• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.29-39
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• 2012

The main objective is to predict the future degradation and maintenance budget for a suspension bridge system. Bayesian inference is applied to find the posterior probability density function of the source parameters (damage indices and serviceability), given ten years of maintenance data. The posterior distribution of the parameters is sampled using a Markov chain Monte Carlo method. The simulated risk prediction for decreased serviceability conditions are posterior distributions based on prior distribution and likelihood of data updated from annual maintenance tasks. Compared with conventional linear prediction model, the proposed quadratic model provides highly improved convergence and closeness to measured data in terms of serviceability, risky factors, and maintenance budget for bridge components, which allows forecasting a future performance and financial management of complex infrastructures based on the proposed quadratic stochastic regression model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.6C
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• pp.480-486
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• 2008

The high peak-to-average power ratio (PAPR) of the transmitted signals is one of major drawbacks of the orthogonal frequency division multiplexing (OFDM). A partial transmit sequences (PTS) technique can improve the PAPR statistics of OFDM signals. However, in a PTS technique, the search complexity to select phase weighting factors increases exponentially with the number of sub-blocks. In this paper, a PTS technique with low computational complexity is presented, which adopts micro-genetic algorithm(${\mu}$-GA) as a search algorithm. A search on the phase weighting factors starts with a population of five randomly generated individuals. An elite having the largest fitness value and the other four individuals selected through the tournament selection strategy are determined, and then the next generation members are generated through the crossover operations among those. If the new generation converges, all the four individuals except the elite are randomly generated again. The search terminates when there has been no improvements on the PAPR during the predefined number of generations, or the maximum number of generations has been reached. To evaluate the performance of the proposed PTS technique, the complementary cumulative distribution functions (CCDF) of the PAPR are compared with those of the conventional PTS techniques.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.4
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• pp.567-578
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• 2002

The main purpose of this study is to investigate the dynamic behaviour of containment building in nuclear power plant excited by aircraft impact loading using a lower order 8-node solid element. The yield and failure surfaces for concrete material model is formulated on the basis of Drucker-Prager yield criteria and are assumed to be varied by taking account of the visco-plastic energy dissipation. The standard 8-node solid element has prone to exhibit the element deficiencies and the so-called B bar method proposed by Hughes is therefore adopted in this study. The implicit Newmark method is adopted to ensure the numerical stability during the analysis. Finally, the effect of different levels of cracking strain and several types of aircraft loading are examined on the dynamic behaviour of containment building and the results are quantitatively summarized as a future benchmark.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.2
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• pp.151-160
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• 2004

In the present study, Loop scheme is applied to generate smooth surfaces. To be consistent with the limit points of target surface, the initial sampling points are properly rearranged. The pointwise errors of curvature and position in the sequence of subdivision process are evaluated in the Loop subdivision scheme. A first-order shear deformable Loop subdivision triangular element which can handle transverse shear deformation of moderately thick shell are developed. The developed element is more general than the previous one based on classical shell theory, since the new one includes the effect of transverse shear deformation and has standard six degrees of freedom per node. The quartic box spline function is used as interpolation basis function. Numerical examples for the benchmark static shell problems are analyzed to assess the performance of the developed subdivision shell element and locking trouble.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.257-266
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• 2015

There is no polynomial-time algorithm that can be obtain the optimal solution for economic load dispatch problem with non-convex fuel cost functions. Therefore, electrical field uses quadratic fuel cost function unavoidably. This paper proposes a valve-point optimization (VPO) algorithm for economic load dispatch problem with non-convex fuel cost functions. This algorithm sets the initial values to maximum powers $P_i{\leftarrow}P_i^{max}$ for each generator. It then reduces the generation power of generator i with an average power cost of $_{max}\bar{c}_i$ to a valve point power $P_{ik}$. The proposed algorithm has been found to perform better than the extant heuristic methods when applied to 13 and 40-generator benchmark data. This paper consequently proves that the optimal solution to economic load dispatch problem with non-convex fuel cost functions converges to the valve-point power of each generator.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.123-131
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• 1999

In this paper, we proposed stereo matching algorithm by dynamic programming with edges emphasized. Existing algorithms show blur generally at depth discontinuities owing to smoothness constraint and non-existence of matching pixel in occlusion regions. Also it accompanies matching error by lackness of matching information in the untextured regions. This paper defines new cost function to make up for the problems occurred to existing algorithms. It is possible through deriving matching of edges in left and right images to be carried out between edge regions anf deriving that in the other regions to be peformed between the other regions. In case of the possibility that edges can be Produced in a large amount, matching between edge information adds weight to cost function in proportion to Path distance. Proposed algorithm was applied to various images obtained by convergent camera model as well as parallel camera model. As the result, proposed algorithm showed improved performance in the aspect of matching error and processing in the occlusion regions compared to existing algorithms. Also it could improve blur especially in discontinuity regions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.157-165
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• 2011

It is challenging to detect foreground objects when background includes an illumination variation, shadow or structural variation due to its motion. Basically pixel-based background models including codebook-based modeling suffer from statistical randomness of each pixel. This paper proposes an algorithm that incorporates Markov random field model into pixel-based background modeling to achieve more accurate foreground detection. Under the assumptions the distance between the pixel on the input imaging and the corresponding background model and the difference between the scene estimates of the spatio-temporally neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameters is proposed. The proposed method alternates between estimating the parameters with the intermediate foreground detection and estimating the foreground detection with the estimated parameters, after computing it with random initial parameters. Extensive experiment is conducted with several videos recorded both indoors and outdoors to compare the proposed method with the standard codebook-based algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.7
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• pp.32-38
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• 2012

In this paper, to eliminate intersymbol interference of the received signal due to multipath propagation, a parallel equalization algorithm using two error estimation functions is proposed. In the proposed algorithm, multilevel two-dimensional signals are considered as equivalent binary signals, then error signals are estimated using the sigmoid nonlinearity effective at the initial phase equalization and threshold nonlinearity with high steady-state performance. The two errors are scaled by a weight depending on the relative accuracy of the two error estimations, then two filters are updated differentially. As a result, the combined output of two filters was to be the optimum value, fast convergence at initial stage of equalization and low steady-state error level were achieved at the same time thanks to the combining effect of two operation modes smoothly. Usefulness of the proposed algorithm was verified and compared with the conventional method through computer simulations.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1171-1180
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• 2010

It is well known that the asymptotic convergence rates of nonparametric regression estimator gets worse as the dimension of covariates gets larger. One possible way to overcome this problem is reducing the dimension of covariates by using single index models. Two coefficient estimation methods in single index models are introduced. One is semiparametric least square estimation method, which tries to find approximate solution by using iterative computation. The other one is weighted average derivative estimation method, which is non-iterative method. Both of these methods offer the parametric convergence rate to normal distribution. However, practical comparison of these two methods has not been done yet. In this article, we compare these methods by examining the variances of estimators in various models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.195-198
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• 2013

Recognition and Tracking of interesting object is the significant field in Computer Vision. Mean-Shift algorithm have chronic problems that some errors are occurred when histogram of tracking area is similar to another area. in this paper, we propose to solve the problem. Each algorithm blocks skin color filtering, face detect and Mean-Shift started consecutive order assists better operation of the next algorithm. Avoid to operations of the overhead of tracking area similar to a histogram distribution areas overlap only consider the number of white pixels by running the Viola-Jones algorithm, simple arithmetic increases the convergence of the Mean-Shift. The experimental results, it comes to 78% or more of white pixels in the Mean-Shift search area, only if the recognition of the face area when it is configured to perform a Viola-Jones algorithm is tracking the object, was 100 percent successful.