• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.72.4-73
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• 2015

Over the last decade, more than 50 quasars have been discovered at redshift about 6 when reionization of the universe occurred. However, most of them are luminous quasars (zAB < 21 mag), implying that such a biased quasar sample, which cannot represent the entire population of quasars at z~6, is not enough to understand the properties of quasars in the early universe. Recently, we have been performing the Infrared Medium-deep Survey (IMS), a moderately wide (120 deg2) and deep (JAB ~ 22.5 - 23 mag) near-infrared imaging survey. Combining this with the optical (ugriz) imaging data from the CFHT Legacy Survey (CFHTLS), we have identified more than 10 faint quasar candidates at z~6 in the IMS field by using multiple color selection criteria. From now on, we will perform spectroscopic confirmations of these faint quasar candidates with IMACS on the Magellan Baade Telescope at Las Campanas Observatory and GMOS on the Gemini South Telescope at Gemini Observatory. The confirmed quasars will be used to constrain the faint-end slope of the quasar luminosity function at z~6 and calculate the ratio of quasar ionizing flux to required flux for reionization of the universe. Moreover, these confirmed quasars will be followed up with near-infrared spectroscopy to determine their black hole masses and Eddington ratios to check the rapidness of their growth.

• Journal of The Korean Astronomical Society
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• v.38 no.2
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• pp.179-182
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• 2005

We conducted an extremely wide field survey of z ${\~}$ 6 Lyman break galaxies (LBGs) to precisely derive their bright end surface density overcoming the bias due to cosmic variance. We selected out LBG candidates in the Subaru/ XMM-Newton Deep Survey Field (SXDS) over the total of ${\~}1.0\;deg^2$ sky area down to $z_{AB} = 26.0 ({\ge}3{\sigma},\;2'.0 aperture)$ using i' - z' > 1.5 color cut. This sample alone is likely to be contaminated by M/L/T dwarfs, low-z elliptical galaxies, and z ${\~}$ 6 quasars. To eliminate these interlopers, we estimated their numbers using an exponential disk star count model, catalogs of old ellipticals in the SXDS and other field, and a z${\~}$6 quasar luminosity function. The finally derived surface density of z ${\~}$ 6 LBGs was 165 $mag^{-1}\;deg^{-2}$ down to $z_{AB}$ = 26.0 and shows good agreement with previous results from the narrower field survey of HST GOODS.

• Journal of The Korean Astronomical Society
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• v.30 no.1
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• pp.83-94
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• 1997

We have identified the candidates for the primordial galaxies in the process of formation in the Hubble Deep Field (hereafter HDF). In order to select these objects we have removed objects brighter than 29-th magnitude in the HDF images and smoothed the maps with the Gaussian filters with the FWHM of 0.8' and 4' to obtain the difference maps. This has enabled us to find. very faint diffuse structures close to the sky level. Peaks are identified in the difference map for each of three HDF chips with three filters (F450W, F606W, and F814W). They have the apparent AB magnitudes typically between 29 and 31. The objects identified in different wavelengths filters have a strong cross-correlations. The correlation lengths are about 0.8'. This means that an object found in one filter can be also found as a peak within 0.8' separation in another filter, thus telling the reality of the identified objects. This angular scale is also the size of the primordial galaxies which have strong color fluctuations on their surfaces. Their large-scale distribution quite resembles that of nearby galaxies, supporting the idea that these objects are ancestors of the present bright galaxies forming at statistically high density regions. Inspections on individual objects show that these primordial galaxy candidates have tiny multiple glares embedded in diffuse backgrounds. Their radial light distributions are quite different from that of nearby bright galaxies. We may be now looking at the epoch of galaxy formation.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.163-171
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• 2016

