• 천문학회보
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• 제44권2호
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• pp.44.1-44.1
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• 2019

We generate new intermediate images between observed consecutive solar images using NVIDIA's SuperSloMo that is a novel video interpolation method. This technique creates intermediate frames between two successive frames to form a coherent video sequence for both spatially and temporally. By using SuperSloMo, we create 600 images (12-second interval) using the observed 121 SDO/AIA 304 Å images (1-minute interval) of a filament eruption event on December 3, 2012. We compare the generated images with the original 12-second images. For the generated 480 images the correlation coefficient (CC), the relative error (R1), and the normalized mean square error (R2) are 0.99, 0.40, and 0.86, respectively. We construct a video made of the generated images and find a smoother erupting movement. In addition, we generate nonexistent 2.4-second interval images using the original 12-second interval images, showing slow motions in the eruption. We will discuss possible applications of this method.

• 천문학회보
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• 제44권1호
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• pp.81.2-81.2
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• 2019

We develop an image-to-image translation model, which is a popular deep learning method based on conditional Generative Adversarial Networks (cGANs), to generate solar magnetograms and EUV images from sunspot drawings. For this, we train the model using pairs of sunspot drawings from Mount Wilson Observatory (MWO) and their corresponding SDO/HMI magnetograms and SDO/AIA EUV images (512 by 512) from January 2012 to September 2014. We test the model by comparing pairs of actual SDO images (magnetogram and EUV images) and the corresponding AI-generated ones from October to December in 2014. Our results show that bipolar structures and coronal loop structures of AI-generated images are consistent with those of the original ones. We find that their unsigned magnetic fluxes well correlate with those of the original ones with a good correlation coefficient of 0.86. We also obtain pixel-to-pixel correlations EUV images and AI-generated ones. The average correlations of 92 test samples for several SDO lines are very good: 0.88 for AIA 211, 0.87 for AIA 1600 and 0.93 for AIA 1700. These facts imply that AI-generated EUV images quite similar to AIA ones. Applying this model to the Galileo sunspot drawings in 1612, we generate HMI-like magnetograms and AIA-like EUV images of the sunspots. This application will be used to generate solar images using historical sunspot drawings.

• 한국의상디자인학회지
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• 제17권3호
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• pp.1-11
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• 2015

This study comparatively analyzed the images and preference of lighting space using the emotion-based technique in order to effectively use it in clothing shops and fashion marketing. In terms of color temperature for light sources, 2,800K of lamp color, 6,500K of daylight color and 4,200K of white color were used. For the assessment, sensory evaluation technique was used. Then, the study found the followings: In terms of the image of lighting space by light source, different images were observed by light source with significant difference by the evaluation category. For factor analysis by the evaluation category, 7 factors were extracted. Among them, evaluation on lighting space was influenced by the following three images: modern space, elegant space and classical space. In particular, the modern space comprised of the following adjectives had the biggest effect on the assessment of the image of lighting space ('refreshing,' 'transparent,' 'bluish,' 'bright' and 'non-classical') (primary evaluation 30.13%). According to assessment on the preference of lighting space, the respondents' most favorite lighting space was 4,200K while their least favorable one was 6,500K in terms of color temperature. In terms of preference by the image of lighting space, they didn't like 'non-elegant' and 'non-beige' images even though they had the images of modern space. Therefore, it was confirmed that beige and elegant space images have an effect on the preference of lighting space.

