• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.5
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• pp.59-68
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• 2018

This study deals with post-occupancy evaluation (POE) of 'Kim Gwang-seok Street'as storytelling road for urban regeneration. The evaluation focused on image preference and use satisfaction of visitors. This is the initial project of popular singer-storytelling street as a new attraction, and the results could be used as basic data for the sustainable planning and management of storytelling streets. 200 respondents of visitor group were analyzed. Frequency analysis for demographics, use, behavior, reliability, and means, were evaluated. Factor analysis and multiple regression analysis were conducted for image preference and use satisfaction. Responses showed high frequency of twenties in age, who were typically students. In use motives and activities, strolling, sightseeing, and taking pictures were shown to be the most common. The mean scores of image variables showed symbolic (5.03), familiar (4.84), beautiful (4.71), and bright (4.63), the highest. The result of exploratory factor analysis, led to 3 factors being categorized; the mood of street, the design, and the dynamics. Due to the result of multiple regression analysis, the preference of space image was significantly influenced by 4 image variables; symbolic (${\beta}=.314$), stable (${\beta}=.248$), friendly (${\beta}=.204$), and fresh (${\beta}=.142$). In the use satisfaction analysis, the mean scores were highest concerning facility design (3.81), typical street (3.78), the image of street (3.81), continued use (3.66), and experiential tourism (3.53), respectively. The mean score of parking space (2.56) was the lowest. In factor analysis concerning use satisfaction, 3 factors were categorized; facility management, experience, and image. By the result of multiple regression analysis, the total satisfaction of use was significantly influenced by 4 independent variables; image of street (${\beta}=.296$), management (${\beta}=.277$), continued use (${\beta}=.205$), and typical street (${\beta}=.183$). As storytelling road for urban regeneration, 'Kim Gwang-seok Street' has shown considerable success in significant improvement of image preference and user satisfaction. Therefore, the enhancement of 'placeness' through storytelling of Kim Gwang-seok, and the strengthening of its identity and symbolism, should be the basic focus of development for the street in the future.

• Fashion & Textile Research Journal
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• v.19 no.5
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• pp.535-546
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• 2017

This study considers direction types and formative characteristics by analyzing natural objets in contemporary fashion window display. The research methods are both literature review and content analysis based on images of contemporary fashion window display. The conclusions of this study are as follows. First, the direction types of natural objets in contemporary fashion window display expand objective outlines by maximizing the morphological form of objects. Second, a rhythmic sense expressed through a gradual repetition of patterns or a sequential arrangement of natural objects. Third, it represents a reversal of image accomplished through a modification of material on the natural objects. Fourth, a new space is created by transforming two dimensional forms of natural objects. The first formative characteristic of natural objects in contemporary fashion window display is exaggeration. This is expressed through either the expansion of the external volume, the collapsed form of natural objects, or the distortion of external colors. Second, it is a dynamics. This makes it possible to recall the dynamic image of the integration of the observer with a priori thinking by systematically sequencing objects or inducing a specific motion. Third, it is an abstraction. This provides an opportunity for the observer to find in a natural beauty by exchanging physical form or reinterpreting characteristic points. Fourth, it concerns eclecticism characteristic that objects created through the mutual fusion of heterogeneous elements indicates an aesthetic inspiration in a limited space by diverting conventional thinking toward natural objects with expansion of consciousness on formative arts.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.455-461
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• 2021

Cardiomegaly is one of the most common diseases seen on chest X-rays, but if it is not detected early, it can cause serious complications. In view of this, in recent years, many researches on image analysis in which deep learning algorithms using artificial intelligence are applied to medical care have been conducted with the development of various science and technology fields. In this paper, we would like to evaluate whether the Inception V3 deep learning model is a useful model for the classification of Cardiomegaly using chest X-ray images. For the images used, a total of 1026 chest X-ray images of patients diagnosed with normal heart and those diagnosed with Cardiomegaly in Kyungpook National University Hospital were used. As a result of the experiment, the classification accuracy and loss of the Inception V3 deep learning model according to the presence or absence of Cardiomegaly were 96.0% and 0.22%, respectively. From the research results, it was found that the Inception V3 deep learning model is an excellent deep learning model for feature extraction and classification of chest image data. The Inception V3 deep learning model is considered to be a useful deep learning model for classification of chest diseases, and if such excellent research results are obtained by conducting research using a little more variety of medical image data, I think it will be great help for doctor's diagnosis in future.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.167-174
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• 2021

The purpose of this study is to classify TIFF images, PNG images, and JPEG images using deep learning, and to compare the accuracy by verifying the classification performance. The TIFF, PNG, and JPEG images converted from chest X-ray DICOM images were applied to five deep neural network models performed in image recognition and classification to compare classification performance. The data consisted of a total of 4,000 X-ray images, which were converted from DICOM images into 16-bit TIFF images and 8-bit PNG and JPEG images. The learning models are CNN models - VGG16, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0. The accuracy of the five convolutional neural network models of TIFF images is 99.86%, 99.86%, 99.99%, 100%, and 99.89%. The accuracy of PNG images is 99.88%, 100%, 99.97%, 99.87%, and 100%. The accuracy of JPEG images is 100%, 100%, 99.96%, 99.89%, and 100%. Validation of classification performance using test data showed 100% in accuracy, precision, recall and F1 score. Our classification results show that when DICOM images are converted to TIFF, PNG, and JPEG images and learned through preprocessing, the learning works well in all formats. In medical imaging research using deep learning, the classification performance is not affected by converting DICOM images into any format.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.145-157
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• 2015

