• Interaction and multiscale mechanics
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• v.1 no.3
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• pp.339-355
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• 2008

A Galerkin meshfree method is presented for analyzing shear deformable cylindrical panels. Based upon the analogy between the cylindrical panel and the curved beam a pure bending mode for cylindrical panel is rationally constructed. The meshfree approximation employed herein is characterized by an enhanced moving least square or reproducing kernel basis function that can exactly represent the pure bending mode and thus meets the requirement of Kirchhoff mode reproducing condition. The variational form is discretized using the efficient stabilized conforming nodal integration with a smoothed nodal gradient based curvature. The resulting meshfree formulation satisfies the integration constraint for bending exactness. Moreover, it is shown here that the smoothed gradient preserves several desired properties which are valid for the standard gradient obtained by direct differentiation, such as partition of nullity and reproduction of a constant strain field. The efficacy of the proposed approach is demonstrated by two benchmark cylindrical panel examples.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.215-220
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• 1998

In order to optimize fuzzy model, we use the optimal algorithm with a hybrid type in the identification of premise parameters and standard least square method in the identification of consequence parameters of a fuzzy model. The hybrid optimal identification algorithm is carried out using a genetic algorithm and improved complex method. Also, the performance index with weighting factor is proposed to achieve a balance between the insults of performance for the training and testing data. Several numerical examples are used to evaluate the performance of the proposed model.

• Korean Journal of Food Science and Technology
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• v.28 no.1
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• pp.40-43
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• 1996

Our objective was to evaluate the potential of near-infrared reflectance spectroscopy to determine the salt content of mayonnaise. The calibration equation for salt developed using partial least square regression was compared with conventional method. 100 samples for calibration set and 40 samples for validation set were used. The multiple correlation coefficient was 0.946 and standard error of prediction was 0.017, when calibration equation was applied to validation set. Near-infrared reflectance spectroscopy method for determining salt content of mayonnaise appears quite satisfactory to evaluate nondestructively.

• YAKHAK HOEJI
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• v.46 no.5
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• pp.324-330
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• 2002

This experiment was carried out to determine non-destructively the hydrogen peroxide concentration of 3％ antiseptic hydrogen peroxide solutions by portable near-infrared (NIR) system. Hydrogen peroxide standards were prepared ranging from 0 to 25.6 w/w％ and the NIR spectra of hydrogen peroxide standard solutions were collected by using a quartz cell in 1 mm pathlength. We found the variation of absorbance band due to OH vibration of hydrogen peroxide depending on the concentration around 1400 nm in the second derivatives spectra. Partial least square regression (PLSR) and multilinear regression (MLR) were explored to develop a calibration model over the spectral range 1100-1720 nm. The model using PLSR was better than that using MLR. The calibration showed good results with a standard error of prediction (SEP) of 0.16％. In order to validate the developed calibration model, routine analyses were performed using commercial antiseptic hydrogen peroxide solutions. The hydrogen peroxide values from the NIR calibration model were compared with the values from a redox titration method. The NIR routine analyses results showed good correlation with those of the redox titration method. This study showed that the rapid and non-destructive determination of hydrogen peroxide in the antiseptic solution was successfully performed by portable NIR system without very harmful solvents.

• Analytical Science and Technology
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• v.15 no.2
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• pp.102-107
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• 2002

This paper describes a rapid determination of phenobarbital in intact phenobarbital tablets using partial least squares regression(PLSR) method of transmittance spectrum of near infrared (NIR) compared with the analytical data of liquid chromatograpy. The linearity, concentration range and precision of this analytical method are studied. The correlation coefficient of the calibration curve is 0.9983 and the standard error of calibration(SEC) is 0.14 %. Intra-day precision and Inter-day precision obtained in this method are CV = 0.45, CV =0.56, respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.140-144
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• 2003

A navigation sensor system for outdoor AGV(Automatic Guided Vehicle) using AMR(Anisotropic Magnetoresitive) sensors is described. We derive a formula of the position of AMR sensor using the measured magnetic field intensity due to permanent magnet with constant distance. The system consists of sensor board. sensor control board and position processing board. The sensor board measures magnetic field intensity, the sensor control board controls the measurement of six sensors sequentially, and the position processing board computes the accurate position of the permanent magnet using Least Square Method. We arranged six sensors at intervals of 30cm and measured the position of the permanent magnet moving at intervals of 30cm. Experimental results showed that we can get standard deviation of 2mm and error of &\pm&4.5mm at a height of 20cm from the permanent magnet.

• Journal of the Korean Applied Science and Technology
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• v.3 no.2
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• pp.23-28
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• 1986

The sulfuric acid sulfonation mixtures of 2-aminonaphthalene, contained 2-aminonaphthalene, Dahl's acid, Broenner's acid, amino-F-acid and Baden acid, can be determined quantitatively by multicomponent spectrophotometric analysis. The analysis was performed in diluted sodium hydroxide aqueous solution and based on the ultraviolet absorption of the sodium salts of each isomers. The determination of quantity of each isomers was performed by subjection the absorbances of the unknown mixture and of its constituents, gathered at a large number of wavelengths, to a least square treatment by an electronic personer computer. This method provided a rapid analysis of such complex mixture, and the standard deviation was ${\pm}1.65$ mole %.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.4
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• pp.117-122
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• 2000

This paper presents an indirect compensation of thermal errors during machining , in which thermal error is modelled as a linear regression of temperatures measured at 4 specified positions. In this regression model, weighting coefficients of the measured temperatures were estimated by using the least square method. The grinding test with compensation , after 4 -hour warning-up operation before the test, showed that the maximum machining error of the work pieces was reduced to 12${\mu}{\textrm}{m}$ while it measured by 28${\mu}{\textrm}{m}$ without compensation . Furthermore the standard deviation of machining errors was also reduced from 8${\mu}{\textrm}{m}$ to 2${\mu}{\textrm}{m}$.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.155-157
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• 2005

We propose a new category of hybrid multi-layer neural networks with hetero nodes such as Fuzzy Set based Polynomial Neurons (FSPNs) and Polynomial Neurons (PNs). These networks are based on a genetically optimized multi-layer perceptron. We develop a comprehensive design methodology involving mechanisms of genetic optimization and genetic algorithms, in particular. The augmented genetically optimized HFPNN (namely gHFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of HFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HFPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFPNNs quantified through experimentation where we use a number of modeling benchmarks-synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.673-678
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• 2000

This paper presents an indirect compensation of thermal errors during machining, in which thermal error is modeled as a linear regression of temperatures measured at 4 specified positions. In this regression model, weighting coefficients of the measured temperatures were estimated by using the least square method. The grinding test with compensation, after 4-hour warming-up operation before the test, showed that the maximum machining error of the work pieces was reduced to 12${\mu}{\textrm}{m}$ while it measured 28${\mu}{\textrm}{m}$ without compensation. Furthermore the standard deviation of machining errors was also reduced from 8${\mu}{\textrm}{m}$ to 2${\mu}{\textrm}{m}$.