• 한국실내디자인학회논문집
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• 제14권5호
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• pp.106-113
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• 2005

New images percolate through human consciousness by the media such as movies, TV programs, and brilliant advertisements. These images reproduce new ' things ' throughout the ' semantic processes ' by those who experience and recognize them. Alvin Toffler describes it as the ' information bomb ' and ' image fragments ' in his talk about the new paradigm of information-oriented era. The increasing number of images and their accelerating rate of appearance imply that images become more momentary, and are evidence that they are transforming entire human life and consciousness. Such awareness means a lot to a designer. Especially, the subject that how modern space-dominating images are related to the structure and materials constituting the space and communicate with human mind will be an important factor in establishing the human-space relationship in the future. Furthermore, the present age overspread with various medium is not the only one privileged of the images that exist within space. They are the results of continuous expansion of existing images, and also process of evolution of space powered by the fusion of images and digital media. Imagified space is a boundary layer of Cyberspace, and the space itself becomes an interface by human recognition and participation. Now, the functional classification of spaces such as ' office, ' ' cafe, ' and ' school ' is meaningless. Whatever it may be, the function of a space is defined by the information it interfaces, and therefore it becomes an interface to information through a large number of images. Based on this idea, we will observe the imagificaiton of space in the form of discussion, and from that, try to understand the phenomenon through the real world examples.

• 천문학회보
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• 제44권1호
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• pp.51.2-51.2
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• 2019

Multi-wavelength observations become very popular in astronomy. Even though there are some correlations among different sensor images, it is not easy to translate from one to the other one. In this study, we apply a deep learning method for image-to-image translation, based on conditional generative adversarial networks (cGANs), to solar images. To examine the validity of the method for scientific data, we consider several different types of pairs: (1) Generation of SDO/EUV images from SDO/HMI magnetograms, (2) Generation of backside magnetograms from STEREO/EUVI images, (3) Generation of EUV & X-ray images from Carrington sunspot drawing, and (4) Generation of solar magnetograms from Ca II images. It is very impressive that AI-generated ones are quite consistent with actual ones. In addition, we apply the convolution neural network to the forecast of solar flares and find that our method is better than the conventional method. Our study also shows that the forecast of solar proton flux profiles using Long and Short Term Memory method is better than the autoregressive method. We will discuss several applications of these methodologies for scientific research.

• 천문학회보
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• 제36권2호
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• pp.73.1-73.1
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• 2011

An understanding of the ultraviolet (UV) properties of nearby galaxies is essential for interpreting images of high redshift systems. In this respect, the prediction of optical-band morphologies at high redshifts requires UV images of local galaxies with various morphologies. We present the simulated optical images of galaxies at high redshifts using diverse and high-quality UV images of nearby galaxies obtained through the Galaxy Evolution Explorer (GALEX). We measured CAS (concentration, asymmetry, clumpiness) as well as Gini/M20 parameters of galaxies at near-ultraviolet (NUV) and simulated optical images to quantify effects of redshift on the appearance of distant stellar systems. We also discuss the change of morphological parameters with redshift.

• 천문학회보
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• 제46권1호
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• pp.60.2-60.2
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• 2021

In this study, we determinate the differential emission measure(DEM) of solar corona using three SDO/AIA EUV channel images and three AI-generated ones. To generate the AI-generated images, we apply a deep learning model based on multi-layer perceptrons by assuming that all pixels in solar EUV images are independent of one another. For the input data, we use three SDO/AIA EUV channels (171, 193, and 211). For the target data, we use other three SDO/AIA EUV channels (94, 131, and 335). We train the model using 358 pairs of SDO/AIA EUV images at every 00:00 UT in 2011. We use SDO/AIA pixels within 1.2 solar radii to consider not only the solar disk but also above the limb. We apply our model to several brightening patches and loops in SDO/AIA images for the determination of DEMs. Our main results from this study are as follows. First, our model successfully generates three solar EUV channel images using the other three channel images. Second, the noises in the AI-generated EUV channel images are greatly reduced compared to the original target ones. Third, the estimated DEMs using three SDO/AIA images and three AI-generated ones are similar to those using three SDO/AIA images and three stacked (50 frames) ones. These results imply that our deep learning model is able to analyze temperature response functions of SDO/AIA channel images, showing a sufficient possibility that AI-generated data can be used for multi-wavelength studies of various scientific fields. SDO: Solar Dynamics Observatory AIA: Atmospheric Imaging Assembly EUV: Extreme Ultra Violet DEM: Diffrential Emission Measure

