• Bulletin of the Korean Chemical Society
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• 제27권2호
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• pp.211-218
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• 2006

This work deals with 24 substances composed of nitrogen-containing heterocycles. The relationships between the chromatographic retention factor (k) and those physicochemical properties which are relevant in quantitative structure-properties relationship (QSPR) studies, such as the polarizability $(\alpha)$, molar refractivity (MR), lipophilicity (logP), dipole moment $(\mu)$, total energy $(E_{tot})$, heat of formation $(\Delta H_f)$, molecular surface area $(S_M)$, and binding energy $(E_b)$, were investigated. The accuracy of the simple linear regressions between the chromatographic retention and the descriptors for all of the compounds was satisfactory (correlation coefficient, $0.8 \leq r \leq 1.0$). The QSPR models of these nitrogen-containing heterocyclic compounds could be predicted with a multiple linear regression equation having the statistical index, r = 1.000. This work demonstrated the successful application of the multiple linear approaches through the development of accurate predictive equations for retention factors in liquid chromatography.

• 한국항공우주학회지
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• 제47권4호
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• pp.274-282
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• 2019

무인항공기의 영상 기반 항법은 널리 사용되는 GPS/INS 통합 항법 시스템의 취약점을 보강할 수 있는 중요한 기술로 이에 대한 연구가 활발히 이루어지고 있다. 하지만 일반적인 영상 대조 기법은 실제 항공기 비행 상황들을 적절하게 고려하기 힘들다는 단점이 있다. 따라서 본 논문에서는 영상기반 항법을 위한 가우시안 혼합 모델 기반의 파티클 필터를 제안한다. 제안한 파티클 필터는 영상과 데이터베이스를 가우시안 혼합 모델로 가정하여 둘 간의 유사도를 이용하여 항체의 위치를 추정한다. 또한 몬테카를로 시뮬레이션을 통해 위치 추정 성능을 확인한다.

• 대한원격탐사학회지
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• 제35권1호
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• Nuclear Engineering and Technology
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• 제54권2호
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• pp.644-652
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• 2022

During the pyroprocessing operation, various signals can be collected by process monitoring (PM). These signals are utilized to diagnose process states. In this study, feasibility of using PM for nuclear safeguards of electrorefining operation was examined based on the use of machine learning for detecting off-normal operations. The off-normal operation, in this study, is defined as co-deposition of key elements through reduction on cathode. The monitored process signal selected for PM was cathode potential. The necessary data were produced through electrodeposition experiments in a laboratory molten salt system. Model-based cathodic surface area data were also generated and used to support model development. Computer models for classification were developed using a series of recurrent neural network architectures. The concept of transfer learning was also employed by combining pre-training and fine-tuning to minimize data requirement for training. The resulting models were found to classify the normal and the off-normal operation states with a 95% accuracy. With the availability of more process data, the approach is expected to have higher reliability.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.149-149
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• 2022

Accurate quantitative evaluation of baseflow contribution to streamflow is imperative to address seasonal drought vulnerability, flood occurrence and groundwater management concerns for efficient and sustainable water resources management in watersheds. Several baseflow separation algorithms using recursive filters, graphical method and tracer or chemical balance have been developed but resulting baseflow outputs always show wide variations, thereby making it hard to determine best separation technique. Therefore, the current global shift towards implementation of artificial intelligence (AI) in water resources is employed to compare the performance of deep learning models with conventional hydrograph separation techniques to quantify baseflow contribution to streamflow of Piney River watershed, Tennessee from 2001-2021. Streamflow values are obtained from the USGS station 03602500 and modeled to generate values of Baseflow Index (BI) using Web-based Hydrograph Analysis (WHAT) model. Annual and seasonal baseflow outputs from the traditional separation techniques are compared with results of Long Short Term Memory (LSTM) and simple Gated Recurrent Unit (GRU) models. The GRU model gave optimal BFI values during the four seasons with average NSE = 0.98, KGE = 0.97, r = 0.89 and future baseflow volumes are predicted. AI offers easier and more accurate approach to groundwater management and surface runoff modeling to create effective water policy frameworks for disaster management.

• Nuclear Engineering and Technology
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• 제54권11호
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• pp.4049-4061
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• 2022

TRISO-coated particle fuel is widely used in high temperature gas cooled reactors and other advanced reactors. The performance of coated fuel particle is one of the fundamental bases of reactor safety. The failure probability of coated fuel particle should be evaluated and determined through suitable fuel performance models and methods during normal and accident condition. In order to better facilitate the design of coated particle fuel, a new TRISO fuel performance code named FRAT (Fission product Release Analysis Tool) was developed. FRAT is designed to calculate internal gas pressure, mechanical stress and failure probability of a coated fuel particle. In this paper, FRAT was introduced and benchmarked against IAEA CRP-6 benchmark cases for coated particle failure analysis. FRAT's results agree well with benchmark values, showing the correctness and satisfactory applicability. This work helps to provide a foundation for the credible application of FRAT.

