Due to force majeure, abnormal cases in climatic conditions, the annual incomes of many farmers and agricultural producers can be destroyed locally. Along with this, the rapid detection of plant diseases also remains difficult in many parts of the regions due to the lack of necessary infrastructure. In this case, it is possible to pave the way for the diagnosis of diseases with the help of the latest achievements due to the possibilities of feedback from the farmer - developer in the formation and updating of the database of sick and healthy plants with the help of advances in computer vision, developing on the basis of machine and deep learning. Currently, model training is increasingly used already on publicly available datasets, i.e. it has become popular to build new models already on trained models. The latter is called as transfer training and is developing very quickly. Using a publicly available data set from PlantVillage, which consists of 54,306 or NewPlantVillage with a data volumed with 87,356 images of sick and healthy plant leaves collected under controlled conditions, it is possible to build a deep convolutional neural network to identify 14 types of crops and 26 diseases. At the same time, the trained model can achieve an accuracy of more than 99% on a specially selected test set.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.675-681
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• 2022

Recently, with the development of IoT technology and AI, unmanned and automated in various fields, interest in video processing, which is the basis for automation such as object recognition and object classification, is increasing. Various studies have been conducted on noise removal in the video processing process, which has a significant impact on image quality and system accuracy and reliability, but there is a problem that it is difficult to restore images for areas with high impulse noise density. In this paper proposes a filter algorithm based on partial mask and Lagrange interpolation to restore the damaged area of impulse noise in the image. In the proposed algorithm, the filtering process was switched by comparing the filtering mask with the noise estimate and the purge weight was calculated based on the low frequency component and the high frequency component of the image to restore the image.