• Journal of the Korean earth science society
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• v.40 no.2
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• pp.177-187
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• 2019

Several volcanic activities have continued in Mt. Baekdu since the Millennium eruption, and these phenomena have increased the need for volcanic activity surveillance. Various geophysical approaches are needed to obtain the depth and size of magma chamber that lie several kilometers below the surface. We examined the applicability of direct-current resistivity survey in this study. In order to explore the deep magma chamber of Mt. Baekdu, which has a spatial limitation due to the borderline, a large-scale survey with a length of tens of kilometers should be conducted. This type of survey requires a distributed measurement system and optimized exploration designs. Therefore, we propose survey designs taking advantage of our developed distributed acquisition system and analyze the applicability using numerical simulation. We confirmed that our designs that use single survey line with offline transmitting points show comparable results to the conventional 3D survey. It is expected that our research result can contribute to the deep geophysical exploration in Mt. Baekdu.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.63.2-63.2
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• 2019

Eight hundred thousand asteroids in the solar system have been identified so far under extensive sky surveys. Kilometer to sub-km sized asteroids, however, are still waiting for discovery, and their size and orbital distribution will provide a better understanding of the collisional and dynamical evolution of the solar system. In order to study the number of asteroids which is detectable with 1.6 m telescope and their orbital distribution, we conducted a small observation campaign as a part of Deep Ecliptic Patrol of the Southern Sky (DEEP-South) project, which is an asteroid survey in the southern hemisphere with Korea Microlensing Telescope Network (KMTNet). We observed the ecliptic plane near opposition ($2^{\circ}{\times}2^{\circ}$ field of view centering on ${\alpha}=22h40m31s$, ${\delta}=-08^{\circ}22^{\prime}58^{{\prime}{\prime}}$) in August 2018, and identified 464 moving objects by visual inspection. As a result, 266 of 464 moving objects turn out to be previously unknown asteroids, and their signal to noise ratio is below two on numerous occasions. Most of the newly detected objects are main belt asteroids (MBAs), while three Hildas, one Jupiter trojan, and two Hungarias are also identified. In this meeting, we report the differences in the orbital distributions between the previously known asteroids and newly discovered ones using statistical methods. We also talk about the observational bias of this survey and suggest future works.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.79.2-79.2
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• 2019

Based on three types of 2-point statistics (galaxy clustering, galaxy-galaxy lensing, and cosmic shear power spectra) from the Deep Lens Survey (DLS), we constrain cosmology and baryonic feedback. The DLS is a deep survey, so-called a precursor to LSST, reaching down to ~27th magnitude in BVRz' over 20 deg2. To measure the three power spectra, we choose two lens galaxy populations centered at z ~0.27 and 0.54 and two source galaxy populations centered at z ~0.64 and 1.1, with more than 1 million galaxies. We perform a number of consistency tests to confirm the reliability of the measurements. We calibrated photo-z estimation of the lens galaxies and validated the result with galaxy cross-correlation measurement. The B-mode signals, indicative of potential systematics, are found to be consistent with zero. The two cosmological results independently obtained from the cosmic shear and the galaxy clustering + galaxy-galaxy lensing measurements agree well with each other. Also, we verify that cosmological results between bright and faint sources are consistent. While there exist some weak lensing surveys showing a tension with Planck, the DLS constraint on S8 agrees nicely with the Planck result. Using the HMcode approach derived from the OWLS simulation, we constrain the strength of baryonic feedback. The DLS results hint at the possibility that the actual AGN feedback may be stronger than the one implemented in the current state-of-the-art simulations.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.78.1-78.1
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• 2019