• 천문학회지
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• 제48권4호
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• pp.213-228
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• 2015

The Horizon Run 4 is a cosmological N-body simulation designed for the study of coupled evolution between galaxies and large-scale structures of the Universe, and for the test of galaxy formation models. Using 6300³ gravitating particles in a cubic box of L_box = 3150 h⁻¹Mpc, we build a dense forest of halo merger trees to trace the halo merger history with a halo mass resolution scale down to M_s = 2.7 × 10¹¹h⁻¹M_⊙. We build a set of particle and halo data, which can serve as testbeds for comparison of cosmological models and gravitational theories with observations. We find that the FoF halo mass function shows a substantial deviation from the universal form with tangible redshift evolution of amplitude and shape. At higher redshifts, the amplitude of the mass function is lower, and the functional form is shifted toward larger values of ln(1/σ). We also find that the baryonic acoustic oscillation feature in the two-point correlation function of mock galaxies becomes broader with a peak position moving to smaller scales and the peak amplitude decreasing for increasing directional cosine μ compared to the linear predictions. From the halo merger trees built from halo data at 75 redshifts, we measure the half-mass epoch of halos and find that less massive halos tend to reach half of their current mass at higher redshifts. Simulation outputs including snapshot data, past lightcone space data, and halo merger data are available at http://sdss.kias.re.kr/astro/Horizon-Run4.

• Nuclear Engineering and Technology
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• 제18권2호
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• pp.107-116
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• 1986

새로이 개발된 망상구조 충전탑에서의 중수증류성능해석 및 설계를 위한 정상 및 비정상상태 모델을 수립하였다. 정상상태 모델은 MESH 방정식에 각단의 압력강하를 고려하여 설정하고, Equation Tearing방법으로 그 해를 구하여 중수중류탄내의 농도, 온도 및 압력구배를 얻었다. 비정상상태 거동해석을 위하여 Cohen의 이상단 모델을 수정한 평형단 전이모델을 세웠으며, 그 모델식의 해석적 해를 구함으로써 시간에 따른 중수 농축과정을 예측할 수 있게 되었다. 설정된 모델들은 전환류 중수증류탑에서의 실험결과와 매우 잘 일치하였으며 개발된 충전물임이 높은 이론단수에 낮은 압력강하, 저체류량을 갖는 우수한 중수농축용 충전물임이 확인되었다.

• Geomechanics and Engineering
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• 제6권4호
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• pp.359-376
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• 2014

Thermal conductivity of ground has a great influence on the performance of Ground Heat Exchangers (GHEs). In general, the ground thermal conductivity significantly depends on the density (or porosity) and the moisture content since they are decisive factors that determine the interface area between soil particles which is available for heat transfer. In this study, a large number of thermal conductivity experiments were conducted for soils of varying porosity and moisture content, and a database of thermal properties for the weathered granite soils was set up. Based on the database, a 3D Curved Surface Model and an Artificial Neural Network Model (ANNM) were proposed for estimating the thermal conductivity. The new models were validated by comparing predictions by the models with new thermal conductivity data, which had not been used in developing the models. As for the 3D CSM, the normalized average values of training and test data were 1.079 and 1.061 with variations of 0.158 and 0.148, respectively. The predictions became somewhat unreliable in a low range of thermal conductivity values in considering the distribution pattern. As for the ANNM, the 'Logsig-Tansig' transfer function combination with nine neurons gave the most accurate estimates. The normalized average values of training data and test data were 1.006 and 0.954 with variations of 0.026 and 0.098, respectively. It can be concluded that the ANNM gives much better results than the 3D CSM.

• 한국멀티미디어학회논문지
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• 제9권11호
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• pp.1474-1482
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• 2006

최근 유비쿼터스 컴퓨팅 환경을 실현하기 위해 다양한 센서 노드 간을 연결하는 무선 센서 네트워크와 IPv6에 대한 많은 연구가 진행되고 있다. 그러나 유비쿼터스 서비스를 제공하기 위한 무선 센서 네트워크와 IPv6 기반의 인터넷을 연동하는 기술에 대한 연구는 미흡하다. 따라서 본 논문에서는 무선 센서 네트워크와 IPv6 기반의 인터넷을 연결하기 위한 릴레이 라우터를 게이트웨이로 이용하는 연동 모델과 싱크로 이용하는 연동 모델을 제안하고, 두 연동 모델을 비교 분석한다. 그리고 릴레이 라우터를 게이트웨이로 이용한 연동 모델을 적용하여 무선 센서 네트워크와 IPv6 기반의 KOREN(Korea advanced REsearch Network)을 연결하는 테스트베드를 개발하고, 실험을 통하여 동작을 검증한다.