• 한국식생활문화학회지
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• 제25권6호
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• pp.719-724
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• 2010

The purpose of this study was to survey public preferences for dining space image styles depending on the types of passage rites in Korea and to determine potential differences in public preferences for dining space image styles depending on the types of passage rites in terms of various general characteristics such as gender, age, family type, and preference for the image and color styles of the dining space. As a result, this study determined the following: According to a public preference survey of dining space image styles depending on the type of passage rites, our respondents showed the highest preference for casual images (27.1%) at a party for a 100-day-old baby. Additionally, our respondents showed the highest preference for casual images (27.4%) when celebrating a baby's first birthday but showed the highest preference for romantic images (35.8%) when celebrating a baby girl's first birthday. Our respondents showed the highest preference for casual images (21.4%) for graduation ceremonies. Our respondents showed the highest preference for classic images (21.7%) at coming-of-age ceremonies for new adult men, but also showed highest preference for elegant images (26.2%) at coming-of-age ceremonies for new adult women. Moreover, the respondents showed highest preference for classic images (41.0%) at traditional wedding ceremonies but elegant images (24.1%) at modern wedding ceremonies. In contrast, the respondents showed highest preference for classic images (31.3%) for a 60th birthday party. The highest preference for classic images (28.9%) was found for a diamond wedding ceremony. Respondents showed highest preference for classic images (30.4%) for a funeral ceremony Finally, our respondents showed highest preference for classic images (32.5%) at memorial services (religious ceremonies).

• 한국실내디자인학회논문집
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• 제21권3호
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• pp.111-118
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• 2012

The purpose of this study is to focus on living rooms that are preferred as a place for changing space image to the maximum and to find a method how finishing materials are expressed by selecting space with mix & match of many images. The study methods are as follows. First, understand the expressive trend of space images through the precedent studies and magazines, and examine its relationship with finishing materials. Second, select space images based on the contents understood earlier and extract adjective words that represent each space image through an expert survey. Third, find the cases where space images are expressed based on the extracted words and analyze expression methods of finishing materials. The results of the study are as follows. First, it was confirmed that recent space images are actively expressed through finishing materials. Second, space images selected through data related to the trend were classified as modern+natural, modern+traditional, modern+retro, classic+natural, classic+humor, and futurism+natural and 4 adjective words for each space image were extracted. Third, expressive elements of finishing materials were extracted as 'material'. 'texture', 'color', and 'pattern' through the precedent studies. Fourth, expressive methods of finishing materials for each space image could be suggested by analyzing the examples that show mix & match based on the contents extracted earlier. Lastly, it is expected to find various methods that lead space image into finishing materials by evaluating responses and changes in visual perception of residents according to expression of finishing materials based on this study.

• 한국실내디자인학회논문집
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• 제23권5호
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• pp.112-121
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• 2014

The purpose of this study is to develop the user-oriented housing interior space sensibility evaluation tools: The user-oriented housing interior space sensibility evaluation tools shall be developed through the systematic selection process of the extracted housing interior space images, which were linked with the adjectives of sensibility evaluation selected for the housing interior space preferred by the user from the specific words of the sensibility extracted to identify the characteristics of the user's sensibility which is recently being changed. In the results of analyzing the words of sensibility for the residential space preferred by the users with 48 pairs of adjectives. The user-oriented sensibility assessment tool was built by extracting 8 sensibility factors of 'cozy', 'practical' 'cheerful', 'traditional', 'unique', 'congenial', 'sensuous', and 'gorgeous' in the exploratory factor analysis. The image scale was constructed in two-dimensions of the sense of space and the type of space for the residential interior space images. The dimension of the 'sense of space' is explained by the axis of open-closed and the dimension of 'type of space, is explained by the axis of 'natural-artificial'. Such a structural model of the residential interior design attributes were divided into 8 groups. And the 42 images representing each group were selected and the user-oriented residential interior space image tool was built by adding user's selective elements.