• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.54.1-54.1
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• 2019

We use iterative smoothing reconstruction method along with exploring in the parameter space of the light curves of the JLA supernova compilation (Joint Light-curve Analysis) to simultaneously reconstruct the expansion history of the universe as well as putting constrains on the light curve parameters without assuming any cosmological model. Our constraints on the light curve parameters of the JLA from our model-independent analysis seems to be closely in agreement with results assuming ΛCDM cosmology or using Chevallier-Polarski-Linder (CPL) parametrization for the equation of state of dark energy. This implies that there is no hidden significant feature in the data that could be neglected by cosmology model assumption. The reconstructed expansion history of the universe and properties of dark energy seems to be in good agreement with expectations of the standard ΛCDM model. Our results also indicate that the data allows a considerable flexibility for expansion history of the universe.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.6
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• pp.925-933
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• 2020

This study is aimed to compare stock assessment models which are effective in assessing red snow crab Chinonoecetes japonicus resources and to select and apply an effective stock assessment model in the future. In order to select an effective stock assessment model, a process-error model, observation-error model, and a Bayesian state-space model were estimated. Analytical results show that the least error is observed between the estimated CPUE (catch per unit effort) and the observed CPUE when using the Bayesian state-space model. For the Bayesian state-space model, the 95% credible interval(CI) ranges for the maximum sustainable yield (MSY), carrying capacity (K), catchability coefficient (q), and intrinsic growth (r) are estimated to be 10,420-47,200 tons, 185,200-444,800 tons, 3.81E-06-9.02E-06, and 0.14-0.66, respectively. The results show that the Bayesian state-space model was most reliable among models.

• Journal of Astronomy and Space Sciences
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• v.31 no.1
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• pp.25-31
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• 2014

There has been a rapid increase of the concern on the space radiation effect on pilots, crew and passengers at the commercial aircraft altitude (~ 10 km) recently. It is because domestic airline companies, Korean Air and Asiana Airlines have just begun operating the polar routes over the North Pole since 2006 and 2009 respectively. CARI-6 and CARI-6M are commonly used space radiation estimation programs which are provided officially by the U.S. federal aviation administration (FAA). In this paper, the route doses and the annual radiation doses for Korean pilots and cabin crew were estimated by using CARI-6M based on 2012 flight records. Also the modeling concept was developed for our own space radiation estimation program which is composed of GEANT4 and NRLMSIS00 models. The GEANT4 model is used to trace the incident particle transports in the atmosphere and the NRLMSIS00 model is used to get the background atmospheric densities of various neutral atoms at the aircraft altitude. Also presented are the results of simple integration tests of those models and the plan to include the space weather variations through the solar proton event (SPE) prediction model such as UMASEP and the galactic cosmic ray (GCR) prediction model such as Badhwar-O'Neill 2010.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.87-92
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• 2011

In this paper, we got the 800 ~ 900 MHz path loss model for Russia PNG area using the free space model and the Okumura-Hata Model. In order to add new regional properties to the existing path loss model, the mean square error technique is used to obtain the correction factor. The correction factors for the free space and the Hata model are 28, 13 dB respectively. By applying this correction factors, the new Russain PNG path loss model is proposed.

• Journal of the Korean Society of Safety
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• v.13 no.1
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• pp.70-76
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• 1998

The smoke filling process for the atrium space containing a fire source is simulated using two types of deterministic fire models : Zone model and Field model. The zone model used is the CFAST(version 1.6) model developed at the Building and Fire Research Laboratories, NIST in the USA. The field model is a self-developed fire field model based on Computational Fluid Dynamics(CFD) theories. This article is focused on finding out the smoke movement and temperature distribution in atrium space which is cubic in shape. A computational procedure for predicting velocity and temperature distribution in fire-induced flow is based on the solution, in finite volume method and non-staggered grid system, of 3-dimensional equations for the conservation of mass, momentum, energy, species and so forth. The fire model i. e. Zone model and Field model predicted similar results for the clear height and the smoke layer temperature.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.9
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• pp.794-806
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• 2017

