• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.2
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• pp.164-173
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• 1994

A numerical model to solve mild slope equation is developed by use of a preconditioned conjugate gradient method (PCGM). In the present paper. accurate boundary conditions and a better preconditioner are employed which are improved from the existing method of Panchang et al. (1991). Computational procedures are focused on weakly nonlinear waves, and emerged problems to make a more accurate model are discussed. The results of model are tested against laboratory results of both circular and elliptic shoals. Model results of wave amplitude show excellent agreement with laboratory data and thes thus model can be used as a powerful tool to calculate wave transformation in shallow waters with complex bathymetry.

• Journal of Family Resource Management and Policy Review
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• v.5 no.2
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• pp.13-27
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• 2001

The purpose of this study was to analyze which factor, influenced their change from pre-wage group to family business group. The subject of study classified by wage earner group and family business group. Independent variables effected by transform to family business group are classified by personal variables and workable variables. The major findings of this study are as followings: Sex, age, marriage of personal variables and pre-work time, pre-wage, wholesale - retail business, restaurant business and person service business of pre-industry of work variables had significant effect on transform to family business. Especially, Marriage group and low pre-wage income group significant effected on transfer to family business. By this, personal to transfer to family business can conjugate information of search and choice decision and can be used as a valuable data for future family business study.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.134-136
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• 1993

This paper presents a novel and efficient method to calculate the critical point of voltage collapse. Conjugate gradient and modified Newton-Raphson methods are employed to calculate two pairs of multiple load flow solutions for two operating conditions, i.e., both +mode and -mode voltages for two loading conditions respectively. Then these four voltage magnitude-load data sets of the bus which is most susceptible to voltage collapse, are fitted to third order polynomial using Lagrangian interpolation in order to represent approximate nose curve (P-V curve). This nose curve locates first estimate of the critical point of voltage collapse. The procedure described above is repeated near the critical point and the new estimate will be very close to the critical point. The proposed method is tested for the eleven bus Klos-Kerner system, with good accuracy and fast computation time.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.3 s.246
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• pp.270-277
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• 2006

Inverse radiation problems were solved for estimating boundary temperature distribution in a way of function estimation approach in an axisymmetric absorbing, emitting and scattering medium, given the measured radiative data. In order to reduce the computational time fur the calculation of sensitivity matrix, automatic differentiation and Broyden combined method were adopted, and their computational precision and efficiency were compared with the result obtained by finite difference approximation.. In inverse analysis, the effects of the precision of sensitivity matrix, the number of measurement points and measurement error on the estimation accuracy had been inspected using quasi-Newton method as an inverse method. Inverse solutions were validated with the result acquired by additional inverse methods of conjugate-gradient method or Levenberg-Marquardt method.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.27 no.6
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• pp.787-793
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• 1994

It has been demonstrated for the finite-difference ocean circulation model that the problem of uncertain forcing and input data can be tackled with an optimization techniques. The uncertainty problem in interesting flow properties is exploring a finite difference ocean circulation model due to the uncertainty in the driving boundary conditions. The mathematical procedure is based upon optimization method by the conjugate gradient method using the simulated data and a simple barotropic model. An example for the ocean circulation model is discussed in which wind forcing and the steady-state circulation are determined from a simulated stream function.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.5
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• pp.191-199
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• 1993

Investigation is given to the detailed procedure of a computer assisted automatic technique for ortho-image generation from digital stereo image data of close-range photographs scanned by the CCD camera scanner. After rectification of geometric scanning errors, the bundle adjustment technique was used to determine the exterior orientation parameters of terrestrial camera. An automatic correlation matching technique was applied to search for the conjugate pixels in digital stereo pairs. And the 3-dimensional coordinates of the corresponding pixels were calculated by the space intersection method. For the generation of ortho-image from the calculated coordinates and right image data values, inverse-weighted-distance average method was used. And the accuracy of the resulting ortho-image was checked by comparing its image coordinates with there corresponding ground coordinates for the check points.

