• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.7
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• pp.375-381
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• 2004

This paper describes a three-winding transformer protective relaying algorithm based on the ratio of increments of flux linkages (RIFL). To minimize the approximation errors, the algorithm uses integration approximation. The RIFL of the two windings is equal to the turns ratio for all operating conditions except for an internal fault. For a single-phase and three-phase transformer containing the wye-connected windings, the increments of flux linkages (IFL) are calculated. For a three-phase transformer containing the delta-connected windings, the difference of IFL between the two phases are calculated to use the line currents, because the winding currents are practically unavailable. Their ratios are compared with the turns ratio. The comparative study between the proposed and differential approximation methods was conducted. The test results show that the algorithm can reduce the errors resulting from the conventional methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.11
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• pp.655-660
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• 2003

This paper describes a transformer protective relaying algorithm based on the ratio of increments of flux linkages (RIFL) of the primary and secondary windings. The algorithm uses integration approximation. The RIFL is equal to the turns ratio for all operating conditions except for an internal fault. For a single-phase transformer and a Y-Y transformer, the increments of flux linkages (IFL) are calculated. For a Y-$\Delta$ transformer, the difference of IFL are calculated to use the line currents rather than the delta winding currents, which are unavailable. Their ratios are compared with the turns ratio. The comparative study between the proposed and conventional differentiation approximation methods was conducted. The test results show that the algorithm reduces the approximation errors of the conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.33-41
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• 2002

In this paper, we present a frequency weighted model reduction using LMIs for minimizing the H$\infty$ weighted model error compared with the methods of frequency weighted balanced truncation and frequency weighted Hankel norm approximation. The proposed algorithm, its form is equal to the sufficient condition of performance preserving controller approximation, is based on an iterative two-step LMI scheme induced from bound real lemma. So, it can be applied to the problem of the performance preserving controller approximation. The controller reduction is useful in a practical control design and ensures its easy implementation and high reliability The validity of the proposed algorithm is shown through numerical examples. Additionaly, we extend the proposed algorithm to performance preserving controller approximation by applying to the HIMAT(highly maneuverable aircraft technology) system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2141-2155
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• 2020

In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.47-53
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• 2014

We propose a parameter estimation algorithm for radar signal active cancellation based on the Pade approximation that requires much less samples than the conventional MLE scheme. We also verify that the radar signal with time-variant center frequency can be estimated with proposed algorithm by renovating the center frequency. We present simulation results for radar signal active cancellation to show the accuracy of estimated signal using proposed algorithm by calculating the ratio of RMSE of estimated signal to amplitude of hostile radar signal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2303-2308
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• 2012

In this paper, we deal with the problem of scheduling parallel tasks with deadlines. Parallel tasks can be simultaneously executed on various machines and specially, we consider the malleable tasks, that is, the tasks whose execution time is given by a function of the number of machines on which they are executed. The goal of the problem is to maximize the throughput of tasks completed within their deadlines. This problem is well-known as NP-hard problem. Thus we will find an approximation algorithm, and its performance is compared with that of the optimal algorithm and analyzed by finding the approximation ratio. In particular, the algorithm has more resources, that is, more machines, than the optimal algorithm. This is called the resource augmentation analysis. We propose an algorithm to guarantee the approximation ratio of 3.67 using 1.5 times machines.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.202-208
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• 2015

In this study, we propose a new inference algorithm for a multiclass Gaussian process classification model using a variational EM framework and the Laplace approximation (LA) technique. This is performed in two steps, called expectation and maximization. First, in the expectation step (E-step), using Bayes' theorem and the LA technique, we derive the approximate posterior distribution of the latent function, indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. In the maximization step, we compute the maximum likelihood estimators for hyper-parameters of a covariance matrix necessary to define the prior distribution of the latent function by using the posterior distribution derived in the E-step. These steps iteratively repeat until a convergence condition is satisfied. Moreover, we conducted the experiments by using synthetic data and Iris data in order to verify the performance of the proposed algorithm. Experimental results reveal that the proposed algorithm shows good performance on these datasets.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.2
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• pp.236-247
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• 1992

Optimization has been developed to minimize the cost function while satisfying constraints. Nonlinear Programming method is used as a tool for the optimization. Usually, cost and constraint function calculations are required in the engineering applications, but those calculations are extremely expensive. Especially, the function and sensitivity analyses cause a bottleneck in structural optimization which utilizes the Finite Element Method. Also, when the functions are quite noisy, the informations do not carry out proper role in the optimization process. An algorithm called "Second-order Approximation Method" has been proposed to overcome the difficulties recently. The cost and constraint functions are approximated by the second-order Taylor series expansion on a nominal points in the algorithm. An optimal design problem is defined with the approximated functions and the approximated problem is solved by a nonlinear programming numerical algorithm. The solution is included in a candidate point set which is evaluated for a new nominal point. Since the functions are approximated only by the function values, sensitivity informations are not needed. One-dimensional line search is unnecessary due to the fact that the nonlinear algorithm handles the approximated functions. In this research, the method is analyzed and the performance is evaluated. Several mathematical problems are created and some standard engineering problems are selected for the evaluation. Through numerical results, applicabilities of the algorithm to large scale and complex problems are presented.presented.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.9
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• pp.1-8
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• 2010

A digital phase wrapping modulation (DPM) open-loop polar transmitter can be efficiently applied to a wideband orthogonal frequency division multiplexing (OFDM) communication system by converting in-phase and quadrature signals to envelope and phase signals and then employing the signal mapping process. This mapping process is very similar to quantization in a general communication system, and when taking into account the error that appears during mapping process, one can replace the coordinates rotation digital computer (CORDIC) algorithm in the coordinate conversion part with the Taylor series approximation method. In this paper, we investigate the application of the Taylor series approximation to the cartesian to polar coordinate conversion part of a DPM polar transmitter for wideband OFDM systems. The conventional approach relies on the CORDIC algorithm. To achieve efficient application, we perform computer simulation to measure mean square error (MSE) of the both approaches and find the minimum approximation order for the Taylor series approximation compatible to allowable error of the CORDIC algorithm in terms of hardware design. Furthermore, comparing the processing speeds of the both approaches in the implementation with FPGA reveals that the Taylor series approximation with lower order improves the processing speed in the coordinate conversion part.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.1
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• pp.251-268
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• 1996

In this paper, a higher-order feedforward neural network called ridge polynomial network (RPN) which shows good approximation capability for nonlnear continuous functions defined on compact subsets in multi-dimensional Euclidean spaces, is presented. This network provides more efficient and regular structure as compared to ordinary higher-order feedforward networks based on Gabor-Kolmogrov polynomial expansions, while maintating their fast learning property. the ridge polynomial network is a generalization of the pi-sigma network (PSN) and uses a specialform of ridge polynomials. It is shown that any multivariate polynomial can be exactly represented in this form, and thus realized by a RPN. The approximation capability of the RPNs for arbitrary continuous functions is shown by this representation theorem and the classical weierstrass polynomial approximation theorem. The RPN provides a natural mechanism for incremental function approximation based on learning algorithm of the PSN. Simulation results on several applications such as multivariate function approximation and pattern classification assert nonlinear approximation capability of the RPN.