There are a huge number of faint objects that have not been observed due to the lack of large and deep surveys. In this study, we demonstrate that a deep learning approach can produce a better quality deep image from a single pass imaging so that could be an alternative of conventional image stacking technique or the expensive large and deep surveys. Using data from the Sloan Digital Sky Survey (SDSS) stripe 82 which provide repeatedly scanned imaging data, a training data set is constructed: g-, r-, and i-band images of single pass data as an input and r-band co-added image as a target. Out of 151 SDSS fields that have been repeatedly scanned 34 times, 120 fields were used for training and 31 fields for validation. The size of a frame selected for the training is 1k by 1k pixel scale. To avoid possible problems caused by the small number of training sets, frames are randomly selected within that field each iteration of training. Every 5000 iterations of training, the performance were evaluated with RMSE, peak signal-to-noise ratio which is given on logarithmic scale, structural symmetry index (SSIM) and difference in SSIM. We continued the training until a GAN model with the best performance is found. We apply the best GAN-model to NGC0941 located in SDSS stripe 82. By comparing the radial surface brightness and photometry error of images, we found the possibility that this technique could generate a deep image with statistics close to the stacked image from a single-pass image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• ETRI Journal
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• v.42 no.2
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• pp.292-304
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• 2020

As side-channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side-channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning-based side-channel analysis. In particular, we outline how deep learning is applied to side-channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

• Korean journal of food and cookery science
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• v.17 no.6
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• pp.533-541
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• 2001

This research was carried out to obtain the information about the use of deep-fat-fried foods and treatment of oils used for deep-fat-frying at home. Data were obtained through questionnaires from 442 housewives in Chonbuk area. The frequency of taking deep-fat-fried foods was affected by ages and residential area. Average score for the preference of deep-fat-fried foods was 3.60 in the 5 point scale. Fifty three percent of the respondents prepared deep-fat-fried foods by themselves at home. The oil most commonly used for deep-fat-frying was soybean oil followed by com oil. Proper frying temperature was determined by dropping salt or food coating materials into the oil. Oil color was used as a parameter for determining the life of frying oils by 81.2% of the respondents. Most of the respondents appealed to use oils one more time after filtering. For the disposal of used frying oil, 65.7% of the respondents used some kinds of absorbing papers; 16.1% made soaps and 10.7% discarded into a sink. According to correlation analysis, the frequency of taking deep-fat-fried food had positive relationships with housewives's health status, preference for foods prepared with oil and fats and family's preference for deep-fat-fried foods.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.251-255
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• 2017

Submillimetre and millimetre-wave surveys with Herschel and the South Pole Telescope have revolutionised the discovery of strong gravitational lenses. Their follow-ups have been greatly facilitated by the multi-wavelength supplementary data in the survey fields. The forthcoming Euclid optical/near-infrared space telescope will also detect strong gravitational lenses in large numbers, and orbital constraints are likely to require placing its deep survey at the North Ecliptic Pole (the natural deep field for a wide class of ground-based and space-based observatories including AKARI, JWST and SPICA). In this paper I review the current status of the multi-wavelength survey coverage in the NEP, and discuss the prospects for the detection of strong gravitational lenses in forthcoming or proposed facilities such as Euclid, FIRSPEX and SPICA.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.841-846
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• 2003

The purpose of this paper is to present and discuss some of harbor foundation constructed on seashore soft ground by Deep Wing Mixing in deep mixing method. A series of laboratory and field experiments including unconfined compressive strength, permeability, geo-physical survey, sea water concentration, lateral and settlement measurement, field core sample were carried out to check physical, mechanical and environmental characteristics of solidified foundation soil treated by HWS solidifying agent. The results from this research showed that Deep Wing Mixing method could be efficiently applied in the construction site of seashore structure foundation.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.1-13
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• 2018

Deep learning (DL) models have been widely applied to AI applications such image recognition and language translation with big data. Recently, DL models have becomes larger and more complicated, and have merged together. For the accelerated training of a large-scale deep learning model, model parallelism that partitions the model parameters for non-shared parallel access and updates across multiple machines was provided by a few distributed deep learning frameworks. Model parallelism as a training acceleration method, however, is not as commonly used as data parallelism owing to the difficulty of efficient model parallelism. This paper provides a comprehensive survey of the state of the art in model parallelism by comparing the implementation technologies in several deep learning frameworks that support model parallelism, and suggests a future research directions for improving model parallelism technology.