• 대한전기학회논문지:전력기술부문A
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• 제53권7호
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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.

• 대한전기학회논문지:전력기술부문A
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• 제52권11호
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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.

• 전자공학회논문지SC
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• 제39권1호
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• pp.33-41
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• 2002

본 논문에서는 기존의 주파수하중 균형절단 기법과 주파수하중 한켈노옴 근사화 기법에 비하여 더 작은 H∞ 하중 축소오차를 가지는 새로운 알고리듬을 제시한다. 제시한 알고리듬은 제한 실 보조정리로부터 반복적인 두 단계의 선형행렬부등식 형태로 유도한다. 또한 제안한 알고리듬을 성능보장을 위한 제어기 차수축소기법에 적용한다. 수치적 예를 통하여 제안한 알고리듬의 타당성을 보이고 기존의 모델 차수축소기법과 비교 분석하며 HIMAT(highly maneuverable aircraft technology) 시스템의 예를 통하여 성능보장을 위한 제어기 차수축소 기법에 적용할 수 있음을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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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.

• 전자공학회논문지
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• 제51권11호
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• pp.47-53
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• 2014

레이더 신호 능동 상쇄를 위하여 기존의 MLE 기법에 비해 매우 적은 수신 신호 샘플만을 이용하여 신호의 파라미터 추정이 가능한 Pade approximation 기반의 적응적 파라미터 추정 알고리즘을 제안한다. 또한 제안하는 알고리즘이 레이더 신호의 중심 주파수를 갱신함으로써 시간에 따라 변하는 중심 주파수를 갖는 신호에 대한 능동적인 추정이 가능함을 보인다. 레이더 신호 능동 상쇄 모의실험을 통해 위협 레이더 신호의 크기에 대한 추정된 신호의 RMSE의 비를 계산함으로써 제안하는 알고리즘의 추정 정확도를 보인다.

• 한국정보통신학회논문지
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• 제16권10호
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• pp.2303-2308
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• 2012

본 논문은 마감시간을 가지는 병렬 태스크들을 스케줄하는 문제를 다룬다. 특히, 가단성 태스크, 다시 말해서, 수행시간이 수행 머신들의 개수의 함수로 주어지는 태스크를 다룬다. 스케줄링 알고리즘의 목표는 마감시간 안에 수행을 끝마친 태스크들의 작업량의 합을 최대화하는 것이다. 이 문제는 NP-hard 문제로 알려져 있다. 따라서, 근사 알고리즘을 찾으려하고, 알고리즘의 성능은 최적 알고리즘 성능과의 비, 다시 말해서, 근사비를 구해서 분석한다. 특히, 본 논문에서는 알고리즘이 최적 알고리즘보다 많은 자원, 즉, 보다 많은 머신들을 가지는 경우에 근사비를 구할 것이다. 이것은 자원추가분석이라고 불린다. 본 논문은 최적 알고리즘보다 1.5배의 머신들을 사용해서 3.67의 근사비를 보장하는 스케줄링 알고리즘을 제안한다.

• IEIE Transactions on Smart Processing and Computing
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• 제4권4호
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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.

• 대한기계학회논문집
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• 제16권2호
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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.

• 대한전자공학회논문지TC
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• 제47권9호
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• pp.1-8
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• 2010

DPM (Digital Phase wrapping Modulation) open-loop polar transmitter는 in-phase와 quadrature 신호를 진폭(envelope) 신호와 위상(phase) 신호로 변환한 후 신호의 사상화 과정을 거쳐 광대역 통신 시스템에서의 효율적인 적용이 가능하다. 사상화 과정은 일반적인 통신 시스템에서의 양자화와 유사하며 그 과정에서 발생하는 오차를 고려할 때 좌표계 변환부에 CORDIC (COordinates Rotation DIgital Computer) 알고리듬 대신 테일러 급수 근사 기법의 사용이 가능하다. 본 논문에서는 테일러 급수 근사 기법을 광대역 OFDM (Orthogonal Frequency Division Multiplexing) 시스템용 DPM polar transmitter의 직교 좌표계-극 좌표계(cartesian to polar coordinate) 변환부에 적용하는 방안에 대한 연구를 수행하였다. 기존의 방법은 CORDIC 알고리듬을 채용하고 있다. 이것을 효율적으로 적용하기 위해 모의 실험을 통해 각각의 기법에 대한 평균제곱오차 (MSE : Mean Square Error) 성능을 측정하고, 설계 관점에서 허용된 CORDIC 오차를 기준으로 알고리듬의 최소 반복횟수와 테일러 급수의 최소 근사 차수를 찾는다. 또한 FPGA 전달 지연속도를 비교한 결과에 의하면 CORDIC 알고리듬 대신 낮은 차수의 테일러 급수 근사 기법을 사용해 좌표 변환부의 처리 속도를 향상시킬 수 있음을 확인하였다.

• 한국통신학회논문지
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• 제21권1호
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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.