• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.29.3-30
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• 2015

We introduce an advanced one-to-one galaxy correspondence method that populates dark matter halos with galaxies by tracing merging histories of most bound member particles (MBPs) identified in simulated virialized halos. To estimate the survival time of a satellite galaxy, we adopt several models of tidal-destruction time derived from an analytic calculation, isolated galaxy simulations, and cosmological simulations. We build mock galaxy samples for each model by using a merging tree information of MBPs from our new Horizon Run 4 N-body simulation from z = 12 to 0. For models of galaxy survival time derived from cosmological and isolated galaxy simulations, about 40% of satellites galaxies merged into a certain halo are survived until z = 0. We compare mock galaxy samples from our MBP-galaxy correspondence scheme and the subhalo-galaxy scheme with SDSS volume-limited galaxy samples around z = 0 with $M_r-5{\log}h$ < -21 and -20. Compared to the subhalo-galaxy correspondence method, our method predicts more satellite galaxies close to their host halo center and larger pairwise peculiar velocity of galaxies. As a result, our method reproduces the observed galaxy group mass function, the number of member galaxies, and the two-point correlation functions while the subhalo-galaxy correspondence method underestimates them.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.78.1-78.1
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• 2013

The hierarchical model of galaxy formation predicts that galaxy halos contain merger relics in the form of long stellar stream. Thus, tidal substructure of stars around globular clusters, such as tidal tails, could be an essential evidence of the merging scenario in the formation of the Galaxy. From April 2010 to December 2012, we obtained $45^{\prime}{\times}45^{\prime}$ wide-field JHKs near-infrared photometric imaging data for about 20 globular clusters in the Milky Way, and examined the stellar density distribution around globular clusters. Here, we introduce the preliminary results of stellar spatial distributions and radial surface density profiles of four globular clusters. In order to minimize the field star contamination and identify the cluster's member candidates stars, we used a statistical filtering algorithm and gave weights on the CMDs of globular clusters. In two-dimensional stellar density maps, we could found tidal stripping structures for some globular clusters. The orientation of tidal substructure seems to associate with the effects of dynamical interactions with the Galaxy and cluster's orbit. Indeed, the radial surface density profile accurately describes this stripping structures as a break in the slope of profile. The observational results could give us further observational evidence of merging scenario of the formation of the Galaxy.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.659-665
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• 2000

In this paper a platoon merging control system is considered as a remotely located system with state represented by a stochastic process. in the system it is common to encounter situations where a single decision maker controls a large number of subsystems and observation and control signals are sent over a communication channel with finite capacity and significant transmission delays. Unlike a classical estimation problem where the observation is a continuous process corrupted by additive noise there is a constraint that the observation must be coded and transmitted over a digital communication channel with fintie capacity. A recursive coder-estimator sequence is a state estimation scheme based on observations transmitted with finite communication capacity constraint. in this paper we introduce a stochastic model for the lead vehicle in a platoon of vehicles in a lane considering the angle between the road surface and a horizontal plane as a stochastic process. In order to merge two platoons the lead vehicle of the following platoon is controlled by a remote control station. Using the observation transmitted over communication channel the remote control station designs the feedback controller. The simulation results show that the intervehicle spacings and the deviations from the desired intervehicle spacing are well regulated.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.69.1-69.1
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• 2017

According to structure formation simulations, weak shocks with typical Mach number, M<3, are expected to form in merging galaxy clusters. The presence of such shocks has been indicated by X-ray and radio observations of many merging clusters. In particular, diffuse radio sources known as radio relics could be explained by synchrotron-emitting electrons accelerated via diffusive shock acceleration (Fermi I) at quasi-perpendicular shocks. Here we also consider possible roles of stochastic acceleration (Fermi II) by compressive MHD turbulence downstream of the shock. Then we explore a puzzling discrepancy that for some radio relics, the shock Mach number inferred from the radio spectral index is substantially larger than that estimated from X-ray observations. This problem could be understood, if shock surfaces associated with radio relics consist of multiple shocks with different strengths. In that case, X-ray observations tend to pick up the part of shocks with lower Mach numbers and higher kinetic energy flux, while radio emissions come preferentially from the part of shocks with higher Mach numbers and higher cosmic ray (CR) production. We also show that the Fermi I reacceleration model with preexisting fossil electrons supplemented by Fermi II acceleration due to postshock turbulence could reproduce observed profiles of radio flux densities and integrated radio spectra of two giant radio relics. This study demonstrates the CR electrons can be accelerated at collisionless shocks in galaxy clusters just like supernova remnant shock in the interstellar medium and interplanetary shocks in the solar wind.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.111-111
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• 2018