It is essential to protect the national space assets and space environment safely as a space development country from the continuously increasing space debris. And Active Debris Removal(ADR) is the most active way to solve this problem. In this paper, we studied the Artificial Neural Network(ANN) for a stable recognition model of vision-based space debris tracking system. We obtained the simulated image of the space environment by the KARICAT which is the ground-based space debris clearing satellite testbed developed by the Korea Aerospace Research Institute, and created the vector which encodes structure and color-based features of each object after image segmentation by depth discontinuity. The Feature Vector consists of 3D surface area, principle vector of point cloud, 2D shape and color information. We designed artificial neural network model based on the separated Feature Vector. In order to improve the performance of the artificial neural network, the model is divided according to the categories of the input feature vectors, and the ensemble technique is applied to each model. As a result, we confirmed the performance improvement of recognition model by ensemble technique.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.651-661
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• 2017

A truss-braced wing (TBW) aircraft has recently received increasing attention due to higher aerodynamic efficiency compared to conventional cantilever wing aircraft. For conceptual TBW aircraft design, we developed a propulsion-and-airframe integrated design environment by replacing a semi-empirical turbofan engine model with a thermodynamic cycle-based one built upon the numerical propulsion system simulation (NPSS). The constructed NPSS model benefitted TBW aircraft design study, as it could handle engine installation effects influencing engine fuel efficiency. The NPSS model also contributed to broadening TBW aircraft design space, for it provided turbofan engine design variables involving a technology factor reflecting progress in propulsion technology. To effectively consolidate the NPSS propulsion model with the TBW airframe model, we devised a rapid, approximate substitute of the NPSS model by reduced-order modeling (ROM) to resolve difficulties in model integration. In addition, we formed an artificial neural network (ANN) that associates engine component attributes evaluated by object-oriented weight analysis of turbine engine (WATE++) with engine design variables to determine engine weight and size, both of which bring together the propulsion and airframe system models. Through propulsion-andairframe design space exploration, we optimized TBW aircraft design for fuel saving and revealed that a simple engine model neglecting engine installation effects may overestimate TBW aircraft performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.387-388
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• 2006

This article provides a dynamic analysis model for huge marine engine that examined analytically variation effects of frequency response by fitting of transverse stays such as hydraulic type. First, vibration analysis using the three dimensional finite element models for the huge marine engine has performed in order to find out the dynamic characteristics. Second, three dimensional finite elements model for the huge marine engine was modifued so that generate forcing nodes in crosshead part and top bracing nodes in cylinder frame part. Third, a system matrix and output matrix was derived for the general siso(single input single out) state space model. Finally, developed state space model for the three dimensional finite elements model for the huge marine engine without the additional modifying process.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.43.1-43.1
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• 2019

In this study, we apply a deep learning model to denoising solar magnetograms. For this, we design a model based on conditional generative adversarial network, which is one of the deep learning algorithms, for the image-to-image translation from a single magnetogram to a denoised magnetogram. For the single magnetogram, we use SDO/HMI line-of-sight magnetograms at the center of solar disk. For the denoised magnetogram, we make 21-frame-stacked magnetograms at the center of solar disk considering solar rotation. We train a model using 7004 paris of the single and denoised magnetograms from 2013 January to 2013 October and test the model using 1432 pairs from 2013 November to 2013 December. Our results from this study are as follows. First, our model successfully denoise SDO/HMI magnetograms and the denoised magnetograms from our model are similar to the stacked magnetograms. Second, the average pixel-to-pixel correlation coefficient value between denoised magnetograms from our model and stacked magnetogrmas is larger than 0.93. Third, the average noise level of denoised magnetograms from our model is greatly reduced from 10.29 G to 3.89 G, and it is consistent with or smaller than that of stacked magnetograms 4.11 G. Our results can be applied to many scientific field in which the integration of many frames are used to improve the signal-to-noise ratio.

• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.279-285
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• 2016

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.