• The Korean Journal of Nuclear Medicine
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• v.37 no.5
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• pp.288-300
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• 2003

Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.5
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• pp.779-789
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• 2016

The effective rainfall is calculated considering the soil moisture. It utilizes observed data directly in order to incorporate the soil moisture into the rainfall-runoff model, or it calculates indirectly within the model. The rainfall-runoff model, IHACRES, used in this study computes the catchment wetness index (CWI) first varying with temperature and utilize it for estimating precipitation loss. The nonlinear relationship between the CWI and the effective rainfall in the Hapcheondam watershed was derived and utilized for the long-term runoff calculation. The effects of variable and constant CWI during calibration and validation were suggested by flow regime. The results show the variable CWI is generally more effective than the constant CWI. The $R^2$ during high flow period shows relatively higher than the ones during normal or low flow period, but the difference between cases of the variable and constant CWI was insignificant. The results indicates that the high flow is relatively less sensitive to the evaporation and soil moisture associated with temperature. On the other hand, the variable CWI gives more desirable results during normal and low flow periods which means that it is crucial to incorporate evaporation and soil moisture depending on temperature into long-term continuous runoff simulation. The NSE tends to decrease during high flow period with high variability which could be natural because NSE index is largely influenced by outliers of underlying variable. Nevertheless overall NSE shows satisfactory range higher than 0.9. The utilization of variable CWI during normal and low flow period would improve the computation of long-term rainfall-runoff simulation.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.4
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• pp.71-80
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• 2009

Although desire for living without hazardous damages grows these days, threats from natural disasters which we are currently exposed to are quiet different from what we have experienced. To cope with this changing situation, it is necessary to assess the characteristics of the natural disasters. Therefore, the main purpose of this research is to suggest a methodology to estimate the potential property loss and assess the flood risk using a regional regression analysis. Since the flood damage mainly consists of loss of lives and property damages, it is reasonable to express the results of a flood risk assessment with the loss of lives and the property damages that are vulnerable to flood. The regional regression analysis has been commonly used to find relationships between regional characteristics of a watershed and parameters of rainfall-runoff models or probability distribution models. In our research, however, this model is applied to estimate the potential flood damage as follows; 1) a nonlinear model between the flood damage and the hourly rainfall is found in gauged regions which have sufficient damage and rainfall data, and 2) a regression model is developed from the relationship between the coefficients of the nonlinear models and socio-economic indicators in the gauged regions. This method enables us to quantitatively analyze the impact of the regional indicators on the flood damage and to estimate the damage through the application of the regional regression model to ungauged regions which do not have sufficient data. Moreover the flood risk map is developed by Flood Vulnerability Index (FVI) which is equal to the ratio of the estimated flood damage to the total regional property. Comparing the results of this research with Potential Flood Damage (PFD) reported in the Long-term Korea National Water Resources Plan, the exports' mistaken opinions could affect the weighting procedure of PFD, but the proposed approach based on the regional regression would overcome the drawback of PFD. It was found that FVI is highly correlated with the past damage, while PFD does not reflect the regional vulnerabilities.

• Journal of the Korean Geotechnical Society
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• v.25 no.12
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• pp.87-105
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• 2009

Nonlinear dynamic deformation characteristics, expressed in terms of normalized shear modulus reduction curve (G/$G_{max}-\log\gamma$, G/$G_{max}$ curve) and damping curve (D-$\log\gamma$), are important input parameters with shear wave velocity profile ($V_s$-profile) in the seismic analysis of (new or existing) fill dam. In this paper, the reasonable and economical methods to evaluate the nonlinear dynamic deformation characteristics for core zone and rockfill zone respectively are presented. For the core zone, 111 G/$G_{max}$ curves and 98 damping curves which meet the requirements of core material were compiled and representative curves and ranges were proposed for the three ranges of confining pressure (0~100 kPa, 100 kPa~200 kPa, more than 200 kPa). The reliability of the proposed curves for the core zone were verified by comparing with the resonant column test results of two kinds of core materials. For the rockfill zone, 135 G/$G_{max}$ curves and 65 damping curves were compiled from the test results of gravelly materials using large scale testing equipments. The representative curves and ranges for G/$G_{max}$ were proposed for the three ranges of confining pressure (0~50 kPa, 50 kPa~100 kPa, more than 100 kPa) and those for damping were proposed independently of confining pressure. The reliability of the proposed curves for the rockfill zone were verified by comparing with the large scale triaxial test results of rockfill materials in the B-dam which is being constructed.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.56-64
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• 1995

The least mean fourth (LMF) adaptive algorithm is a stochastic gradient method that minimizes the error in the mean fourth sense. Despite its potential advantages, the algorithm is much less popular than the conventional least mean square (LMS) algorithm in practice. This seems partly because the analysis of the LMF algorithm is much more difficult than that of the LMS algorithm, and thus not much still has been known about the algorithm. In this paper, we explore the statistical convergence behavior of the LMF algorithm when the input to the adaptive filter is zero-mean, wide-sense stationary, and Gaussian. Under a system idenrification mode, a set of nonlinear evolution equations that characterizes the mean and mean-squared behavior of the algorithm is derived. A condition for the conbergence is then found, and it turns out that the conbergence of the LMF algorithm strongly depends on the choice of initial conditions. Performances of the LMF algorithm are compared with those of the LMS algorithm. It is observed that the mean convergence of the LMF algorithm is much faster than that of the LMS algorithm when the two algorithms are designed to achieve the same steady-state mean-squared estimation error.

