• Advances in Computational Design
• /
• v.5 no.4
• /
• pp.349-362
• /
• 2020

Dynamic characteristics of a scale-dependent porous metal foam cylindrical shell under a traveling load have been explored within this article based on a numerical approach. Within the material texture of the metal foams, uniform and non-uniform porosities may be dispersed. Based upon differential quadrature method (DQM) and Laplace transforms, the equations of motion for a shear deformable scale-dependent shell may be solved numerically. Scale-dependent shell modeling has been provided based upon strain gradient elasticity. Solving the equations will give the shell deflection as a function of load speed. Also, it is reported that shell deflection relies on the porosity dispersion and strain gradient influences.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.2542-2547
• /
• 2005

In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic encoding algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization algorithm which is based on variable length binary encoding method. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. It is very efficient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.10 no.4
• /
• pp.477-490
• /
• 2018

Effects of inflow Reynolds number (Re), turbulence intensity (I) and pressure gradient on the transition flow over a blade section were studied using the ${\gamma}-Re{\theta}$ transition model (STAR-CCM+). Results show that the $Re_T$ (transition Re) at the transition location ($P_T$) varies strongly with Re, I and the magnitude of pressure gradient. The $Re_T$ increases significantly with the increase of the magnitude of favorable pressure gradient. It demonstrates that the $Re_T$ on different blade sections of a rotating propeller are different. More importantly, when there is strong adverse pressure gradient, the $P_T$ is always close to the minimum pressure point. Based on these conclusions, the $P_T$ on model propeller blade surface can be estimated. Numerical investigations of pressure distribution and transition flow on a propeller blade section prove these findings. Last, a simple method was proposed to estimate the $P_T$ only based on the propeller geometry and the advance coefficient.

• Smart Structures and Systems
• /
• v.22 no.3
• /
• pp.359-368
• /
• 2018

A method is proposed for the identification of instantaneous frequencies (IFs) in time-varying structures. The proposed method combines a maximum gradient algorithm and a smoothing operation. The maximum gradient algorithm is designed to extract the wavelet ridges of response signals. The smoothing operation, based on a polynomial curve fitting algorithm and a threshold method, is employed to reduce the effects of random noises. To verify the effectiveness and accuracy of the proposed method, a numerical example of a signal with two frequency modulated components is investigated and an experimental test on a steel cable with time-varying tensions is also conducted. The results demonstrate that the proposed method can extract IFs from the noisy multi-component signals and practical response signals successfully. In addition, the proposed method can provide a better IF identification results than the standard synchrosqueezing wavelet transform.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.663-667
• /
• 2004

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

• Journal of KIISE:Software and Applications
• /
• v.36 no.11
• /
• pp.901-911
• /
• 2009

In this paper, we propose a construction technique of two-level composite gradient map to automatically track a face with large movement in successive frames. Our method is composed of three main steps. First, the gradient maps with two-level resolution are generated for fast convergence of active contour. Second, to recognize the variations of face between successive frames and remove the neighbor background, weighted composite gradient map is generated by combining the composite gradient map and difference mask of previous and current frames. Third, to prevent active contour from converging local minima, the energy slope is generated by using closing operation. In addition, the fast closing operation is proposed to accelerate the processing time of closing operation. For performance evaluation, we compare our method with previous active contour model-based face tracking methods using a visual inspection, robustness test and processing time. Experimental results show that our method can effectively track the face with large movement and robustly converge to the optimal position even in frames with complicated background.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.697-699
• /
• 2004

Stochastic Neighbor Embedding(SNE) is a probabilistic method of mapping high-dimensional data space into a low-dimensional representation with preserving neighbor identities. Even though SNE shows several useful properties, the gradient-based naive SNE algorithm has a critical limitation that it is very slow to converge. To overcome this limitation, faster optimization methods should be considered by using trust region method we call this method fast TR SNE. Moreover, this paper presents a couple of useful optimization methods(i.e. conjugate gradient method and Newton's method) to embody fast SNE algorithm. We compared above three methods and conclude that TR-SNE is the best algorithm among them considering speed and stability. Finally, we show several visualizing experiments of TR-SNE to confirm its stability by experiments.

• Journal of Power System Engineering
• /
• v.9 no.4
• /
• pp.202-208
• /
• 2005

The genetic algorithm (GA) which is one of the popular optimum algorithm has been used to solve a variety of optimum problems. Because it need not require the gradient of objective function and is easier to find global solution than gradient-based optimum algorithm using the gradient of objective function. However optimum method using the GA and the finite element method (FEM) takes many computational time to solve the optimum structural design problem which has a great number of design variables, constraints, and system with many degrees of freedom. In order to overcome the drawback of the optimum structural design using the GA and the FEM, the author developed a computer program which can optimize frame structures by using the GA and the generalized transfer stiffness coefficient method. In order to confirm the effectiveness of the developed program, it is applied to optimum design of plane frame structures. The computational results by the developed program were compared with those of iterative design.

• Structural Engineering and Mechanics
• /
• v.88 no.4
• /
• pp.301-309
• /
• 2023

In this paper, a new modified conjugate gradient (MCG) method is presented which is based on a new gradient regularizer, and this method is used to identify the dynamic load on airfoil structure without and with considering random structure parameters. First of all, the newly proposed algorithm is proved to be efficient and convergent through the rigorous mathematics theory and the numerical results of determinate dynamic load identification. Secondly, using the perturbation method, we transform uncertain inverse problem about force reconstruction into determinate load identification problem. Lastly, the statistical characteristics of identified load are evaluated by statistical methods. Especially, this newly proposed approach has successfully solved determinate and uncertain inverse problems about dynamic load identification. Numerical simulations validate that the newly developed method in this paper is feasible and stable in solving load identification problems without and with considering random structure parameters. Additionally, it also shows that most of the observation error of the proposed algorithm in solving dynamic load identification of deterministic and random structure is respectively within 11.13%, 20%.

• Nuclear Engineering and Technology
• /
• v.51 no.5
• /
• pp.1181-1194
• /
• 2019

A new moving-mesh Finite Volume Method (FVM) for the efficient solution of the two-dimensional neutron diffusion equation is introduced. Many other moving-mesh methods developed to solve the neutron diffusion problems use a relatively large number of sophisticated mathematical equations, and so suffer from a significant complexity of mathematical calculations. In this study, the proposed method is formulated based on simple mathematical algebraic equations that enable an efficient mesh movement and CV deformation for using in practical nuclear reactor applications. Accordingly, a computational framework relying on a new moving-mesh FVM is introduced to efficiently distribute the meshes and deform the CVs in regions with high gradient variations of reactor power. These regions of interest are very important in the neutronic assessment of the nuclear reactors and accordingly, a higher accuracy of the power densities is required to be obtained. The accuracy, execution time and finally visual comparison of the proposed method comprehensively investigated and discussed for three different benchmark problems. The results all indicated a higher accuracy of the proposed method in comparison with the conventional fixed-mesh FVM.