본 연구의 목적은 조건부 합성 기법(Conditional Merging, CM) 기법을 활용하여 GPM(Global Precipitation Measurement) 위성 자료를 보정하고, 이를 격자기반 분포형 강우-유출 모형(KIneMatic wave STOrm Runoff Model2, KIMSTORM2)에 적용하여 보정된 자료의 효율성을 검토하는데 있다. 모형의 유출 해석은 남강댐 유역($2,293km^2$)을 대상으로 하였으며, 2016년 10월에 발생한 태풍 차바에 대하여 GPM 자료와 CM 기법을 적용한 GPM 자료를 각각 활용하여 결과를 비교하였다. 이 때, 강우자료의 보정은 유역 내 위치한 21개 지점의 지상강우자료를 활용하였으며, 각각의 위성강우자료에 유출 검보정은 남강댐 유역 내 3개의 수위관측 지점(산청, 창촌, 남강댐)을 대상으로 실시하였다. 유출 결과는 결정계수(Coefficient of determination, $R^2$), 모형 효율성 계수(Nash-Sutcliffe efficiency, NSE) 및 유출용적지수(Volume conservation index, VCI)를 이용하여 산정하였다. 지상강우자료와 CM 기법을 통해 보정한 강우자료는 대기의 많은 영향을 받는 위성자료의 특성을 보정하여 공간유출 및 첨두유출을 합리적으로 재현할 수 있을 것으로 예상된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Speech Sciences
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• v.9 no.1
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• pp.39-47
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• 2002

This paper proposes an effective method to remedy the acoustic modeling problem inherent in the usual log-normal Parallel Model Composition intended for achieving robust speech recognition. In particular, the Gaussian kernels under the prescribed log-normal PMC cannot sufficiently express the corrupted speech distributions. The proposed scheme corrects this deficiency by judiciously selecting the 'fairly' corrupted component and by re-estimating it as a mixture of two distributions using data-driven PMC. As a result, some components become merged while equal number of components split. The determination for splitting or merging is achieved by means of measuring the similarity of the corrupted speech model to those of the clean model and the noise model. The experimental results indicate that the suggested algorithm is effective in representing the corrupted speech distributions and attains consistent improvement over various SNR and noise cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4557-4572
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• 2020

The Alaska competition provides an opportunity to study the practical problems of real-world steganalysis. Participants are required to solve steganalysis involving various embedding schemes, inconsistency JPEG Quality Factor and various processing pipelines. In this paper, we propose a method to ensemble multiple deep learning steganalyzers. We select SRNet and RESDET as our base models. Then we design a three-layers model ensemble network to fuse these base models and output the final prediction. By separating the three colors channels for base model training and feature replacement strategy instead of simply merging features, the performance of the model ensemble is greatly improved. The proposed method won second place in the Alaska 1 competition in the end.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.88-96
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• 2011

Underwater acoustic communication is applicable in various areas, such as ocean data collection, undersea exploration and development, tactical surveillance, etc. Thus, robot control system construction used for underwater-robot like AUV or ROV is essential in these areas. In this paper, we propose the Message Merging MAC($M^2$-MAC) protocol, which is suitable for real time robot control system, considering energy efficiency in important parts of underwater acoustic sensor network constitution. In this proposed MAC protocol, gateway node receives the data from robot nodes according to the time slots that were allotted previously. And messages delivered from base-station are generated to one MAC frame by buffering process. Finally, generated MAC frames are broadcasted to all robot nodes in the cluster. Our suggested MAC protocol can also be hybrid MAC protocol, which is successful blend of contention based and contention-free based protocol through relevant procedure with Maintenance&Sleep (M&S) period, when new nodes join and leave as an orphan. We propose mathematical analysis model concerned about End-to-End delay and energy consumption, which is important factor in constructing real-time robot control system. We also verify the excellence of performance according to comparison of existing MAC protocols with our scheme.