• Structural Engineering and Mechanics
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• v.56 no.4
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• pp.549-567
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• 2015

A first-order moment method (FORM) reliability analysis is commonly used for structural stability analysis. It requires the values and partial derivatives of the performance to function with respect to the random variables for the design. These calculations can be cumbersome when the performance functions are implicit. A Gaussian process (GP)-based response surface is adopted in this study to approximate the limit state function. By using a trained GP model, a large number of values and partial derivatives of the performance functions can be obtained for conventional reliability analysis with a FORM, thereby reducing the number of stability analysis calculations. This dynamic renewed knowledge source can provide great assistance in improving the predictive capacity of GP during the iterative process, particularly from the view of machine learning. An iterative algorithm is therefore proposed to improve the precision of GP approximation around the design point by constantly adding new design points to the initial training set. Examples are provided to illustrate the GP-based response surface for both structural and non-structural reliability analyses. The results show that the proposed approach is applicable to structural reliability analyses that involve implicit performance functions and structural response evaluations that entail time-consuming finite element analyses.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.7
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1160-1169
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• 1996

In this study, an experiment of information retrieval using fuzzy connection matrix of keywords was conducted. A query for retrieval was constructed from each keyword and Boolean operator such as AND, OR, NOT. In a workstation environment, the performance of the fuzzy retrieval system was proved to be considerably effective than that of the system using the crisp set theory. And both recall ratio and precision ratio showed that the proposed technique would be a possible alternative in future information retrieval. Some special features of this experimental system were ; ranking the results in the order of connectivity, making the retrieval results correspond flexibly by changing the threshold value, trying to accord the retrieval process with the retrieval semantics by treating the averse-connectivity (fuzzy value) as a semantic approximation between kewords.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.7
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• pp.813-820
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• 2010

The vertical roller mill is important for machine grinding and mixing various crude materials in the process of producing Portland cement. A vertical roller mill is subjected to cyclic bending stress because of the roller load. Because of the cyclic bending stress, only $4{\times}10^6-8{\times}10^6$ cycles are achieved instead of $4{\times}10^7$ cycles. The stress also causes fractures at the edge of grinding path of the outer roller. The expenses incurred in repairing the grinding path amounts to 30% of the total maintenance cost. Therefore, it is desirable to redesign the vertical roller mill in order to reduce the expenses incurred in repairing the roller. In this study, artificial neural networks (ANNs) were applied in order to solve the multiobjective optimization problem for vertical roller mills by using the function approximation ability of ANNs. To learn and generalize ANNs, the maximum and minimum stresses were estimated from the results of the finite-element analysis of a vertical roller mill. Thus, ANNs could be applied to solve the multiobjective optimization problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.358-377
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• 2015

In this paper, we propose a novel approach for trajectory recovery. Our system uses a triangulation procedure for skeletonization and graph theory to extract the trajectory. Skeletonization extracts the polyline skeleton according to the polygonal contours of the handwritten characters, and as a result, the junction becomes clear and the characters that are touching each other are separated. The approach for the trajectory recovery is based on graph theory to find the optimal path in the graph that has the best representation of the trajectory. An undirected graph model consisting of one or more strokes is constructed from a polyline skeleton. By using the polyline skeleton, our approach accelerates the process to search for an optimal path. In order to evaluate the performance, we built our own dataset, which includes testing and ground-truth. The dataset consist of thousands of handwritten characters and word images, which are extracted from five handwritten documents. To show the relative advantage of our skeletonization method, we first compare the results against those from Zhang-Suen, a state-of-the-art skeletonization method. For the trajectory recovery, we conduct a comparison using the Root Means Square Error (RMSE) and Dynamic Time Warping (DTW) in order to measure the error between the ground truth and the real output. The comparison reveals that our approach has better performance for both the skeletonization stage and the trajectory recovery stage. Moreover, the processing time comparison proves that our system is faster than the existing systems.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.249-250
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• 1998

In this study, we modeled moving-actuator type Total Artificial Heart (TAH) with cardiovascular system as a form of electric circuit. The bronchial circulation, important for the imbalance between the left cardiac output and the right one, was considered and added to the model. In the model, the relations of hemodynamic variables, just as blood pressures, volumes, or flow rates of each part of body, can be expressed as simultaneous first order ordinary differential equations. To solve the equations by the numerical analysis, Runge-Kutta forth order approximation method was adopted. The simulation software (SimTAH), implemented in C++ as a window-based application program, was developed to display the hemodynamic variables and to receive control inputs from users. SimTAH was evaluated by comparison of the simulation results with the results of mock-circulation tests, in vitro.

• 한국해양학회지
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• v.24 no.3
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• pp.121-131
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• 1989

Cold water observed at sea surface near the southeastern coast of Korea in summers 1982 and 1983 was studied by using data of hydrography, sea level, wind and satellite image. In summer season when water column shows 3-layered structure a "full" upwelling occurs by southwesterly transient wind continuing for several days. During upwelling event, surface water of high temperature moved offshore, middle water of low temperature outcropped to the sea surface, and sea level was lowered, however, equilibrium depth of surface layer was not changed. It may be concluded that cold water at the surface originates from middle layer and strong surface front is a result of surfacing of seasonal thermocline. In order to see the relationship between position of surface front and wind input, a model of Csanady (1982) was applied in a rigid lid approximation. The results show that frontal position can be determined by wind input and water structure near the southeastern coast of Korea. Cold water in summer can appear at the sea surface only when there is wind larger than a minimum wind impulse of order $10m^2/sec$.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.99-104
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• 2009

We propose a waveform inversion method for SH-wave data obtained in a shallow seismic refraction survey, to determine a 2D inhomogeneous S-wave profile of shallow soils. In this method, a 2.5D equation is used to simulate SH-wave propagation in 2D media. The equation is solved with the staggered grid finite-difference approximation to the 4th-order in space and 2nd-order in time, to compute a synthetic wave. The misfit, defined using differences between calculated and observed waveforms, is minimised with a hybrid heuristic search method. We parameterise a 2D subsurface structural model with blocks with different depth boundaries, and S-wave velocities in each block. Numerical experiments were conducted using synthetic SH-wave data with white noise for a model having a blind layer and irregular interfaces. We could reconstruct a structure including a blind layer with reasonable computation time from surface seismic refraction data.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.3
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• pp.173-182
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• 2014

The mixed convolved action principle provides a new rigorous weak variational formalism for a broad range of initial boundary value problems in mathematical physics and mechanics in terms of mixed formulation, convolution, and fractional calculus. In this paper, its potential in the development of numerical methods for transient problems in various dynamical systems when adopting temporally second order approximation is investigated. For this, the classical single-degree-of-freedom linear elastic dynamical systems are primarily considered to investigate computational characteristics of the developed algorithms. For the undamped system, all the developed algorithms are symplectic with respect to the time step. For the damped system, they are shown to be accurate with good convergence characteristics.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.