• Journal of the Korean Society of Propulsion Engineers
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• v.13 no.6
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• pp.1-7
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• 2009

The gas turbine engine performance is greatly relied on its component performance characteristics. Generally, acquisition of component maps is not easy for engine purchasers because it is an expensive intellectual property of gas turbine engine supplier. In the previous work, the maps were inversely generated from engine performance deck data, but this method is limited to obtain the realistic maps due to calculated performance deck data. Therefore this work proposes newly to generate more realistic compressor map from experimental performance test data. And then a realistic compressor map can be generated form those processed data using the proposed extended scaling method at each rotational speed. Evaluation can be made through comparison between performance analysis results using the performance simulation program including the generated compressor map and on-condition monitoring performance data.

• Smart Structures and Systems
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• v.12 no.5
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• pp.523-545
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• 2013

Piezoelectric and dielectric behaviors of a piezoceramic patch adhesively centered on a carbon composite plate are identified using a robust multi-objective optimization procedure. For this purpose, the patch piezoelectric stress coupling and blocked dielectric constants are automatically evaluated for a wide frequency range and for the different identifiable behaviors. Latters' symmetry conditions are coded in the design plans serving for response surface methodology-based sensitivity analysis and meta-modeling. The identified constants result from the measured and computed open-circuit frequencies deviations minimization by a genetic algorithm that uses meta-model estimated frequencies. Present investigations show that the bonded piezoceramic patch has effective three-dimensional (3D) orthotropic piezoelectric and dielectric behaviors. Besides, the sensitivity analysis indicates that four constants, from eight, dominate the 3D orthotropic behavior, and that the analyses can be reduced to the electromechanically coupled modes only; therefore, in this case, and if only the dominated parameters are optimized while the others keep their nominal values, the resulting piezoelectric and dielectric behaviors are found to be transverse-isotropic. These results can help designing piezoceramics smart composites for various applications like noise, vibration, shape, and health control.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.9
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• pp.1499-1507
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• 2003

Physical importance of cutting temperatures has long been recognized. Cutting temperatures have strongly influenced both the tool life and the metallurgical state of machined surfaces. Temperatures in drilling processes are particularly important, because chips remain in contact with the tool for a relatively long time in a hole. Tool temperatures tend to be higher in drilling processes than in other in machining processes. This paper concerns with modeling of thermal behaviors in drilling processes as well as estimation of the cutting temperature distribution based on remote temperature measurements. One- and two-dimensional estimation problems are proposed to analyze drilling temperatures. The proposed thermal models are compared with solutions of finite element methods. Observer algorithms are developed to solve inverse heat conduction problems. In order to apply the estimation of cutting temperatures, approximation methods are proposed by using the solution of the finite element method. In two-dimensional analysis, a moving heat source according to feedrate of the drilling process is regarded as a fixed heat source with respect to the drilling location. Simulation results confirm the application of the proposed methods.

• Structural Engineering and Mechanics
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• v.12 no.5
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• pp.527-540
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• 2001

A new sort of learning algorithm named whole learning algorithm is proposed to simulate the nonlinear and dynamic behavior of RC members for the estimation of structural integrity. A mathematical technique to solve the multi-objective optimization problem is applied for the learning of the feedforward neural network, which is formulated so as to minimize the Euclidean norm of the error vector defined as the difference between the outputs and the target values for all the learning data sets. The change of the outputs is approximated in the first-order with respect to the amount of weight modification of the network. The governing equation for weight modification to make the error vector null is constituted with the consideration of the approximated outputs for all the learning data sets. The solution is neatly determined by means of the Moore-Penrose generalized inverse after summarization of the governing equation into the linear simultaneous equations with a rectangular matrix of coefficients. The learning efficiency of the proposed algorithm from the viewpoint of computational cost is verified in three types of problems to learn the truth table for exclusive or, the stress-strain relationship described by the Ramberg-Osgood model and the nonlinear and dynamic behavior of RC members observed under an earthquake.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.509-518
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• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• The KIPS Transactions:PartA
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• v.12A no.4 s.94
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• pp.321-326
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• 2005