In 2015, drought was at the worst stage of devastation in Soyanggang Dam watershed. The purpose of this study is to trace the drought area around Soyanggang dam watershed by using Terra MODIS image because it has the ability of spatio-temporal dynamics. The MODIS indices, which included the enhanced vegetation index (NDVI), were extracted from MODIS product MOD13 16-day composite datasets with a spatial resolution of 250m from 2010.01.01 to 2015.06.30. We found that application of Vegetation Condition Index (VCI) and Standardized Vegetation Index (SVI) was suitable for monitoring the drought area. The result can be used to acquire the drought data scattered and demonstrate the potential for the use of MODIS data for temporal and spatial detection of drought effects.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.51.3-51.3
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• 2019

Solar magnetograms are important for studying solar activity and predicting space weather disturbances1. Farside magnetograms can be constructed from local helioseismology without any farside data2-4, but their quality is lower than that of typical frontside magnetograms. Here we generate farside solar magnetograms from STEREO/Extreme UltraViolet Imager (EUVI) $304-{\AA}$ images using a deep learning model based on conditional generative adversarial networks (cGANs). We train the model using pairs of Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) $304-{\AA}$ images and SDO/Helioseismic and Magnetic Imager (HMI) magnetograms taken from 2011 to 2017 except for September and October each year. We evaluate the model by comparing pairs of SDO/HMI magnetograms and cGAN-generated magnetograms in September and October. Our method successfully generates frontside solar magnetograms from SDO/AIA $304-{\AA}$ images and these are similar to those of the SDO/HMI, with Hale-patterned active regions being well replicated. Thus we can monitor the temporal evolution of magnetic fields from the farside to the frontside of the Sun using SDO/HMI and farside magnetograms generated by our model when farside extreme-ultraviolet data are available. This study presents an application of image-to-image translation based on cGANs to scientific data.

• Korean Chemical Engineering Research
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• v.47 no.2
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• pp.179-184
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• 2009

Recent evolution of computer technology enhances the effectiveness of CFD(Computational Fluid Dynamics) analysis for the 3-dimensional complex transport phenomena including turbulent flows. Cheaper and easier than laser and ultra-sonic methods, the windows simulator name by CAMSI(Computer-Aided Management of Stereo Images) has been developed in order to implement the 3-dimensional image using a disparity histogram extracted from left and right stereo images. In our program using the area-based method, the matching pixel finding methods consist of SSD(Sum of Squared Distance), SAD(Sum of Absolute Distance), NCC(Normalized Correlation Coefficient) and MPC(Matching Pixel Count). On performing the program, stereo images on different window sizes for various matching pixel finding methods are compared reasonably. When the image has a small noise, SSD on small window size is more effective. Whereas there is much noise, NCC or MPC is more effective than SSD. CAMSI from the present study will be much helpful to implement the complex objects and to analyze 3-dimensional CFD around them.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.8
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• pp.677-683
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• 2015

Hybrid rocket combustion displays low frequency instability(LFI, 10~30Hz) at a certain condition. Vortex shedding in the post-chamber is suspected to cause the occurrence of LFI. This study focused on the visualization of flow image using light emissions from high temperature combustion gas. Results shows that combustion pressure oscillates at a frequency of about 18 Hz, which is in phase with oscillations of light emission. Since LFI is not a property of thermo-acoustic instability, this result suggested there exists a physical coupling of pressure fluctuations with light emissions proportional to chemical reaction. Also POD analysis shows that dominant symmetric spatial modes in the stable combustion shift suddenly into asymmetric spatial pattern with the appearance of LFI. Especially, the appearance of mode 3 is a typical change of flow dynamics in unstable combustion representing a rotational fluid motions associated with vortex shedding.

• Ocean and Polar Research
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• v.35 no.3
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• pp.229-237
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• 2013

We measured two-dimensional (2-D) oxygen distribution in the surface sediment layer of intertidal sediment using a simple and inexpensive planar oxygen optode, which is based on a color ratiometric image approach. The recorded emission intensity of red color luminophore light significantly changed with oxygen concentration by $O_2$ quenching of platinum(II)octaethylporphyrin (PtOEP). The ratios between the intensity of red and green emissions with oxygen concentration variation demonstrated the Stern-Volmer relationship. The 2-D oxygen distribution image showed microtopographic structure, diffusivity boundary layer and burrow in surface sediment layer. The oxygen penetration depth (OPD) was about 2 mm and the one-dimensional vertical diffusive oxygen uptake (DOU) was 12.6 mmol $m^{-2}d^{-1}$ in the undisturbed surface sediment layer. However, those were enhanced near burrow by benthic fauna, and the OPD was two times deeper and DOU was increased by 34%. The simple and inexpensive oxygen planar optode has great application potential in the study of oxygen dynamics with high spatiotemporal resolution, in benthic boundary layers.

• International Journal of Fluid Machinery and Systems
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• v.2 no.1
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• pp.31-39
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• 2009

The flow instability in a low specific speed mixed-flow pump, having a positive slope of head-flow characteristics was investigated. Based on the static pressure measurements, it was found that a rotating stall in the vaned diffuser occurs at about 65% flow rate of best efficiency point (BEP). A dynamic Particle Image Velocimetry (DPIV) measurement and the numerical simulations were conducted in order to investigate the flow fields. As a result, the diffuser rotating stall was simulated even by Computational Fluid Dynamics (CFD) and the calculated periodic flow patterns agree well with the measured ones by DPIV. It is clarified that a periodical large scaled backflow, generated at the leading edge of the suction surface of the diffuser vane, causes the instability. Furthermore, the growth of the strong vortex at the leading edge of the diffuser vane induces the strong backflow from the diffuser outlet to the inlet. The scale of one stall cell is covered over four-passages in total thirteen vane-passages.