• Computers and Concrete
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• v.11 no.4
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• pp.291-304
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• 2013

By considering the creep characteristics of concrete core under eccentric compression, a creep model of concrete filled steel tubes (CFT) columns under eccentric compressive loads is proposed based on the concrete creep model B3. In this proposed model, a discrete element method is introduced to transform the eccentric loading into axial loading. The validity of the model is verified by comparing the predicting results with the published creep experiments results on CFT specimens under compressive loading, together with the predicting values based on other concrete creep models, such as ACI209, CEB90, GL2000 and elastic continuation and plastic flow theory. By using the proposed model, a parameters study is carried out to analysis the effects of practical design parameters, such as concrete mix (e.g. water to cement ratio, aggregate to cement ratio), steel ratio and eccentricity ratio, on the creep of CFT columns under eccentric compressive loading.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2565-2571
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• 2020

Background: Understanding the behaviour of nuclear fuel claddings by conducting burst test on single cladding tube under simulated loss-of-coolant accident conditions and developing theoretical cum empirical predictive computer codes have been the focus of several investigations. The developed burst criterion (a) assumes symmetrical deformation of cladding tube in contrast to experimental observation (b) interpolates the properties of Zircaloy-4 cladding in mixed α+β phase (c) does not account for azimuthal temperature variations. In order to overcome all these drawbacks of burst criterion, it is reasoned that artificial intelligence technique may be a better option to predict the burst parameters. Methods: Artificial neural network models based on feedforward backpropagation algorithm with logsig transfer function are developed. Results: Neural network architecture of 2-4-4-3, that is model with two hidden layers having four nodes in each layer is found to be the most suitable. The mean, maximum, and minimum prediction errors for this optimised model are 0.82%, 19.62%, and 0.004%, respectively. Conclusion: The burst stress, burst temperature, and burst strain obtained from burst criterion have average deviation of 19%, 12%, and 53% respectively whereas the developed neural network model predicted these parameters with average deviation of 6%, 2%, and 8%, respectively.

• Environmental Engineering Research
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• v.25 no.3
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• pp.374-383
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• 2020

Landfill gas (LFG) emissions from a given amount of landfill waste depend on the carbon flows in the waste. The objective of this study was to more accurately estimate the first-order decay parameters through methane (CH₄) and carbon flow balances based on the analysis of a full-scale landfill with long-term data and detailed field records on LFG and leachate. The carbon storage factor for the case-study landfill was 0.055 g-degradable organic carbon (DOC) stored per g-wet waste and the amounts of DOC lost with the leachate were less than 1.3%. The appropriate CH₄ generation rate constant (k) for bulk waste was 0.24 y-1. The the CH₄ generation potential (L0) values ranged 33.7-46.7 m3-CH₄ Mg-1, based on the fraction of DOC that can decompose (DOCf) value of 0.40. Results show that CH4 and carbon flow balance methods can be used to estimate model parameters appropriately and to predict long-term carbon emissions from landfills.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.47.1-47.1
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• 2016

The determination of three dimensional parameters (e.g., radial speed, angular width, source location) of Coronal Mass Ejections (CMEs) is very important for space weather forecast. To estimate these parameters, several cone models based on a flat cone or a shallow ice-cream cone with spherical front have been suggested. In this study, we investigate which cone model is proper for halo CME morphology using 26 CMEs which are identified as halo CMEs by one spacecraft (SOHO or STEREO-A or B) and as limb CMEs by the other ones. From geometrical parameters of these CMEs such as their front curvature, we find that near full ice-cream cone CMEs are dominant over shallow ice-cream cone CMEs. Thus we develop a new full ice-cream cone model by assuming that a full ice-cream cone consists of many flat cones with different heights and angular widths. This model is carried out by the following steps: (1) construct a cone for given height and angular width, (2) project the cone onto the sky plane, (3) select points comprising the outer boundary, (4) minimize the difference between the estimated projection speeds with the observed ones. We apply this model to 12 SOHO halo CMEs and compare the results with those from other stereoscopic methods (a geometrical triangulation method and a Graduated Cylindrical Shell model) based on multi-spacecraft data.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.326-326
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• 2018

The study will assess the seasonal effect of hydrological models on performance and parameters for streamflow simulation. TPHM, GR4J, CAT, and TANK-SM hydrological models will be applied for simulating streamflow in ten small and large watersheds located in South Korea. The readily available hydrometeorological data will be applied as an input to the four hydrological models and the potential evapotranspiration will be computed using the Penman-Monteith equation. The SCE-UA algorithm implemented in PEST will be used to calibrate the models considering similar objective functions bedside the calibration will be renewed to capture the seasonal effects on the model performance and parameters. The seasonal effects on the model performance and parameters will be presented after assessing the four hydrologic models results. The conventional approach and season-based results will be evaluated for each model in the tested watersheds and a conclusion will be made based on the finding of the results.

