• Gut and Liver
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• v.12 no.6
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• pp.633-640
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• 2018

Background/Aims: Obesity is a risk factor for gastroesophageal reflux disease (GERD), with several studies demonstrating positive associations between body mass index (BMI) and GERD symptoms. However, little is known about the effect of BMI changes on erosive esophagitis (EE). In this study, we investigated whether BMI reduction could resolve EE. Methods: A retrospective cohort study was performed to assess the natural course of EE according to changes in BMI. Participants undergoing health check-ups from 2006 to 2012 were enrolled, and 1,126 subjects with EE were included. The degree of esophagitis was measured by upper endoscopy and serially checked over a 5-year follow-up. Logistic regression and Cox proportional hazards models were used to investigate the association between BMI reduction and EE resolution. Results: Substantial weight loss is associated with EE resolution. The adjusted odds ratio for EE resolution was 1.44 (95% confidence interval [CI], 1.09 to 1.92) among participants with a decrease in BMI compared to those with no decrease in BMI. The EE resolution rate was related to the degree of BMI reduction. The effect of weight loss on EE resolution was higher among subjects who lost more weight. Compared with subjects with no decrease in BMI, the hazard ratios for EE resolution were 1.09 (95% CI, 0.89 to 1.35), 1.31 (95% CI, 1.01 to 1.72) and 2.12 (95% CI, 1.44 to 3.12) in subjects with BMI reductions of ${\leq}1$, 1-2, and >$2kg/m^2$, respectively. Conclusions: EE resolution is associated with a decrease in BMI, and weight loss is potentially an effective GERD treatment.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.463-468
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• 2021

Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.15-22
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• 2020

In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.347-359
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• 2010

Ortho-photos provide valuable spatial and spectral information for various Geographic Information System (GIS) and mapping applications. The absence of relief displacement and the uniform scale in ortho-photos enable interested users to measure distances, compute areas, derive geographic locations, and quantify changes. Differential rectification has traditionally been used for ortho-photo generation. However, differential rectification produces serious problems (in the form of ghost images) when dealing with large scale imagery over urban areas. To avoid these artifacts, true ortho-photo generation techniques have been devised to remove ghost images through visibility analysis and occlusion detection. So far, the Z-buffer method has been one of the most popular methods for true ortho-photo generation. However, it is quite sensitive to the relationship between the cell size of the Digital Surface Model (DSM) and the Ground Sampling Distance (GSD) of the imaging sensor. Another critical issue of true ortho-photo generation using high resolution satellite imagery is the scan line search. In other words, the perspective center corresponding to each ground point should be identified since we are dealing with a line camera. This paper introduces alternative methodology for true ortho-photo generation that circumvents the drawbacks of the Z-buffer technique and the existing scan line search methods. The experiments using real data are carried out while comparing the performance of the proposed and the existing methods through qualitative and quantitative evaluations and computational efficiency. The experimental analysis proved that the proposed method provided the best success ratio of the occlusion detection and had reasonable processing time compared to all other true ortho-photo generation methods tested in this paper.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.1
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• pp.54-58
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• 2019

Purpose At now, there are many kind of dedicated heart SPECT machine in clinical nuclear medicine. Among those, the fixed focusing type SPECT can make a good quality, quantity image because a detectors of this SPECT arranged forward a special ROI and didn't rotate around of body. So, in this paper, we will evaluate a spatial uniformity about resolution and sensitivity at a same plane of a fixed focusing type SPECT. Materials and Methods We used D-SPECT as a fixed focusing type SPECT and Cario MD as a rotated parallel type SPECT to comparing each other. We injected $^{99m}Tc(14.8MBq/1cc)$ to 10 capillary tube (diameter=1mm), and we set those line sources a tfield of view of each SPECT. And then we acquired SPECT date, we applied are construction by recommended methods. By using two tomography images, we calculated a full width of half maximum as a resolution and total counts as a sensitivity, and we compared a CV (coefficientofvariation) values between two images as a spatial uniformity. Results In case of D-SPECT, a CV of resolution and sensitivity are 7.45%, 12.34%. In case of Cario MD, an CV of resolution and sensitivity are 12.49%, 21.84% Conclusion As a results, CV of resolution and sensitivity of a fixed focusing type SPECT is 67.75%, 77.00% higher than ones of a rotated parallel type SPECT. It means that a fixed focusing type SPECT is more uniformed, because this new SPECT can reduce a motion blur artifact by rotating detector around body, also all of detector that made by semiconductor arrange forward a special FOV like heart.