Lane detection is a widely researched topic. Although simple road detection is easily achieved by previous methods, lane detection becomes very difficult in several complex cases involving noisy edges. To address this, we use a Convolution neural network (CNN) for image enhancement. CNN is a deep learning method that has been very successfully applied in object detection and recognition. In this paper, we introduce a robust lane detection method based on a CNN combined with random sample consensus (RANSAC) algorithm. Initially, we calculate edges in an image using a hat shaped kernel, then we detect lanes using the CNN combined with the RANSAC. In the training process of the CNN, input data consists of edge images and target data is images that have real white color lanes on an otherwise black background. The CNN structure consists of 8 layers with 3 convolutional layers, 2 subsampling layers and multi-layer perceptron (MLP) of 3 fully-connected layers. Convolutional and subsampling layers are hierarchically arranged to form a deep structure. Our proposed lane detection algorithm successfully eliminates noise lines and was found to perform better than other formal line detection algorithms such as RANSAC

• Journal of Bio-Environment Control
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• v.2 no.1
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• pp.53-57
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• 1993

This study was carried out to investigate the appropriate hydroponic system when cherry tomatoes were grown in summer. The base diameter of the trunk, leaf length, leaf width, and the length of cluster were good in deep flow culture(DFC), and not different between NFT and rockwool culture. The first time of flowering and the fruit coloring per cluster were not different among cultural systems, but the marketable yields were good in DFC. In DFC, ％ dry weight, firmness, the content of organic acid and sugar were low, and the ratio of sugar/organic acid and vitamin C were high. So DFC is recommended for the summer cultivation of cherry tomatoes.

• Smart Structures and Systems
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• v.23 no.4
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.288-294
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• 2022

The media content market has been growing, as various types of content are being mass-produced owing to the recent proliferation of the Internet and digital media. In addition, platforms that provide personalized services for content consumption are emerging and competing with each other to recommend personalized content. Existing platforms use a method in which a user directly inputs video metadata. Consequently, significant amounts of time and cost are consumed in processing large amounts of data. In this study, keyframes and audio spectra based on the YCbCr color model of a movie trailer were extracted for the automatic generation of metadata. The extracted audio spectra and image keyframes were used as learning data for genre recognition in deep learning. Deep learning was implemented to determine genres among the video metadata, and suggestions for utilization were proposed. A system that can automatically generate metadata established through the results of this study will be helpful for studying recommendation systems for media super-personalization.

• Journal of Broadcast Engineering
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• v.28 no.2
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• pp.230-237
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• 2023

Self-driving is a key technology of the fourth industry and can be applied to various places such as cars, drones, cars, and robots. Among them, localiztion is one of the key technologies for implementing autonomous driving as a technology that identifies the location of objects or users using GPS, sensors, and maps. Locilization can be made using GPS or LIDAR, but it is very expensive and heavy equipment must be mounted, and precise location estimation is difficult for places with radio interference such as underground or tunnels. In this paper, to compensate for this, we proposes an image retrieval using attention module and image segmentation maps using color images acquired with low-cost vision cameras as an input.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.205-207
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• 2020

This paper proposes a novel deep learning-based method to upsample a depth map. Most conventional methods estimate high-resolution depth map by modifying pixel value of given depth map using high-resolution color image and low-resolution depth map. However, these methods cause under- or over-shooting problems that restrict performance improvement. To overcome these problems, the proposed method iteratively performs grid warping scheme which shifts pixel values to restore blurred image for estimating high-resolution depth map. Experimental results show that the proposed method improves both quantitative and visual quality compared to the existing method.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.38.3-38.3
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• 2019

We present newly discovered dwarf galaxy candidates in deep wide-field images of NGC 1291 obtained with KMTNet. We identify 15 dwarf galaxy candidates by visual inspection within the virial radius of NGC 1291. Using imaging simulations, we demonstrate that our imaging data is complete up to 26 mag arcsec^-2 or -10 abs.mag with > 70% of the completeness rate. We also apply automated detection method to find the dwarfs. However, the completeness and the reliability are relatively low compared to the visual inspection. We find that structural and photometric properties of dwarf candidates such as effective radius, central surface brightness, Sérsic index, and absolute magnitude appear to be consistent with those of known dwarf galaxies in nearby groups and clusters, except for color. NGC 1291, residing in a relatively isolated environment, tends to accompany bluer dwarf galaxies (≃0.58) than those in denser environment. It shows that the quenching of dwarfs is susceptible to the environment.