• Steel and Composite Structures
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• v.50 no.1
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• pp.15-24
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• 2024

This research presents dynamical reaction investigation of pore-dependent and nano-thickness beams having functional gradation (FG) constituents exposed to a movable particle. The nano-thickness beam formulation has been appointed with the benefits of refined high orders beam paradigm and nonlocal strain gradient theory (NSGT) comprising two scale moduli entitled nonlocality and strains gradient modulus. The graded pore-dependent constituents have been designed through pore factor based power-law relations comprising pore volumes pursuant to even or uneven pore scattering. Therewith, variable scale modulus has been thought-out until process a more accurate designing of scale effects on graded nano-thickness beams. The motion equations have been appointed to be solved via Ritz method with the benefits of Chebyshev polynomials in cosine form. Also, Laplace transform techniques help Ritz-Chebyshev method to obtain the dynamical response in time domain. All factors such as particle speed, pores and variable scale modulus affect the dynamical response.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.629-640
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• 2010

The exemplar-based inpainting model is widely used to remove objects from natural images and to restore a damaged region. This paper presents a method which improves the performance of the conventional exemplar-based inpainting model by modifying three major parts in the model: data term, confidence term and patch selection. While the conventional data term is calculated using the local gradient, the proposed method uses 16 compass masks to get the global gradient to make the method robust to noise. To overcome the problem that the confidence term gets negligible in the inside of the eliminated region, a method is proposed which makes the confidence term decrease slowly in the eliminated region. The patch selection procedure is modified so that the closer patch has higher weight. Experiments showed that the proposed method produced more natural images and lower reconstruction error than the conventional exemplar-based inpainting.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.10C
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• pp.1402-1413
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• 2004

In non-rigid image registration, the Jacobian determinant of the estimated deformation should be positive everywhere since physical deformations are always invertible. We propose a constrained optimization technique at ensures the positiveness of Jacobian determinant for cubic B-spline based deformation. We derived sufficient conditions for positive Jacobian determinant by bounding the differences of consecutive coefficients. The parameter set that satisfies the conditions is convex; it is the intersection of simple half spaces. We solve the optimization problem using a gradient projection method with Dykstra's cyclic projection algorithm. Analytical results, simulations and experimental results with inhale/exhale CT images with comparison to other methods are presented.

• Transactions of Materials Processing
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• v.31 no.4
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• pp.173-178
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• 2022

The present study employed two different machine-learning approaches, the extreme gradient boosting (XGB) and light gradient boosting machine (LGBM), to predict a compressive deformation behavior of additively manufactured Ti-6Al-4V. Such approaches have rarely been verified in the field of metallurgy in contrast to artificial neural network and its variants. XGB and LGBM provided a good prediction for elongation to failure under an extrapolated condition of processing parameters. The predicting accuracy of these methods was better than that of response surface method. Furthermore, XGB and LGBM with optimum hyperparameters well predicted a deformation behavior of Ti-6Al-4V additively manufactured under the extrapolated condition. Although the predicting capability of two methods was comparable, LGBM was superior to XGB in light of six-fold higher rate of machine learning. It is also noted this work has verified the LGBM approach in solving the metallurgical problem for the first time.

• Steel and Composite Structures
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• v.46 no.2
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• pp.153-173
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• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.

• Steel and Composite Structures
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• v.32 no.2
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• pp.157-171
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• 2019

