• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.4 s.304
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• pp.43-50
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• 2005

This paper proposes a new pattern recognition system combining the new adaptive feature weighting based on the genetic algorithm and the modified KNN(K Nearest-Neighbor) rules. The new feature weighting proposed herein avoids the overfitting and finds the Proper feature weighting value by determining the middle value of weights using GA. New GA operators are introduced to obtain the high performance of the system. Moreover, a class dependent feature weighting strategy is employed. Whilst the classical methods use the same feature space for all classes, the Proposed method uses a different feature space for each class. The KNN rule is modified to estimate the class of test pattern using adaptive feature space. Experiments were performed with the unconstrained handwritten numeral database of Concordia University in Canada to show the performance of the proposed method.

• Journal of Korean Association for Spatial Structures
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• v.1 no.1 s.1
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• pp.143-155
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• 2001

Generally, truss design has been determined by the designer's experience and intuition. But if we perform the most economical structural design we must consider not only cross-sections of members but also configurations(howe, warren and pratt types etc.) of single truss as the number of panel and truss height. The purpose of this study is to develope automated optimum design techniques for steel truss structures considering cross-sections of members and shape of trusses simultaneously. As the results, it could be possible to find easily the optimum solutions subject to design conditions at the preliminary structural design stage of the steel truss structures. In this study, the objective function is expressed as the whole member weight of trusses, and the applied constraints are as stresses, slenderness ratio, local buckling, deflection, member cross-sectional dimensions and truss height etc. The automated optimum design algorithm of this study is divided into three-level procedures. The first level on member cross-sectional optimization is performed by the sequential unconstrained minimization technique(SUMT) using dynamic programming method. And the second level about truss height optimization is applied for obtaining the optimum truss height by three-equal interval search method. The last level of optimization is applied for obtaining the optimum panel number of truss by integer programming method. The algorithm of multi-level optimization programming technique proposed in this study is more helpful for the economical design of plane trusses as well as space trusses.

• Journal of Korean Society of Steel Construction
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• v.12 no.5 s.48
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• pp.515-525
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• 2000

The purpose of this paper was to develop size & shape optimization programming algorithm of plane trusses. The optimum techniques applied in this study were extended penalty method of Sequential Unconstrained Minimization Techniques(SUMT) and direct search method with multi-variables proposed by Hooke & Jeeves. Upper mentioned two methods were used iteratively at each level of size and shape optimization routines. The design variables of size optimization were circular steel tube(structural member) diameter and thickness, those of shape optimization were joint coordinates, and the objective function was represented as total weight of truss. During the optimum design, two level procedures of size and shape optimization were interacted iteratively until the final optimum values were attained. At the previous studies about shape optimization of truss, the member sectional areas and coordinates were applied as design variables. So that they could not apply the buckling effect of compression member. In this paper, actual sizes of member and nodal coordinates are used as design variables to consider the buckling effect of compression member properly.

• Journal of Korean Society of Steel Construction
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• v.16 no.1 s.68
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• pp.123-134
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• 2004

In this research, a multilevel decomposition technique to enhance the efficiency of the configuration optimization of truss structures was proposed. On the first level, the nonlinear programming problem was formulated considering cross-sectional areas as design variables, weight, or volume as objective function and behavior under multiloading condition as design constraint. Said nonlinear programming problem was transformed into a sequential linear programming problem. which was effective in calculation through the approximation of member forces using behavior space approach. Such approach has proven to be efficient in sensitivity analysis and different form existing shape optimization studies. The modified method of feasible direction (MMFD) was used for the optimization process. On the second level, by treating only shape design variables, the optimum problem was transformed into and unconstrained optimal design problem. A unidirectional search technique was used. As numerical examples, some truss structures were applied to illustrate the applicability. and validity of the formulated algorithm.

• Geotechnical Engineering
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• v.13 no.4
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• pp.95-108
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• 1997

For normally consolidated clay, several researchers have developed a number of theoretical time factors to determine the coefficient of consolidation However, depending on the assumptions and analytical techniques, it could considerably vary even for a specific degree of consolidation. In this paper, a method is proposed to determine a consistent coefficient of consolidation over all ranges of degree of consolidation by applying the concept of the Optimum Design Technique. The initial excess pore pressure distribution is assumed to be obtainable by the successive spherical cavity expansion theory. The dissipation of pore pressure is simulated by means of two dimensional linear-uncoupled axisymmetric consolidation analysis. The minimization of the differences between the measured and the predicted excess pore pressures was carried by BFGS unconstrained optimum design algorithm with one dimensional golden section search technique. By analyzing numerical and real field examples, it can be found that the adopted optimum technique gives a consistent and convergent results.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.155-160
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• 2023

