• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2527-2533
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• 2011

In this paper we propose a new pre-processing algorithm applied to multi-view video coding using color compensation algorithm based on image features. Multi-view images have a difference between neighboring frames according to illumination and different camera characteristics. To compensate this color difference, first we model the characteristics of cameras based on frame's feature from each camera and then correct the color difference. To extract corresponding features from each frame, we use Harris corner detection algorithm and characteristic coefficients used in the model is estimated by using Gauss-Newton algorithm. In this algorithm, we compensate RGB components of target images, separately from the reference image. The experimental results with many test images show that the proposed algorithm peformed better than the histogram based algorithm as much as 14 % of bit reduction and 0.5 dB ~ 0.8dB of PSNR enhancement.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.10-18
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• 2010

This paper proposes an image stabilization algorithm for close watching UAV(Unmanned Aerial Vehicle) using motion separation and stabilization mode. The motion of UAV is composed of its actual navigating motion and unwanted vibrating motion so that image sequences obtained from UAV are shaken randomly. In order to stabilize these images we separate the vibrating motion component from UAV motion and remove the effect caused by it from image sequences. In the proposed algorithm the motion and global intensity change of two consecutive images are modeled with 6 motion parameters and 2 intensity change parameters respectively. These modeled parameters are estimated by non-linear least square method based on Gauss-Newton algorithm. The vibrating motion component is separated from the estimated motion using IIR filtering and the geometric deformation caused by it is removed from image sequences. In order to apply the proposed method to real aerial image sequences with many abrupt changes of camera view, we proposed a stabilizing method using two different modes named as stabilizing and non-stabilizing mode. Experimental results show that the accuracy of motion estimation is 99% and the efficiency of removing the vibrating motion component is 90%. We apply the proposed method to real aerial image sequences and verified its stabilizing performance.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1081-1087
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• 1999

Load Flow calulation methods can usually be divided into Gauss-Seidel method, Newton-Raphson method and decoupled method. Load flow calculation is a basic on-line or off-line process for power system planning. operation, control and state analysis. These days Newton-Raphson method is mainly used since it shows remarkable convergence characteristics. It, however, needs considerable calculation time in construction and calculation of inverse Jacobian matrix. In addition to that, Newton-Raphson method tends to fail to converge when system loading is heavy and system has a large R/X ratio. In this paper, matrix equation is used to make algebraic expression and then to slove load flow equation and to modify above defects. And it preserve P-Q bus part of Jacobian matrix to shorten computing time. Application of mentioned algorithm to 14 bus, 39 bus, 118 bus systems led to identical results and the same numbers of iteration obtained by Newton-Raphson method. The effect of computing time reduction showed about 28% , 30% , at each case of 39 bus, 118 bus system.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.76-84
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• 2013

In the passive emitter localization using instantaneous TDOA (time difference of arrival) and FDOA (frequency difference of arrival) measurements, the estimation accuracy can be improved by collecting additional measurements. To achieve this goal, it is required to increase the number of the sensors. However, in electronic warfare environment, a large number of sensors cause the loss of military strength due to high probability of intercept. Also, the additional processes should be considered such as the data link and the clock synchronization between the sensors. Hence, in this paper, the passive localization of a stationary emitter is presented by using the successive TDOA and FDOA measurements from two moving sensors. In this case, since an independent pair of sensors is added in the data set at every instant of measurement, each pair of sensors does not share the common reference sensor. Therefore, the QCLS (quadratic correction least squares) methods cannot be applied, in which all pairs of sensor should include the common reference sensor. For this reason, a Gauss-Newton algorithm is adopted to solve the non-linear least square problem. In addition, to show the performance of the proposed method, we compare the RMSE (root mean square error) of the estimates with CRLB (Cramer-Rao lower bound) and derived the CEP (circular error probable) planes to analyze the expected estimation performance on the 2-dimensional space.

• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.186-194
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• 2019

Deterministic optimization, commonly used to find the geophysical inverse solutions, have its limitation that it cannot find the proper solution since it might converge into the local minimum. One of the solutions to this problem is to use global optimization based on a stochastic approach, among which a large number of particle swarm optimization (PSO) applications have been introduced. In this paper, I developed a geophysical inversion algorithm applying PSO method for the layered-earth resistivity inversion of the small-loop electromagnetic (EM) survey data and carried out numerical inversion experiments on synthetic datasets. From the results, it is confirmed that the PSO inversion algorithm could increase the inversion success rate even when attempting the inversion of small-loop EM survey data from which it might be difficult to find a best solution by applying the Gauss-Newton inversion algorithm.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.311-315
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• 1998

Load Flow calculation methods can usually be divided into Gauss-Seidel method, Newton-Raphson method and decoupled method. Load flow calculation is a basic on-line or off-line process for power system planning, operation, control and state analysis. These days Newton-Raphson method is mainly used since it shows remarkable convergence characteristics. It, however, needs considerable calculation time in construction and calculation of inverse Jacobian matrix. In addition to that, Newton-Raphson method tends to fail to converge when system loading is heavy and system has a large R/X ratio. In this paper, matrix equation is used to make algebraic expression and then to solve load flow equation and to modify above defects. And it preserve certain part of Jacobian matrix to shorten the time of calculation. Application of mentioned algorithm to 14 bus, 39 bus, 118 bus systems led to identical result and the number of iteration got by Newton-Raphson method. The effect of time reduction showed about 28%, 30%, at each case of 39 bus, 118 bus system.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1144-1148
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• 1996

This paper presents WMR modelling and path tracking algorithm using Inertial Navigation System. The error models of gyroscope and accelerometers in INS are derived by Gauss-Newton method which is nonlinear regression model. Then, to test availability of error model, we pursue the fitness diagnosis about probability characteristic for real data and estimated data. Performance of inertial sensor with error model and Kalman filter is pursued by comparing with one without them. The computer simulation shows that position error remarkably decrease when error compensation is applied.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1338-1347
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• 2013

Face recognition is a science of automatically identifying individuals based their unique facial features. In order to avoid overfitting and reduce the computational reduce the computational burden, a new face recognition algorithm using PCA-fisher linear discriminant (PCA-FLD) and fuzzy radial basis function neural network (RBFNN) is proposed in this paper. First, face features are extracted by the principal component analysis (PCA) method. Then, the extracted features are further processed by the Fisher's linear discriminant technique to acquire lower-dimensional discriminant patterns, the processed features will be considered as the input of the fuzzy RBFNN. As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on Levenberg-Marquart (L-M) for fuzzy RBF neural network is introduced in this paper, which combined the Gradient Descent algorithm with the Gauss-Newton algorithm. Experimental results on the ORL face database demonstrate that the proposed algorithm has satisfactory performance and high recognition rate.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.1-8
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• 2004

We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Robin (1974), is extended to a more general case of non-monotone missing data. The proposed method is algebraically equivalent to the Newton-Raphson method for the observed likelihood, but avoids the burden of computing the first and the second partial derivatives of the observed likelihood Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method. A numerical example is also presented to illustrate the method.

• Journal of Electrical Engineering and Technology
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• v.2 no.3
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• pp.312-319
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• 2007

The solution of the power flow is one of the most important problems in electrical power systems. These traditional methods such as Gauss-Seidel method and Newton-Raphson (NR) method have had drawbacks up to now such as initial values, abnormal operating solutions and divergences in heavy loads. In order to overcome theses problems, the power flow solution incorporating genetic algorithm (GA) is introduced in this paper. General operator of genetic algorithm, arithmetic crossover, and non-uniform mutation operator of GA are suggested to solve the power flow problem. While abnormal solution cannot be obtained by a NR method, multiple power flow solution can be obtained by a GA method. With a heavy load, both normal solution and abnormal solution can be obtained by a proposed method. In this paper, a floating number representation instead of the binary number representation is introduced for accuracy. Simulation results have been compared with traditional methods.