We present an interactive cloth simulation method based on the mass-spring model, which is the most widely used one in the field of cloth animation. We focus especially on the case where relatively strong forces are applied on relatively small number of mass-points. Through distributing the forces on some specific points to the overall mass-points, our method simulates the cloth in pseudo-real time. Given a deformed cloth, we start from resolving the super-elasticity effect using Provot's dynamic inverse method [9]. In the next stage, we adjust the angles between neighboring mass-points, to finally remove the unexpected zigzags due to the previous super-elasticity resolving stage.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.604-609
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• 2002

The passive microwave remote sensing has progressed considerably in recent years. Important earth surface parameters are detected and monitored by airborne and space born radiometers. However the spatial resolution of real aperture measurements is constrained by the antenna aperture size available on orbiting platforms and on the ground. The inverse problem technique is researched in order to improve the spatial resolution of microwave scanning radiometer. We solve a two-dimensional (surface) temperature-imaging problem with a major intention to develop high-resolution methods. In this paper, the scenario for estimation of both radiometer point spread function (PSF) and target configuration is explained. The PSF of the radiometer is assumed to be unknown and estimated from the observations. The configuration and brightness temperature of targets are also estimated. To do this, we deal with the parametric modeling of observation scenario. The performance of developed algorithms is illustrated on two-dimensional experimental data obtained by the water vapor radiometer.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.59-79
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• 2021

Value at Risk (VaR) is one of the most common risk management tools in finance. Since a portfolio of several assets, rather than one asset portfolio, is advantageous in the risk diversification for investment, VaR for a portfolio of two or more assets is often used. In such cases, multivariate distributions of asset returns are considered to calculate VaR of the corresponding portfolio. Copulas are one way of generating a multivariate distribution by identifying the dependence structure of asset returns while allowing many different marginal distributions. However, they are used mainly for bivariate distributions and are not widely used in modeling joint distributions for many variables in finance. In this study, we would like to examine the performance of various copulas for high dimensional data and several different dependence structures. This paper compares copulas such as elliptical, vine, and hierarchical copulas in computing the VaR of portfolios to find appropriate copula functions in various dependence structures among asset return distributions. In the simulation studies under various dependence structures and real data analysis, the hierarchical Clayton copula shows the best performance in the VaR calculation using four assets. For marginal distributions of single asset returns, normal inverse Gaussian distribution was used to model asset return distributions, which are generally high-peaked and heavy-tailed.

• Nuclear Engineering and Technology
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• v.56 no.9
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• pp.3491-3500
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• 2024

In this work, the hybrid vibro-acoustic model reduction technique that is a physical-modal combined formulation is proposed to accelerate the finite element model updating process of the vibro-acoustic pipeline system. Particularly, the new formulation could provide an effective way of the model updating by preserving the physical DOFs for the direct calibration of the undetermined boundary conditions. The sensitivity based vibro-acoustic model updating is first conducted, and then the undetermined spring constant at the displacement boundary condition is then directly and effectively calibrated by using the proposed hybrid model reduction formulation. The proposed method is implemented in the real nuclear facility to evaluate its performance. In addition, an experimental implementation test using the inverse force identification process is also conducted to demonstrate the reliability of the generated vibro-acoustic FE model through the proposed method.

• Structural Engineering and Mechanics
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• v.72 no.6
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• pp.689-712
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• 2019

Time-series models like AR-ARX and ARMAX, provide a robust way to capture the dynamic properties of structures, and their residuals can be effectively used as features for damage detection. Even though several research papers discuss the implementation of AR-ARX and ARMAX models for damage diagnosis, they are basically been exploited so far for detecting the time instant of damage and also the spatial location of the damage. However, the inverse problem associated with damage quantification i.e. extent of damage using time series models is not been reported in the literature. In this paper, an approach to detect the extent of damage by combining the ARMAX model by formulating the inverse problem as a multi-constrained optimization problem and solving using a newly developed hybrid adaptive differential search with dynamic interaction is presented. The proposed variant of the differential search technique employs small multiple populations which perform the search independently and exchange the information with the dynamic neighborhood. The adaptive features and local search ability features are built into the algorithm in order to improve the convergence characteristics and also the overall performance of the technique. The multi-constrained optimization formulations of the inverse problem, associated with damage quantification using time series models, attempted here for the first time, can considerably improve the robustness of the search process. Numerical simulation studies have been carried out by considering three numerical examples to demonstrate the effectiveness of the proposed technique in robustly identifying the extent of the damage. Issues related to modeling errors and also measurement noise are also addressed in this paper.