This paper presents a method for predicting blood pressure using the photoplethysmography signals. First, after measuring the optical blood flow signal, artifacts are removed through a preprocessing process, and a signal for learning is obtained. In addition, weight and height, which affect blood pressure, are measured as additional information. Next, a system is built to estimate systolic and diastolic blood pressure by learning the photoplethysmography signals, height, and weight as input variables through an artificial intelligence algorithm. The constructed system predicts the systolic and diastolic blood pressures using the inputs. The proposed method can continuously predict blood pressure in real time by receiving photoplethysmography signals that reflect the state of the heart and blood vessels, and the height and weight of the subject in an unconstrained method. In order to confirm the usefulness of the artificial intelligence-based blood pressure prediction system presented in this study, the usefulness of the results is verified by comparing the measured blood pressure with the predicted blood pressure.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.2
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• pp.123-133
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• 2006

The main objective of this study is to develop an optimization algorithm of framed structures with rigid and various semi-rigid connections using the multilevel dynamic programming and the sequential unconstrained minimization techniques (SUMT). The second-order elastic analysis is performed for steel framed structures. The second order elastic analysis is developed based on nonlinear beam-column theory considering the bowing effect. The following semi-rigid connections are considered; double web angle, top-seat angle and top-seat angle with web angle. We considered the three connection models, such as modified exponential, polynomial and three parameter model. The total weight of the structural steel is used as the objective function in the optimization process. The dimensions of steel cross section are selected as the design variables. The design constraints consist of strength requirements for axial, shear and flexural resistance and serviceability requirements.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.8
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• pp.975-981
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• 2013

A robust optimization is only one of the ways to minimize the effects of variances in design variables on the objective functions at the preliminary design stage. To predict the variances and to formulate the probabilistic constraints are the most important procedures for the robust optimization formulation. Though several methods such as the process capability index and the six sigma technique were proposed for the prediction and formulation of the variances and probabilistic constraints, respectively, there are few attempts using a percent defective which has been widely applied in the quality control of the manufacturing process for probabilistic constraints. In this study, the robust optimization for a lower control arm of automobile vehicle was carried out, in which the design space showing the mean and variance sensitivity of weight and stress was explored before robust optimization for a lower control arm. The 2nd order Taylor expansion for calculating the standard deviation was used to improve the numerical accuracy for predicting the variances. Simplex algorithm which does not use the gradient information in optimization was used to convert constrained optimization into unconstrained one in robust optimization.

• The Journal of the Acoustical Society of Korea
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• v.7 no.4
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• pp.31-53
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• 1988

In this work we study extensively the structures and performance characteristics of the block least mean-square (BLMS) adaptive digital filters (ADF's) that can be realized efficiently using the fast Fourier transform (FFT). The weights of a BLMS ADF realized using the FFT can be adjusted either in the time domain or in the frequency domain, leading to the time-domain BLMS(TBLMS) algorithm or the frequency-domain BLMS (FBLMS) algorithm, respectively. In Part Ⅰof the paper, we first present new results on the overlap-add realization and the number-theoretic transform realization of the FBLMS ADF's. Then, we study how we can incorporate the concept of different frequency-weighting on the error signals and the self-orthogonalization of weight adjustment in the FBLMS ADF's , and also in the TBLMS ADF's. As a result, we show that the TBLMS ADF can also be made to have the same fast convergence speed as that of the self-orthogonalizing FBLMS ADF. Next, based on the properties of the sectioning operations in weight adjustment, we discuss unconstrained FBLMS algorithms that can reduce two FFT operations both for the overlap-save and overlap-add realizations. Finally, we investigate by computer simulation the effects of different parameter values and different algorithms on the convergence behaviors of the FBLMS and TBLMS ADF's. In Part Ⅱ of the paper, we will analyze the convergence characteristics of the TBLMS and FBLMS ADF's.

• The Journal of the Acoustical Society of Korea
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• v.7 no.4
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• pp.54-76
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• 1988

In Part Ⅰ of the paper, we have developed various block least mean-square (BLMS) adaptive digital filters (ADF's) based on a unified matrix treatment. In Part Ⅱ we analyze the convergence behaviors of the self-orthogonalizing frequency-domain BLMS (FBLMS) ADF and the unconstrained FBLMS (UFBLMS) ADF both for the overlap-save and overlap-add sectioning methods. We first show that, unlike the FBLMS ADF with a constant convergence factor, the convergence behavior of the self-orthogonalizing FBLMS ADF is governed by the same autocorrelation matrix as that of the UFBLMS ADF. We then show that the optimum solution of the UFBLMS ADF is the same as that of the constrained FBLMS ADF when the filter length is sufficiently long. The mean of the weight vector of the UFBLMS ADF is also shown to converge to the optimum Wiener weight vector under a proper condition. However, the steady-state mean-squared error(MSE) of the UFBLMS ADF turns out to be slightly worse than that of the constrained algorithm if the same convergence constant is used in both cases. On the other hand, when the filter length is not sufficiently long, while the constrained FBLMS ADF yields poor performance, the performance of the UFBLMS ADF can be improved to some extent by utilizing its extended filter-length capability. As for the self-orthogonalizing FBLMS ADF, we study how we can approximate the autocorrelation matrix by a diagonal matrix in the frequency domain. We also analyze the steady-state MSE's of the self-orthogonalizing FBLMS ADF's with and without the constant. Finally, we present various simulation results to verify our analytical results.