• Korean Journal of Remote Sensing
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• v.13 no.2
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• pp.121-132
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• 1997

This paper describes the development of a stereo matching algorithm for extracting Digital Elevation Model(DEM) from satellite images. This matching algorithm is based on a non-linear least squares correlation estimation but has improved matching speed. The algorithm consists of three steps: matching execution, matching control and matching optimization. Each is described. The performance of the presented algorithm is quantitatively analyzed with experiments on matching probability, matching speed and matching convergence radius.

• Smart Media Journal
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• v.12 no.6
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• pp.9-14
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• 2023

The longest increasing subsequence is a fundamental problem which has been studied for a long time in computer science. In this paper, we consider the maximal increasing subsequence problem where the constraint is released from the longest to the maximal. For two kinds of increasing (monotone increasing and strictly increasing), we propose linear-time algorithms computing a maximal increasing subsequence of an input sequence from an alphabet Σ. Our algorithm for computing a maximal monotone increasing subsequence requires O(1) space and our algorithm for computing a maximal strictly increasing subsequence requires O(|Σ|) space.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.4
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• pp.161-167
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• 2008

In this paper, the optimal design of trusses using advanced analysis and genetic algorithm is performed. An advanced analysis takes into account geometric nonlinearity and material nonlinearity. The micro genetic algorithm is used as optimization technique. The weight of structures is treated as the objective function. The constraint functions are defined by load-carrying capacities and displacement requirement. The effectiveness of the proposed method is verified by comparing the results of the proposed method with those of other method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5379-5388
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• 2012

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.11
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• pp.989-996
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• 2016

This paper intends to develop the rotor track and balance (RTB) algorithm using the nonlinear RTB models and a real-coded hybrid genetic algorithm. The RTB response data computed using the trim solutions with variation of the adjustment parameters have been used to build nonlinear RTB models based on the quadratic interpolation functions. Nonlinear programming problems to minimize the track deviations and the airframe vibration responses have been formulated to find optimum settings of balance weights, trim-tab deflections, and pitch-link lengths of each blade. The results are efficiently resolved using the real-coded genetic algorithm hybridized with the particle swarm optimization techniques for convergence acceleration. The nonlinear RTB models and the optimized RTB parameters have been compared with those computed using the linear models to validate the proposed techniques. The results showed that the nonlinear models lead to more accurate models and reduced RTB responses than the linear counterpart.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.2
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• pp.55-64
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• 2000

In this paper, we propose an efficient datapath placement algorithm to minimize track density. Here, we consider each datapath element as a cluster, and merge the most strongly connected two clusters to a new cluster until only one cluster remains. As nodes in the two clusters to be merged are already linearly ordered respectively, we can merge two clusters with connecting them. The proposed algorithm produces circular linear ordering by connecting starting point and end point of the final cluster, and n different linear ordering by cutting between two contiguous elements of the circular linear ordering. Among the n different linear ordering, the linear ordering to minimize track density is final solution. In this paper, we show and utilize that if two clusters are strongly connected in a graph, the inner product of the corresponding vectors mapped in d-dimensional space using spectral method is maximum. Compared with previous datapath placement algorithm GA/S $A^{［2］}$, the proposed algorithm gives similar results with much less computation time.

• Journal of Korean Society of Steel Construction
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• v.15 no.2
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• pp.197-206
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• 2003

The optimal design method in cooperation with a nonlinear elastic analysis method was presented. The proposed nonlinear elastic method overcame the drawback of the conventional LRFD method this approximately accounts for the nonlinear effect caused by using the moment amplification factors of and. The genetic algorithm uses a procedure based on the Darwinian notions of the survival of the fittest, where selection, crossover, and mutation operators are used to look for high performance among the sections of the database. They satisfy constraint functions and give the lightest weight to the structure. The objective function was set to the total weight of the steel structure. The constraint functions were load-carrying capacities, serviceability, and ductility requirement. Case studies for a two-dimensional frame, a three-dimensional frame, and a three-dimensional steel arch bridge were likewise presented.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.43-54
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• 2004

This study analyzes characteristics and applicability of genetic algorithms and genetic operators to optimize highway alignments. Genetic algorithms, one of artificial intelligence techniques, are fast and efficient search algorithms for generating, evaluation and finding optimal highway alignment alternatives. The performance of genetic algorithms as an optimal search tool highly depends on genetic operators that are designed as a problem-specific. This study adopts low mutation operators(uniform mutation operator, straight mutation operator, non-uniform mutation operator whole non-uniform mutation operator) to explore whole search spaces, and four crossover operators(simple crossover operator, two-point crossover operator, arithmetic crossover operator, heuristic crossover operator) to exploit food characteristics of the best chromosome in previous generations. A case study and a sensitivity analysis have shown that the eight problem-specific operators developed for optimizing highway alignments enhance the search performance of genetic algorithms, and find good solutions(highway alignment alternatives). It has been also found that a mixed and well-combined use of mutation and crossover operators is very important to balance between pre-matured solutions when employing more crossover operators and more computation time when adopting more mutation operators.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.3
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• pp.61-67
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• 2001

In this paper, a method that apply LP algorithm to image for speech recognition is suggested, using both speech and image information for recogniton of korean numeral speech. The input speech signal is pre-emphasized with parameter value 0.95, analyzed for B th LP coefficients using Hamming window, autocorrelation and Levinson-Durbin algorithm. Also, a gray image signal is analyzed for 2-dimensional LP coefficients using autocorrelation and Levinson-Durbin algorithm like speech. These parameters are used for input parameters of neural network using back-propagation algorithm. The recognition experiment was carried out at each noise level, three numeral speechs, '3','5', and '9' were enhanced. Thus, in case of recognizing speech with 2-dimensional LP parameters, it results in a high recognition rate, a low parameter size, and a simple algorithm with no additional feature extraction algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.