• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.82.2-82.2
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• 2012

We present Herschel-PACS observations of L1448-MM, a Class 0 protostar with a prominent outflow, part of the DIGIT Key Program (PI: N. Evans). We detect numerous emission lines including CO and $H_2O$ rotational transitions, OH transitions, and [OI] forbidden transitions at wavelengths from 55 to 210 ${\mu}m$. The $H_2O$, [OI], mid-J CO (J < 23), and OH emission distributes along the outflow direction although high-J CO and other OH emission peaks at the central spatial pixel. According to our simple excitation analysis, CO seems to have two temperature components of warm and hot, which might be attributed to the PDR and shock, respectively. After exploring a wide range of physical conditions with a non-LTE LVG code, RADEX, we found that either shock alone or the combination of PDR and shock can explain the observations. The relative fraction of observed line luminosities suggest that L1448-MM is shielded from the UV radiation because $H_2O$ and CO are the dominant coolants rather than OH and [OI]. In addition, our observed fluxes match better with C-shock models rather than J-shocks. The non-LTE LVG model supports that the IR pumping process is important for OH transitions because the OH line ratios are fitted much better when the dust thermal continuum is included.

• Investigative Magnetic Resonance Imaging
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• v.21 no.4
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• pp.210-222
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• 2017

Purpose: To develop a novel combination of controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) with integrated SSFP (CAIPI-iSSFP) for accelerated SSFP imaging without banding artifacts at 3T. Materials and Methods: CAIPI-iSSFP was developed by adding a dephasing gradient to the balanced SSFP (bSSFP) pulse sequence with a gradient area that results in $2{\pi}$ dephasing across a single pixel. Extended phase graph (EPG) simulations were performed to show the signal behaviors of iSSFP, bSSFP, and RF-spoiled gradient echo (SPGR) sequences. In vivo experiments were performed for brain and abdominal imaging at 3T with simultaneous multi-slice (SMS) acceleration factors of 2, 3 and 4 with CAIPI-iSSFP and CAIPI-bSSFP. The image quality was evaluated by measuring the relative contrast-to-noise ratio (CNR) and by qualitatively assessing banding artifact removal in the brain. Results: Banding artifacts were removed using CAIPI-iSSFP compared to CAIPI-bSSFP up to an SMS factor of 4 and 3 on brain and liver imaging, respectively. The relative CNRs between gray and white matter were on average 18% lower in CAIPI-iSSFP compared to that of CAIPI-bSSFP. Conclusion: This study demonstrated that CAIPI-iSSFP provides up to a factor of four acceleration, while minimizing the banding artifacts with up to a 20% decrease in the relative CNR.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.32-39
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• 2009

In this paper, the method of region segmentation using genetic algorithm is proposed for an improvement of efficiency in moving picture coding. A genetic algorithm is the method that searches a large probing space using only a function value for a optimal combination consecutively. By progressing both motion presumption and region segmentation at once, we can assign the motion vector in a image to a small block or a pixel respectively, and transform the capacity of coding and a signal to noise rate into a problem of optimization. That is to say, there is close correlation between region segmentation and motion presumption in motion-compensated prediction coding. This is to optimize the capacity of coding and a S/N ratio. This is to arrange the motion vector in each block of picture according to the state of optimization. Therefore, we examined both the data type of genetic algorithm and the method of data processing to obtain the results of optimal region segmentation in this paper. And we confirmed the validity of a proposed method using the test pictures by means of computer simulation.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.118-121
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• 2014

Image interpolation is a method of determining the value of new pixel coordinate in the process of image scaling. Recently, image contents are likely to be a large-capacity, interpolation algorithm is required to generate fast enhanced result image. In this paper, fast multiple mixed image interpolation for image resolution enhancement is proposed. The proposed method estimates expected 12 shortfalls from four sub-images of a input image, and generates the result image that is interpolated in the combination of the expected shortfalls with the input image. The experimental results demonstrate that PSNR increases maximum value of 1.9dB, SSIM increases maximum value of 0.052, and the subjective quality is superior to any other compared methods. Moreover, it is known by algorithm running time comparison that the proposed method has been at least three times faster than the compared conventional methods. The proposed method can be useful for application on image resolution enhancement.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.258-266
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• 2019

This study was conducted to apply natural river technologies to levees and examine the results. The new eco-friendly bio-polymer was applied, a combination of eco-friendly biopolymers and soil, to levee slope to enhance durability and eco-friendliness and to establish reinforcement measures against unstable levees due to overtopping. A semi-prototype levee of 1 m in height, 3 m in width, with a 1:2 slope and 5 m length, was constructed at the Andong River Experiment Center. The bio-soil mixed with the biopolymer and the soil at an appropriate ratio was treated with a 5 cm thickness on the surface of levee to perform the stability evaluation according to overtopping. Using the pixel-based analysis technique using the image analysis program, the breached area of levee slope was calculated over time. As a result, the time for complete decay occurs more than 12 times than that of ordinary soil levee. Therefore, when the new substance is applied to the surface of levee, the decay delay effect appears to be high.

