• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.107-115
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• 2008

Recently, a considerable number of studies have been conducted on the high resolution imagery showing the boundaries of objects clearly. When urban areas are analyzed in detail using the high resolution imagery, the size of analyzed zone is apt to be decided arbitrarily. Sufficient prior information about study area makes the decision of analysis zone possible; otherwise, it is difficult to determine the optimized analysis zone using only satellite imagery. In this study, the variograms of artificial simple images are analyzed before applying to the real satellite images. As a result of the analysis of simple images, the sill has an effect on the density of objects and also the size of objects and spacing influence the range. The variograms of real satellite images are analyzed with reference to the result of model test and are applied to determining the optimized analysis zone. This study shows that variogram can be applied to determining effectively the optimized analysis zone in case of no prior information on study area; moreover it will be expected to be used for an index to express the characteristics of urban imagery as well as conventional kriging and simulation.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1628-1633
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• 2016

This paper derives an effective shape of the ferromagnetic pole pieces (low-speed rotor) for the reduction of transmission torque ripple in a magnetic-geared machine based on a Box-Behnken design (BBD). In particular, using a non-linear finite element method (FEM) based on 2-D numerical analysis, we conduct a numerical investigation and analysis between independent variables (selected by the BBD) and reaction variables. In addition, we derive a regression equation for reaction variables according to the independent variables by using multiple regression analysis and analysis of variance (ANOVA). We assess the validity of the optimized design by comparing characteristics of the optimized model derived from a response surface analysis and an initial model.

• Composites Research
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• v.18 no.4
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• pp.52-58
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• 2005

A cyclocopter propelled by the cycloidal blade system, which can be described as a horizontal rotary wing, is a new concept of VTOL vehicle. In this paper, optimized structure design is carried out for the aerodynamically optimized cyclocopter rotor system. Database is obtained fer design variables such as stacking sequence (ply angles), number of plies and spar locations through MSC/NASTRAN and optimum values are determined by RSM and some other optimizing processes. For the rotor system including optimized blade and composite hub m, the maximum stress by static analysis is within the failure criteria. And the rotor system is designed for the purpose of avoiding possible dynamic instabilities by inconsistency between frequencies of rotor rotation and some low natural frequencies of rotor.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1505-1509
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• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

• International Journal of Highway Engineering
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• v.12 no.2
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• pp.123-127
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• 2010

In this study, the profiles of optimized rehabilitated section was measured by a lightweight inertial profiler, and pavement smoothness was evaluated. To analyze the repeatability of the used lightweight profiler, two repeatable measurements were conducted. The agreement between two repeatable measurements were evaluated by Cross-correlation function. Pavement smoothness of the optimized rehabilitated pavement section and existing area was compared in terms of International Roughness Index and Profilograh Index. In general, the pavement smoothness of the rehabilitated sections was not good compared to the existing pavement sections. The analysis results could be used for the evaluation of pavement smoothness of the optimized rehabilitated pavement sections.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.551-560
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• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1935-1946
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• 2013

In this study, the behavior characteristics of bent pile structures with varying cross-section was examined through the measured results of field load test. A framework for determining the bending stress is calculated based on the stresses in the circumference of the pile using 3D finite element analysis. It is found that the bending stress near the pile-column joint changes rapidly and fracture zones occurs easily at variable cross-sections in bent pile structures. Also, the optimized column-pile diameter ratio was analyzed through the relationship between the column-pile diameter ratio and lateral crack load ratio. Based on this study, the optimized column-pile diameter ratio can be obtained near the inflection point of the curve between the column-pile diameter ratio and lateral crack load ratio. Therefore, a present study by considering the optimized variable cross-section condition would be improved bent pile structures design.

• Journal of Microbiology and Biotechnology
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• v.17 no.1
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• pp.89-95
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• 2007

Improvements in the dissolution of proteins in two-dimensional gel electrophoresis have greatly advanced the ability to analyze the proteomes of microorganisms under a wide variety of physiological conditions. This study examined the effect of various combinations of chaotropic agents, a reducing agent, and a detergent on the dissolution of the Streptomyces peucetius cytosolic proteins. The use of urea alone in a rehydration buffer as a chaotropic agent gave the proteome a higher solubility than any of the urea and thiourea combinations, and produced the highest resolution and clearest background in two-dimensional gel electrophoresis. Two % CHAPS, as a detergent in a rehydration buffer, improved the protein solubility. After examining the effect of several concentrations of reducing agent, 50 mM DTT in a rehydration buffer was found to be an optimal condition for the proteome analysis of Streptomyces. Using this optimized buffer condition, more than 2,000 distinct and differentially expressed soluble proteins could be resolved using two-dimensional gel electrophoresis with a pI ranging from 4-7. Under this optimized condition, 15 novel small proteins with low-level expression, which could not be analyzed under the non-optimized conditions, were identified. Overall, the optimized condition helped produce a better reference gel for Streptomyces peucetius.

• Korean Journal of Environmental Biology
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• v.22 no.2
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• pp.329-335
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• 2004

To develop a biomarker for the monitoring of the contamination of estrogenic endocrine disrupters in the aquatic environment, reverse transcription -polymerase chain reaction (RT-PCR) analysis of vitellogenin (Vg) mRNA expression was optimized in Bombina orientalis, a Korean red bellied toad species. Based on partial cDNA sequences of both Vg and beta actin genes of B. orientalis, specific primers for RT-PCR of Vg and beta actin mRNAs were developed. Semiquantitative RT-PCR of the Vg mRNA in liver was optimized using a beta actin mRNA as an internal control in both sexes. In female RT-PCR using $1\;\mu{g}$ of the liver cDNA resulted in a linear increment in the PCR product of Vg from 18 to 34 cycles of amplification. In male, on the contrary, the RT- PCR product was first detected at 30 cycles of amplification and a linear increment was observed from 30 to 40 cycles of amplification, suggesting that male B. orientalis expresses minute amount of Vg mRNA which is a $2^{-12}$ equivalent of female. In conclusion, the optimized protocol for semiquantitative RT-PCR analysis of Vg mRNA level in B. orientalis male liver will be useful for the environmental monitoring the xenoestrogen contamination in the freshwater environment in Korea.