• Journal of the Korea Concrete Institute
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• v.22 no.3
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• pp.381-388
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• 2010

In this study, characteristics of the seismic response of the non-earthquake resistant reinforced concrete (RC) frame were identified. The test building is designed to withstand only gravity loads and not in compliance with modern seismic codes. Smooth bars were utilized for the reinforcement. Members are provided with minimal amount of stirrups to withstand low levels of shear forces and the core concrete is virtually not confined. Columns are slender and more flexible than beams, and beam-column connections were built without stirrups. Through the modeling of an example RC frame, the feasibility of the fiber elementbased 3D nonlinear analysis method was investigated. Since the torsion is governed by the fundamental mode shape of the structure under dynamic loading, pushover analysis cannot predict torsional response accurately. Hence, dynamic response history analysis is a more appropriate analysis method to estimate the response of an asymmetric building. The latter method was shown to be accurate in representing global responses by the comparison of the analytical and experimental results. Analytical models without rigid links provided a good estimation of reduced stiffness and strength of the test structure due to bond-slip, by forming plastic hinges closer to the column ends. However, the absence of a proper model to represent the bond-slip poased the limitations on the current inelastic analysis schemes for the seismic analysis of buildings especially for those with round steel reinforcements. Thus, development of the appropriate bond-slip model is in need to achieve more accurate analysis.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Journal of Animal Science and Technology
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• v.54 no.6
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• pp.395-400
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• 2012

This study was conducted to estimate the growth curve parameters for the body weight (BW) and body length (BL) of miniature pigs in Korea. Growth curve parameters were estimated through a nonlinear regression model using Gompertz, Logistic, and von Bertalanffy methods. A total of 25 piglets were measured monthly from birth up to 15 months of age to estimate both body weight and length. Results showed that the estimated average values for the body weight (body length) were 31.83 kg (58.77 cm) for the mature weight (A), 3.06 (1.74) for the growth ratio (${\beta}$), and 0.28 (0.52) for the maturing rate (${\kappa}$). Average inflection points showing maximum growth rate estimated each month for body weight were 3.97 kg and 11.70 cm, while for the body length were 1.06 kg and 21.61 cm. Moreover, the estimated maturation rates of the body weight and length for the group of Sire 1 were 0.22 and 0.40 respectively, whereas for the group of Sire 2 these values were 0.34 and 0.39. On the other hand, for the groups of Dam 1, Dam 2, and Dam 3, maturation rates for their body weights were 0.26, 0.28 and 0.33 respectively, while for their body lengths these values were 0.43, 0.37, and 0.38, respectively. The study also indicated a negative relationship between the values of mature weight and maturity rate for the body weight will result to a higher inflection point which is in contrast for the body length where results show that a positive relationship between the values of mature length and the maturity rate will result to a higher inflection point. Furthermore, the growth performance of miniature pig varies across stages but using these estimated growth curve parameters could improve the genetic traits of miniature pig.

• Journal of the Korean Geotechnical Society
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• v.36 no.10
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• pp.21-31
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• 2020

In this study, Hoek-Brown constants (m_i) were calculated through nonlinear regression analyses using the results of the triaxial compression tests for the basaltic intact rocks in Jeju Island. The relationships of the m_i with the uniaxial compressive strength (UCS), Brazilian tensile strength (BTS) and UCS/BTS of the Jeju basalts were investigated, respectively. In addition, a method that can be used in determining Hoek-Brown failure envelopes including the tensile and compressive failures of the Jeju basalts has been proposed. As results, the m_i values had no clear correlations with the UCS, BTS and UCS/BTS of the Jeju basalts, but there were two strong correlations between UCS and m_i/UCS, and between BTS and m_i/BTS of the Jeju basalts. In addition, it was found that the tensile strengths calculated by the Hoek-Brown failure criterion underestimate the tensile strengths of the Jeju basalts through the relationship between the m_i and UCS/BTS of the Jeju basalts. The method presented in this study is considered to be useful in determining the Hoek-Brown failure envelope for the tensile and compressive failures of the Jeju basalts.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.5
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• pp.1025-1037
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• 2015

Flexible base isolation bearings that separate superstructure from ground have been widely used in the construction field because they make a significant contribution to increasing the fundamental period of the structure, thereby decreasing response acceleration transmitted into the superstructure. However, the established bearing devices installed to uphold the whole building give rise to some problems involved with failure and collapse due to lack of the capacity as modern structures are getting more massive and higher. Therefore, this study suggests new isolation bearings assembled with additional restrainers enabled to reinforcing and recentering, and then evaluates their performance to withstand the seismic load. The superelastic shape memory alloy (SMA) bars are installed into the conventional lead-rubber bearing (LRB) devices in order to provide recentering forces. These new systems are modeled as component spring models for the purpose of conducting nonlinear dynamic analyses with near fault ground motion data. The LRB devices with steel bars are also designed and analyzed to compare their responses with those of new systems. After numerical analyses, ultimate strength, maximum displacement, permanent deformation, and recentering ratio are compared to each model with an aim to investigate which base isolation models are superior. It can be shown that LRB models with superelastic SMA bars are superior to other models compared to each other in terms of seismic resistance and recentering effect.