• Journal of the Korean Crystal Growth and Crystal Technology
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• v.6 no.1
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• pp.1-10
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• 1996

A numerical study was conducted on the effects of the cusp magnetic field in 8" silicon single crystal grwoth by Czochralski method. For a damping effects simulation by magnetic field, low reynolds number ${\kappa} - {\varepsilon}$ model was adopted. Symmetrci cusp magnetic field has a effect of damping streamline crystal, is lowerd with the increasing cusp magnetic field intensity. The uniformity of the oxygen concentration was improved. The asymmetirc cusp magnetic field increased the oxygen concentration however, oxygen concentration distribution in the radial direction was remained uniform. Suitable combination of symmetric and asymmetric cusp magnetic fields could give uniform and low oxygen concentration in the axial direction.tion.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• Investigative Magnetic Resonance Imaging
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• v.18 no.3
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• pp.219-224
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• 2014

Purpose : To determine the incidence of truncated triangle appearance of anterior horn (AH) to body of medial meniscus (MM) and determine its clinical significance. Materials and Methods: IRB approval was obtained, and informed consent waived for this study. The criteria of "pseudoradial tear" was truncated triangle appearance of the tip of AH to body of MM on one or more coronal images with adjacent fluid signal intensity at the blunted tip. Two musculoskeletal radiologists retrospectively evaluated 485 knee MR images independently for the presence and number of sections with "pseudoradial tear" of AH to body of MM using proton density-weighted coronal MR images. Inter-and intraobserver agreement was calculated using kappa coefficients. Medical records were reviewed for arthroscopic correlation. Results: A pseudoradial tear in the AH to body of MM was present in 381 (78.6%) patients. Locations were 112 in AH (29.4%), 143 in AH to body (37.5%), and 126 in body (33.1%). Number of consecutive sections of pseudoradial tear were 1 in 100 (26.2%), 2 in 164 (43.0%), 3 in 94 (24.7%), 4 in 21 (5.5%), and 5 in 2 (0.5%). Interobserver agreement was 0.99 for presence and 0.43 for number of sections of pseudoradial tear. Arthroscopies were performed in 96 patients and none of the pseudoradial tears were proven as true radial tears on arthroscopy. Conclusion: Pseudoradial tears are frequently seen in AH to body of MM on coronal MR images and may be another pitfall that a radiologist needs to be aware of and be able to differentiate from true radial tear.