• Applied Microscopy
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• v.47 no.3
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• pp.95-100
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• 2017

High resolution measurements of the carrier profile in semiconductor devices is required as the semiconductor industry progresses from the 10-nm lithography node to 7-nm and beyond. We examine the factors which determine the resolution of the present method of scanning spreading resistance microscopy as well as such factors for the newer method of scanning frequency comb microscopy that is now under development. Also, for the first time, we consider the sensitivity of both methods to the location of heterogeneities in the semiconductor. In addition, mesoscopic effects on these measurements are considered for the first time. Two simple analytical models are extended to study the sensitivity to heterogeneities as well as mesoscopic effects.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.164-169
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• 2005

Microphone array techniques are being used widely in wind tunnel measurements for identification of the distributed aerodynamic noise sources on the model being tested. Depending on the frequencies and sound levels, conventional beamforming algorithm has limitation in separating two adjacent sources. Several modifications to the classical beamforming have been developed to enhance way resolution and reduce sidelobe levels. In this Paper the robust adaptive beamforming and the CLEAN algorithm are used to compare to the result of conventional beamforming method. It is found that the CLEAN algorithm is capable of pin-pointing locations of multiple sources nearby, while these sources are unidentifiable with robust adaptive or conventional beamforming techniques.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.49-52
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• 2002

High-resolution satellite image processing software should be able to ensure accurate, fast, compact data processing in offline or online environment. In this paper, component software for high-resolution satellite image processing is developed using OpenGIS components and real-time data processing architecture. The developed component software is composed of three major packages, which are data provide package, user interface package, and fast data processing package. The data provider package encodes and decodes diverse image/vector data formats and give identical data access methods to developers. The user interface package supports menus, toolbars, dialogs, and events to use easier. The fast data processing package follows the OpenGIS's data processing standards, which can deal with several processors as components with standard procedural functionalities.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.807-810
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• 2003

This research uses high-resolution satellite images as a source of collecting farmland information. For effectively extract the paddy area, we use texture information and different classify methods to assist the satellite image classification. First, using maximum likelihood classifier to extract paddy information from images. The results show that User Accuracy and Procedure Accuracy of the paddy area can increase from 80.60% to 95.45% and 84.38% to 95.45%. Second, establishing a paddy Knowledge Base and using Knowledge Base Classifier to extract paddy area, and result shows the User Accuracy and Producer Accuracy to be 92.16% and 90.06%. Finally, The result shows we can effectively contribute to the paddy field information extraction from high-resolution satellite images.

• Korean Journal of Occupational Health Nursing
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• v.27 no.1
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• pp.13-24
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• 2018

Purpose: The purpose of this study was to investigate conflicts and the experiences of conflict resolution of hospital nurses. Methods: This qualitative study used phenomenological methodology. The research participants were 8 nurses of C hospital. Data were collected through in-depth individual interviews from November 2016 to February 2017. The data analysis method was based on Colaizzi's approach. Results: The study results showed that the experiences of nurses consisted of 15 themes and 6 theme clusters: 1) Ambiguous role, 2) Recognizing lack of competence, 3) Difficulty maintaining relationships, 4) Unsatisfactory work environment, 5) Efforts to resolve conflicts, and 6) Conflict resolution process through labor union. Conclusion: The findings of this study recommend that hospital organizations and managers should pay attention to improving the work environment and organizational culture of members. The results of the present study can improve the understanding of various aspects of the daily life of hospital nurses.