• Korean Architects
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• no.6 s.374
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• pp.18-19
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• 2000

• Journal of Korean Society of Environmental Engineers
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• v.22 no.7
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• pp.1263-1271
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• 2000

This study investigated the floc structure and removal of turbidity and organic matter by alum coagulation. Results of this study indicated that sweep floc and charge neutralization area were shifted to more acidic region than that in the Amirtharajah's diagram. This was caused by organic matter present in the raw water. Removal regions of turbidity and organic matter were generally overlapped. However, organic matters was removed better at lower pH than turbidity. Floc structure was characterized by measuring fractal dimension and volume diameter using AIA and SALLS. SALLS method was found to be more reliable than AIA method. Floes in sweep floc region had larger size and fractal dimension than flocs in charge neutralization region. As pollutant removal increased, larger fractal dimension and size of floc were measured.

• Journal of Korean Society on Water Environment
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• v.16 no.4
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• pp.459-468
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• 2000

This study is to investigate the floc structure and removal of turbidity and organic matter by PACI coagulation. The turbidity removal by PACI coagulation was obtained at larger pH range than alum coagulation. And the removal of organic matter was obtained at smaller pH range than that of turbidity. The organic matter was removed by the adsorption of $Al(OH)_3$ precipitates. Floc structure was characterized by measuring fractal dimension and volume diameter using AIA and SALLS. Fractal dimension measured by AIA did not show the different characteristics of floc produced in sweep floe and charge neutralization region. Using SALLS, floes in sweep floc region were found to be larger size and fractal dimension than flocs in charge neutralization region. As pollutant removal increased. larger fractal dimension and size of floc were measured. SALLS method was found to be more useful method to characterize flocs in coagulation than AIA method.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.81.3-81.3
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• 2019

In this study, we apply conditional Generative Adversarial Network, which is one of the deep learning method, to the image-to-image translation from solar magentograms to solar UV and EUV images. For this, we train a model using pairs of SDO/AIA 9 wavelength UV and EUV images and their corresponding SDO/HMI line-of-sight magnetograms from 2011 to 2017 except August and September each year. We evaluate the model by comparing pairs of SDO/AIA images and corresponding generated ones in August and September. Our results from this study are as follows. First, we successfully generate SDO/AIA like solar UV and EUV images from SDO/HMI magnetograms. Second, our model has pixel-to-pixel correlation coefficients (CC) higher than 0.8 except 171. Third, our model slightly underestimates the pixel values in the view of Relative Error (RE), but the values are quite small. Fourth, considering CC and RE together, 1600 and 1700 photospheric UV line images, which have quite similar structures to the corresponding magnetogram, have the best results compared to other lines. This methodology can be applicable to many scientific fields that use several different filter images.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.42.3-42.3
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• 2019

We translate Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA) ultraviolet (UV) multi-channel images into another UV single-channel image using a deep learning algorithm based on conditional generative adversarial networks (cGANs). The base input channel, which has the highest correlation coefficient (CC) between UV channels of AIA, is 193 Å. To complement this channel, we choose two channels, 1600 and 304 Å, which represent upper photosphere and chromosphere, respectively. Input channels for three models are single (193 Å), dual (193+1600 Å), and triple (193+1600+304 Å), respectively. Quantitative comparisons are made for test data sets. Main results from this study are as follows. First, the single model successfully produce other coronal channel images but less successful for chromospheric channel (304 Å) and much less successful for two photospheric channels (1600 and 1700 Å). Second, the dual model shows a noticeable improvement of the CC between the model outputs and Ground truths for 1700 Å. Third, the triple model can generate all other channel images with relatively high CCs larger than 0.89. Our results show a possibility that if three channels from photosphere, chromosphere, and corona are selected, other multi-channel images could be generated by deep learning. We expect that this investigation will be a complementary tool to choose a few UV channels for future solar small and/or deep space missions.

• Korean Architects
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• s.542
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• pp.26-33
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• 2014

• Journal of the Korean hospital association
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• v.22 no.10 s.208
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• pp.46-49
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• 1993

• Journal of the Korean hospital association
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• v.22 no.5 s.204
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• pp.4-10
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• 1993

• Korean Architects
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• s.506
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• pp.89-94
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• 2011

• Korean Architects
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• s.552
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• pp.38-43
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• 2015