• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.85-101
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• 2020

The optimal parameters for the fluid-structure interaction analysis using the Smoothed Particle Hydrodynamics (SPH) for fluids and finite elements for structures, respectively, are explored, and the effectiveness of the simulations with those parameters is validated by solving several open surface fluid problems. For the optimization of the Equation of State (EOS) and the simulation parameters such as the time step, initial particle spacing, and smoothing length factor, a dam-break problem and deflection of an elastic plate is selected, and the least squares analysis is performed on the simulation results. With the optimal values of the pivotal parameters, the accuracy of the simulation is validated by calculating the exerted force on a moving solid column in the open surface fluid. Overall, the SPH-FEM coupled simulation is very effective to calculate the fluid-structure interaction. However, the relevant parameters should be carefully selected to obtain accurate results.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.615-627
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• 2022

Principal Fitted Component (PFC) is a semi-parametric sufficient dimension reduction (SDR) method, which is originally proposed in Cook (2007). According to Cook (2007), the PFC has a connection with other usual non-parametric SDR methods. The connection is limited to sliced inverse regression (Li, 1991) and ordinary least squares. Since there is no direct comparison between the two approaches in various forward regressions up to date, a practical guidance between the two approaches is necessary for usual statistical practitioners. To fill this practical necessity, in this paper, we newly derive a connection of the PFC to covariance methods (Yin and Cook, 2002), which is one of the most popular SDR methods. Also, intensive numerical studies have done closely to examine and compare the estimation performances of the semi- and non-parametric SDR methods for various forward regressions. The founding from the numerical studies are confirmed in a real data example.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3672-3681
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• 2022

This study investigates the environmental footprint impacts of nuclear energy consumption in the presence of environmental technology and globalization of the ten largest ecological footprint countries from 1990 up to 2017. By considering a set of methods that can help solve the issue of cross-sectional dependence, we employ the Lagrange multiplier bootstrap cointegration method, Driscoll-Kraay standard errors for long-run estimation and feasible generalized least squares (FGLS) and panel-corrected standard errors (PCSE) for robustness. The finding revealed significant negative effects of nuclear energy consumption, environmental-related technology, population density and significant positive effects of globalization and economic growth on ecological footprint. These results are also robust by assessing the long-run impacts of predictors on carbon footprint and CO₂ emissions as alternate ecological measures. These conclusions provide the profound significance of nuclear energy consumption for environmentally sustainable development in the top ten ecological footprint countries and serve as an important reference for ecological security for other countries globally.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.191-196
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• 1993

Since the welding process is complex and highly nonlinear, it is very difficult to accurately model the process for real-time control. In this paper, a discrete-time transfer function matrix model for gas metal arc welding process is proposed. Although this linearized model is valid only around the operating point of interest, the adaptation mechanism employed in the control system render this model useful over a wide operating range. A multivariable one-step-ahead adaptive control strategy combined with a recursive least-squares method for on-line parameter estimation is implemented in order to achieve the desired weld bead geometries. Command following and disturbance rejection properties of the adaptive control system for both SISO and MIMO cases are investigated by simulation and experiment.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.935-940
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• 2005

This paper presents the results of a statistical analysis using historical data of cost contingency. As a result, a model that predicts and estimates an accurate cost contingency value using the least squares estimation method was developed. Data such as original contract amounts, estimated contingency amounts set by maximum funding limits, and actual contingency amounts, were collected and used for model development. The more effective prediction model was selected from the two developed models based on its prediction capability. The model would help guide project managers making financial decisions when the determination of the cost contingency amounts for multiple projects is necessary.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.383-388
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• 2004

This paper presents the aerodynamic coefficient modeling with a new model structure explored by Least Squares using Modulating Function Technique (LS/MFT) for an F-16XL airplane using wind tunnel data supplied by NASA/LRC. A new model structure for aerodynamic coefficient was proposed, one that considered all possible combination terms of angle of attack ${\alpha}$(t) and ${\alpha}$(t) given number of harmonics K, and was compared with Pearson's model, which has the same number of parameters as the new model. Our new model harmonic results show better agreement with the physical data than Pearson's model. The number of harmonics in the model was extended to 6 and its parameters were estimated by LS/MFT. The model output of lift coefficient with K=6 correspond reasonably well with the physical data. In particular, the estimation performances of four aerodynamic coefficients were greatly improved at high frequency by considering all harmonics included in the input${\alpha}$(t), and by using the new model. In addition, the importance of each parameter in the model was analyzed by parameter reduction errors. Moreover, the estimation of three parameters, i.e., amplitude, phase and frequency, for a pure sinusoid and a finite sum of sinusoids- using LS/MFT is investigated.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1429-1444
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• 2016

We suggest a monitoring procedure to detect changes in the mean of the stochastic process. The monitoring procedure is based on penalized least squares estimates. Unlike the fluctuation (FL) monitoring, we use the numbers of nonzero estimates not the fluctuations of sequential parameter estimates. We investigate the behavior of the proposed monitoring procedure by means of a simulation study and compare its performance with CUSUM monitoring.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.653-658
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• 2012

Load modeling has a significant influence on power system analysis and control. In recent years, measurement-based load modeling has been widely practiced. In the load modeling algorithm, the model structure is determined and the parameters of the established model are estimated. For parameter estimation, least-squares optimization method is applied. The model parameters are estimated so that the error between the measured values and the predicted values is to be minimized. By introducing sliding window concept, on-line load modeling method can be performed which reflects the dynamic behaviors of loads in real-time. For the purpose of data acquisition, the measurement system including PMU is implemented in university level. In this paper, case studies are performed using real PMU data from Korea Univ. and Seoul National University of Science and Technology. The performances of modeling real and reactive power behaviors using exponential and ZIP load model are evaluated.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.67-72
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. SVM also uses quadratic programming approach whose another advantage in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property In fuzzy regression. However this is not a computationally expensive way. SVM allows us to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. In particular, SVM is a very attractive approach to model nonlinear interval data. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.283-285
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• 2005

This paper presents a new two-terminal numerical algorithm for fault location estimation and for faults recognition using the synchronized phasor in time-domain. The proposed algorithm is also based on the synchronized voltage and current phasor measured from the PMUs(Phasor Measurement Units) installed at both ends of the transmission lines. Also the arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records obtained by transient recorder. From the calculated arc voltage amplitude it can make a decision whether the fault is permanent or transient. In this paper the algorithm is given and estimated using DFT(Discrete Fourier Transform) and the LES(Least Error Squares Method). The algorithm uses a very short data window and enables fast fault detection and classification for real-time transmission line protection. To test the validity of the proposed algorithm, the Electro-Magnetic Transient Program(EMTP/ATP) and MATLAB is used.