In this research, the dynamic instability region (DIR) of the sandwich nano-beams are investigated based on nonlocal strain gradient elasticity theory (NSGET) and various higher order shear deformation beam theories (HSDBTs). The sandwich piezoelectric nano-beam is including a homogenous core and face-sheets reinforced with functionally graded (FG) carbon nanotubes (CNTs). In present study, three patterns of CNTs are employed in order to reinforce the top and bottom face-sheets of the beam. In addition, different higher-order shear deformation beam theories such as trigonometric shear deformation beam theory (TSDBT), exponential shear deformation beam theory (ESDBT), hyperbolic shear deformation beam theory (HSDBT), and Aydogdu shear deformation beam theory (ASDBT) are considered to extract the governing equations for different boundary conditions. The beam is subjected to thermal and electrical loads while is resting on Visco-Pasternak foundation. Hamilton principle is used to derive the governing equations of motion based on various shear deformation theories. In order to analysis of the dynamic instability behaviors, the linear governing equations of motion are solved using differential quadrature method (DQM). After verification with validated reference, comprehensive numerical results are presented to investigate the influence of important parameters such as various shear deformation theories, nonlocal parameter, strain gradient parameter, the volume fraction of the CNTs, various distributions of the CNTs, different boundary conditions, dimensionless geometric parameters, Visco-Pasternak foundation parameters, applied voltage and temperature change on the dynamic instability characteristics of sandwich piezoelectric nano-beam.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.5
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• pp.680-687
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• 2014

PGA(phase gradient autofocus) has been widely used to remove motion induced phase errors in the ISAR(inverse synthetic aperture radar) imaging. The critical process for the processing time and image quality is windowing stage in PGA. In this paper, the new method to determine window size based on polynomial least square approximation is proposed. Moreover, dominant range bins are selected for efficient phase error estimation, which improve image quality and speed up convergence. The simulation results show that the proposed algorithm provides high quality ISAR images while computational efficiency of inherent PGA is retained.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.247-252
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• 2015

Adaptive algorithms based on the criterion of zero-error probability (ZEP) have robustness to impulsive noise and their decision feedback (DF) versions are known to compensate effectively for severe multipath channel distortions. However the ZEP-DF algorithm computes several summation operations at each iteration time for each filter section and this plays an obstacle role in practical implementation. In this paper, the ZEP-DF with recursive gradient estimation (RGE) method is proposed and shown to reduce the computational burden of O(N) to a constant which is independent of the sample size N. Also the weight update of the initial state and the steady state is a continuous process without bringing about any propagation of gradient estimation error in DF structure.

• Steel and Composite Structures
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• v.42 no.3
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• pp.397-405
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• 2022

The existing researches on the dynamics of the fluid-conveying pipes only focus on stability and vibration problems, and there is no literature report on the wave propagation of the fluid-conveying pipes. Therefore, the purpose of this paper is to explore the propagation characteristics of longitudinal and flexural waves in the fluid-conveying pipes. First, it is assumed that the material properties of the fluid-conveying pipes vary based on a power function of the thickness. In addition, it is assumed that the material properties of both the fluid and the pipes are closely depended on temperature. Using the Euler-Bernoulli beam equation and based on the linear theory, the motion equations considering the thermal-mechanical-fluid coupling is derived. Then, the exact expressions of phase velocity and group velocity of longitudinal waves and bending waves in the fluid-conveying pipes are obtained by using the eigenvalue method. In addition, we also studied the free vibration frequency characteristics of the fluid-conveying pipes. In the numerical analysis, we successively studied the influence of temperature, functional gradient index and liquid velocity on the wave propagation and vibration problems. It is found that the temperature and functional gradient exponent decrease the phase and group velocities, on the contrary, the liquid flow velocity increases the phase and group velocities. However, for vibration problems, temperature, functional gradient exponent parameter, and fluid velocity all reduce the natural frequency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.3
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• pp.266-272
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• 2004

In this paper, an observer-based adaptive controller is proposed to control the longitudinal motion of vehicles. The standard gradient method will be used to estimate the vehicle parameters such as mass, time constant, etc. The nonlinear model between the driving force and the vehicle acceleration will be chosen to design the state observer for the vehicle velocity and acceleration. It will be shown that the proposed observer is exponentially stable, and that the adaptive controller proposed in this paper is stable by the Lyapunov function candidate. It will be proved that the errors of the relative distance, velocity and acceleration converge to zero asymptotically fast, and that the overall system is also asymptotically stable. The simulation results are presented to investigate the effectiveness of the proposed method.