• Journal of Astronomy and Space Sciences
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• v.38 no.3
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• pp.157-164
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• 2021

We describe a method for the in-orbit calibration of body-mounted magnetometers based on the CHAOS-7 geomagnetic field model. The code is designed to find the true calibration parameters autonomously by using only the onboard magnetometer data and the corresponding CHAOS outputs. As the model output and satellite data have different coordinate systems, they are first transformed to a Star Tracker Coordinate (STC). Then, non-linear optimization processes are run to minimize the differences between the CHAOS-7 model and satellite data in the STC. The process finally searches out a suite of calibration parameters that can maximize the model-data agreement. These parameters include the instrument gain, offset, axis orthogonality, and Euler rotation matrices between the magnetometer frame and the STC. To validate the performance of the Python code, we first produce pseudo satellite data by convoluting CHAOS-7 model outputs with a prescribed set of the 'true' calibration parameters. Then, we let the code autonomously undistort the pseudo satellite data through optimization processes, which ultimately track down the initially prescribed calibration parameters. The reconstructed parameters are in good agreement with the prescribed (true) ones, which demonstrates that the code can be used for actual instrument data calibration. This study is performed using Python 3.8.5, NumPy 1.19.2, SciPy 1.6, AstroPy 4.2, SpacePy 0.2.1, and ChaosmagPy 0.5 including the CHAOS-7.6 geomagnetic field model. This code will be utilized for processing NextSat-1 and Small scale magNetospheric and Ionospheric Plasma Experiment (SNIPE) data in the future.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.146.4-146
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• 2001

Various methods have been proposed for the time series prediction. Most of the conventional methods only optimize parameters of mathematical models, but to construct an appropriate functional form of the model is more difficult in the first place. We employ the Genetic Programming (GP) to construct the functional form of prediction models. Our method is distinguished because the model parameters are optimized by using Back-Propagation (BP)-like method and the prediction model includes discontinuous functions, such as if and max, as node functions for describing complicated phenomena. The above-mentioned functions are non-differentiable, but the BP method requires derivative. To solve this problem, we develop ...

• Journal of Korean Society of Water and Wastewater
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• v.21 no.4
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• pp.409-420
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• 2007

ASM No. 1 is a very useful model to analyze wastewater treatment system removing organic carbon and nitrogen material. But it isn't adequate to control the wastewater treatment system with real time since it has many material divisions and parameters. So, the purpose of this study is the simplification of ASM No. 1 to control the wastewater treatment system. ASM No. 1 was changed with the model which has 3 material divisions(COD, $NH_4{^+}$, $NO_3{^-}$) and two phases(Aerobic and Anoxic condition). SBR was running with two phases(Phase I and II). Phase II running 20 minutes with aerobic time was used for deciding model parameters and Phase I running 12 minutes with aerobic time was used for proving the simplified model. The simplified model was compared with ASM No. 1 using data of Phase I and II. As a result of model comparison, the simplified model has enough ability to express the variation of $NH_4{^+}$ compound.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.15-26
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• 2009

Bass diffusion model have played a central role in studying the diffusion of the new products since 1969, the year of publication of Bass model. Almost 750 publications based on the Bass diffusion model have explored extensions and applications. Extension models can be divided into two types. One is the model containing marketing-mix variables and the other is the model containing additional parameters. This paper presents another extension model of the latter type. Our model allows the time varying coefficients of innovation and imitation. Two pieces approximation of time varying coefficients is introduced and it's parameters are estimated based on NLS(Non-Linear Mean Square) method. Empirical studies are performed and the results show that our model is superior to the basic Bass model and the NUI(Non-Uniform Influence) model which is the well-known extension of the Bass model. The model developed in this paper is, also, transformed into the Bass model with the ready potential adopters in order to enhance the descriptive power.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.63-70
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• 2017

The purpose of this study was to calibrate the hydrologic parameters of SWAT model and analyze the daily runoff for the study watershed using SWAT-CUP. The Hardware watershed is located in Virginia, USA. The watershed area is $356.15km^2$, and the land use accounts for 73.4 % of forest and 23.2 % of pasture. Input data for the SWAT model were obtained from the digital elevation map, landuse map, soil map and others. Water flow data from 1990 to 1994 was used for calibration and from 1997 to 2005 was for validation. The SUFI-2 module of the SWAT-CUP program was used to calibrate the hydrologic parameters. The parameters were calibrated for the highly sensitive parameters presented in previous studies. The P-factor, R-factor, $R^2$, Nash-Sutcliffe efficiency (NS), and average flow were used for the goodness-of-fit measures. The applicability of the model was evaluated by sequentially increasing the number of applied parameters from 4 to 11. In this study, 10-parameter set was accepted for calibration in consideration of goodness-of-fit measures. For the calibration period, P-factor was 0.85, R-factor was 1.76, $R^2$ was 0.51 and NS was 0.49. The model was validated using the adjusted ranges of selected parameters. For the validation period, P-factor was 0.78, R-factor was 1.60, $R^2$ was 0.60 and NS was 0.57.