• The Journal of Internal Korean Medicine
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• v.42 no.5
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• pp.1001-1008
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• 2021

Objective: The aim of this case study was to describe a case of iris diagnosis of primary hyperhidrosis and the use of Korean medicine. Methods: A patient with symptoms of hyperhidrosis was diagnosed as having Taeeumin after assessment using the four basic Korean diagnostic methods. Iris diagnosis was used for further examination. The images obtained showed a remarkably defined collarette and increased nerve rings, which suggested an overactive sympathetic nerve system. Under the diagnosis of Taeeum, a Korean herbal medicine was prescribed with additional herbs to help alleviate the hyperactivity of the sympathetic nervous system. Results: The patient had been receiving treatment for hyperhidrosis for >30 years, with various medical attempts to relieve her symptoms, which were ineffective. She showed signs of improvement from day 4 into the treatment, and 80% of her symptoms were improved after completing a 6-week treatment course. Conclusion: The herbal medicine prescribed to the patient proved effective for reducing her chronic symptoms that had been unresponsive to previous medical treatments.

• Korean Journal of Optics and Photonics
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• v.34 no.3
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• pp.99-105
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• 2023

The performance of astronomical telescopes can be negatively affected by atmospheric turbulence. To address this issue, techniques for atmospheric turbulence correction have been developed, requiring the simulation of atmospheric turbulence in the laboratory. The most practical way to simulate atmospheric turbulence is to use a phase plate. When measuring a phase plate that simulates strong turbulence, a Shack-Hartmann wave-front sensor is commonly used. However, the laser power decreases as it passes through the phase plate, potentially leading to a weak laser signal at the sensor. This paper investigates the need to control the laser power when measuring a phase plate that simulates strong atmospheric turbulence, and examines the effects of the laser power on the measured wavefront. For phase plates with relatively high Fried parameter r₀, the laser power causes a variation of over 10% in r₀. For phase plates with relatively low r₀, the laser power causes a variation of less than 5%, which means that the influence of the laser power is negligible for phase plates that simulate strong atmospheric turbulence. Based on the system described in this paper, a phase plate simulating strong atmospheric turbulence can be measured at a laser power of 5 mW or higher. Therefore, controlling the laser's output power is necessary when measuring a phase plate for simulating atmospheric turbulence, especially for phase plates with low r₀ values.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.144-159
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• 2014

Techniques for making a single panoramic image using multiple pictures are widely studied in many areas such as computer vision, computer graphics, etc. The panorama image can be applied to various fields like virtual reality, robot vision areas which require wide-angled shots as an useful way to overcome the limitations such as picture-angle, resolutions, and internal informations of an image taken from a single camera. It is so much meaningful in a point that a panoramic image usually provides better immersion feeling than a plain image. Although there are many ways to build a panoramic image, most of them are using the way of extracting feature points and matching points of each images for making a single panoramic image. In addition, those methods use the RANSAC(RANdom SAmple Consensus) algorithm with matching points and the Homography matrix to transform the image. The SURF(Speeded Up Robust Features) algorithm which is used in this paper to extract featuring points uses an image's black and white informations and local spatial informations. The SURF is widely being used since it is very much robust at detecting image's size, view-point changes, and additionally, faster than the SIFT(Scale Invariant Features Transform) algorithm. The SURF has a shortcoming of making an error which results in decreasing the RANSAC algorithm's performance speed when extracting image's feature points. As a result, this may increase the CPU usage occupation rate. The error of detecting matching points may role as a critical reason for disqualifying panoramic image's accuracy and lucidity. In this paper, in order to minimize errors of extracting matching points, we used $3{\times}3$ region's RGB pixel values around the matching points' coordinates to perform intermediate filtering process for removing wrong matching points. We have also presented analysis and evaluation results relating to enhanced working speed for producing a panorama image, CPU usage rate, extracted matching points' decreasing rate and accuracy.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.9
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• pp.1388-1398
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• 2015

Quality Function Deployment (QFD) is the most complete and comprehensive method for translating what customers need from a product. This study utilized QFD to develop sauces made from Gochujang and to determine how to fulfill international customers' requirements. A customer survey and expert opinion survey were conducted from May 13 to August 22, 2014 and targeted 220 consumers and 20 experts in the U.S. and China. Finally, a total of 208 (190 consumers and 18 experts) useable data were selected. The top three customer requirements for Gochujang sauces were identified as fresh flavor (4.40), making better flavor (3.99), and cooking availability (3.90). Thirty-three engineering characteristics were developed. The results from the calculation of relative importance of engineering characteristics identified that 'cooking availability', 'free sample and food testing', 'unique concept', and 'development of brand' were the highest. The relative importance of engineering characteristics, correlation, and technical difficulties are ranked, and this result could contribute to the development Korean sauces based on customer needs and engineering characteristics.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.1
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• pp.15-22
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• 2017

Purpose During Brain SPECT study, critical factor for proper study with $^{99m}Tc-ECD$ or $^{99m}Tc-HMPAO$ is one of the important causes to patent's movement. It causes both improper diagnosis and examination failure. In this study, we evaluated the effect of Dynamic Continuous Mode Acquisition compared to Step and Shoot Mode to raise efficacy and reject the data set with movement, as well as, be reconstructed in certain criteria. Materials and Methods Deluxe Jaszczak phantom and Hoffman 3D Brain phantom were used to find proper standard data set and exact time. Step and Shoot Mode and Dynamic Continuous Mode Acquisition were performed with SymbiaT16. Firstly, Deluxe Jaszczak phantom was filled with $Na^{99m}TcO_4$ 370 MBq and obtained in 60 minutes to check spatial resolution compared with Step and Shoot Mode and Dynamic Continuous Mode. The second, the Hoffman 3D Phantom filled with $Na^{99m}TcO_4$ 74 MBq was acquired for 15 Frame/minutes to evaluate visual assessment and quantification. Finally, in the Deluxe Jaszczak phantom, Spheres and Rods were measured by MI Apps program as well as, checking counts with the frontal lobe, temporal lobe, occipital lobe, cerebellum and hypothalamus parts was performed in the Hoffman 3D Brain Phantom. Results In Brain SPECT Study, using Dynamic Continuous Mode rather than current Step and Shoot Mode, we can do the reading using the 20 to 50 % of the acquired image, and during the test if the patient moves, we can remove unneeded image to reduce the rate of restudy and reinjection. Conclusion Dynamic Continuous Mode in Brain study condition enhances effects compared to Step and Shoot Mode. And also is powerful method to reduce reacquisition rate caused by patient movement. The findings further indicate that it suggest rejection limit to maintain clinical value with certain reconstruction factors compared with Tomo data set. Further examination to improve spatial resolution, SPECT/CT should be the answer for that.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1475-1490
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• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.