• International Journal of Automotive Technology
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• v.3 no.2
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• pp.71-78
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• 2002

A new control method far vision-based autonomous vehicle is proposed to determine navigation direction by analyzing lane information from a camera and to navigate a vehicle. In this paper, characteristic featured data points are extracted from lane images using a lane recognition algorithm. Then the vehicle is controlled using new Levenberg-Marquardt neural network algorithm. To verify the usefulness of the algorithm, another algorithm, which utilizes the geometric relation of a camera and vehicle, is introduced. The second one involves transformation from an image coordinate to a vehicle coordinate, then steering is determined from Ackermann angle. The steering scheme using Ackermann angle is heavily depends on the correct geometric data of a vehicle and a camera. Meanwhile, the proposed neural network algorithm does not need geometric relations and it depends on the driving style of human driver. The proposed method is superior than other referenced neural network algorithms such as conjugate gradient method or gradient decent one in autonomous lateral control .

• ETRI Journal
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• v.33 no.4
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• pp.517-527
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• 2011

Currently, there is a considerable interest in 3D object reconstruction using terrestrial laser scanner (TLS) systems due to their ability to automatically generate a considerable amount of points in a very short time. To fully map an object, multiple scans are captured. The different scans need to be registered with the help of the point cloud in the overlap regions. To guarantee reliable registration, the scans should have large overlap ratio with good geometry for the estimation of the transformation parameters among these scans. The objective of this paper is to propose a registration method that relaxes/eliminates the overlap requirement through the utilization of photogrammetrically reconstructed features. More specifically, a point-based procedure, which utilizes non-conjugate points along corresponding linear features from photogrammetric and TLS data, will be used for the registration. The non-correspondence of the selected points along the linear features is compensated for by artificially modifying their weight matrices. The paper presents experimental results from simulated and real datasets to illustrate the feasibility of the proposed procedure.

• Mass Spectrometry Letters
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• v.9 no.4
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• pp.91-94
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• 2018

Bilin tetrapyrroles are metabolic products of the breakdown of porphyrins within a species. In the case of mammals, these bilins are formed by the catabolism of heme and can be utilized as either biomarkers in disease or as an indicator of human waste contamination. Although a small subset of bilin tandem mass spectrometry reports exist, limited data is available in online databases for their fragmentation. The use of fragmentation data is important for metabolomics analyses to determine the identity of compounds detected within a sample. Therefore, in this study, the fragmentation of bilins generated by positive ion mode electrospray ionization is examined by collision-induced dissociation (CID) as a function of collision energy on an FT-ICR MS. The use of the FT-ICR MS allows for high mass accuracy measurements, and thus the formulas of resultant product ions can be ascertained. Based on our observations, fragmentation behavior for hemin, biliverdin and its dimethyl ester, phycocyanobilin, bilirubin, bilirubin conjugate, mesobilirubin, urobilin, and stercobilin are discussed in the context of the molecular structure and collision energy. This report provides insight into the identification of structures within this class of molecules for untargeted analyses.

• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.537-550
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• 2000

In this study, Neural Networks models were used to forecast daily streamflow at Jindong station of the Nakdong River basin. Neural Networks models consist of CASE 1(5-5-1) and CASE 2(5-5-5-1). The criteria which separates two models is the number of hidden layers. Each model has Fletcher-Reeves Conjugate Gradient BackPropagation(FR-CGBP) and Scaled Conjugate Gradient BackPropagation(SCGBP) algorithms, which are better than original BackPropagation(BP) in convergence of global error and training tolerance. The data which are available for model training and validation were composed of wet, average, dry, wet+average, wet+dry, average+dry and wet+average+dry year respectively. During model training, the optimal connection weights and biases were determined using each data set and the daily streamflow was calculated at the same time. Except for wet+dry year, the results of training were good conditions by statistical analysis of forecast errors. And, model validation was carried out using the connection weights and biases which were calculated from model training. The results of validation were satisfactory like those of training. Daily streamflow forecasting using Neural Networks models were compared with those forecasted by Multiple Regression Analysis Mode(MRAM). Neural Networks models were displayed slightly better results than MRAM in this study. Thus, Neural Networks models have much advantage to provide a more sysmatic approach, reduce model parameters, and shorten the time spent in the model development.