• Journal of the Korean association of regional geographers
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• v.12 no.4
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• pp.496-507
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• 2006

A hybrid approach integrating both high-resolution and hyperspectral data sets was used to map vegetation cover of a coastal lowland area in the Hawaii Volcanoes National Park. Three common grass species (broomsedge, natal redtop, and pili) and other non-grass species, primarily shrubs, were focused in the study. A 3-step, hybrid approach, combining an unsupervised and a supervised classification schemes, was applied to the vegetation mapping. First, the IKONOS 1-m high-resolution data were classified to create a binary image (vegetated vs. non--vegetated) and converted to 20-meter resolution percent cover vegetation data to match AVIRIS data pixels. Second, the minimum noise fraction (MNF) transformation was used to extract a coherent dimensionality from the original AVIRIS data. Since the grasses and shubs were sparsely distributed and most image pixels were intermingled with lava surfaces, the reflectance component of lava was filtered out with a binary fractional cover analysis assuming that tile total reflectance of a pixel was a linear combination of the reflectance spectra of vegetation and the lava surface. Finally, a supervised approach was used to classify the plant species based on tile maximum likelihood algorithm.

• Restorative Dentistry and Endodontics
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• v.28 no.6
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• pp.485-490
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• 2003

Complete understanding of the exterior and interior structure of the tooth would be prerequisite to the successful clinical results, especially in the restorative and endodontic treatment. Although three-dimensional reconstruction method using x-ray microtomography could not be used in clinical cases, it may be the best way to reconstruct the morphologic characteristics of the tooth structure in detail without destructing the tooth itself. This study was done to three dimensionally reconstruct every teeth in the arch in order to increase the understanding about the endodontic treatment and to promote the effective restorative treatment by upgrading the knowledge of the tooth morphology. After placing tooth between the microfocus x-ray tube and the image intensifier to obtain two-dimensional images of each level. scanning was done under the condition of 80 keV, $100{\;}\mu\textrm{m}$, 16.8 magnification with the spot size of $8{\;}\mu\textrm{m}$. Cross-section pixel size of $16.28{\;}\mu\textrm{m}$ and 48.83 cross-section to cross-section distance were also used. From the results of this study, precise three dimensional reconstructed images of every teeth could be obtained. Furthermore, it was possible to see image that showed interested area only, for example. enamel portion only, pulp and dentin area without enamel structure, pulp only, combination image of enamel and pulp, etc. It was also possible to see transparent image without some part of tooth structure. This image might be used as a guide when restoring and preparing the full and partial crown by showing the positional and morphological relationship between the pulp and the outer tooth structure. Another profit may be related with the fact that it would promote the understanding of the interior structure by making observation of the auto-rotating image of AVI file from the various direction possible.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.21-29
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• 2019

This paper proposes a novel framework for image fusion of satellite imagery to enhance spatial resolution of the image via structure-texture decomposition. The resolution of the satellite imagery depends on the sensors, for example, panchromatic images have high spatial resolution but only a single gray band whereas multi-spectral images have low spatial resolution but multiple bands. To enhance the spatial resolution of low-resolution images, such as multi-spectral or infrared images, the proposed framework combines the structures from the low-resolution image and the textures from the high-resolution image. To improve the spatial quality of structural edges, the structure image from the low-resolution image is guided filtered with the structure image from the high-resolution image as the guidance image. The combination step is performed by pixel-wise addition of the filtered structure image and the texture image. Quantitative and qualitative evaluation demonstrate the proposed method preserves spectral and spatial fidelity of input images.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.4
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• pp.171-178
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• 2019

The use of anti-scatter grids in radiographic imaging has the advantage of preventing the image distortion caused by scattered radiation. However, it carries the side effect of leaving artifacts in the X-ray image. In this paper, we propose a grid line suppression technique using discrete cosine transform(DCT). In X-ray images, the grid lines have different characteristics depending on the shape of the object and the area of the image. To solve this problem, we adopt the DCT transform based on a dynamic segmentation, and propose a filter transfer function for each individual segment. An algorithm for detecting the band of grid lines in frequency domain and a band stop filter(BSF) with a filter transfer function of a combination of Kaiser window and Butterworth filter have been proposed. To solve the blocking effects, we present a method to determine the pixel values using multiple structured images. The validity of the proposed theory has been evaluated from the experimental results using 140 X-ray images.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.