• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.4
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• pp.388-397
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• 1989

This paper describes an intelligent iterative parallel algorithm for solving large sparse linear systems of equations, and proposes a ststic dataflow computer architechture for the implementation of the algorithm. Implemented with the Jacobi interative method, the intelligent algorithm reduces the parallel execution time by reducing the individual inner product operation time.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.828-833
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• 2005

Kernel methods have shown to improve the performance of conventional linear classification algorithms for complex distributed data sets, as mapping the data in input space into a higher dimensional feature space(7). In this paper, we propose a fuzzy kernel K-nearest neighbor(fuzzy kernel K-NN) algorithm, which applies the distance measure in feature space based on kernel functions to the fuzzy K-nearest neighbor(fuzzy K-NN) algorithm. In doing so, the proposed algorithm can enhance the Performance of the conventional algorithm, by choosing an appropriate kernel function. Results on several data sets and segmentation results for real images are given to show the validity of our proposed algorithm.

• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.73-78
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• 2017

A general linearly constrained broadband adaptive array is examined in the eigenvector space with respect to the optimal weight vector and the adaptive algorithm. The optimal weight vector and the general adaptive algorithm in the eigenvector space are obtained by eigenvector matrix transformation. Their operations are shown to be the same as in the standard coordinate system except for the relevant transformed vectors and matrices. The nulling performance of the general linearly constrained broadband adaptive array depends on the gain factor such that the constraint plane is shifted perpendicularly to the origin by an increase in the gain factor. The general linearly constrained broadband adaptive array is observed to perform better than a conventional linearly constrained adaptive array in a coherent signal environment, while the former performs similarly to the latter in a non-coherent signal environment.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.58-66
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• 1998

In many situations, it is very important to identify a certain unkown system, it from its input-output data. For this purpose, several system modeling algorithms have been suggested heretofore, and studies regarding the fuzzy modeling based on its nonlinearity get underway as well. Generatlly, fuzzy models have the capability of dividing input space into several subspaces, compared to linear ones. But hitherto subggested fuzzy modeling algorithms do not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem, this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently that conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space, the method of principal component is ued. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.430-443
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• 2005

A general construction algorithm of the Grassmann space parameters in linear systems - so-called, the Plucker matrix, 'L' in m-input, p-output, n-th order static output feedback systems and the Plucker matrix, $'L^{aug}'$ in augmented (m+d)-input, (p+d)-output, (n+d)-th order static output feedback systems - is presented for numerical checking of necessary conditions of complete static and complete minimum d-th order dynamic output feedback pole-assignments, respectively, and also for discernment of deterministic computation condition of their pole-assignable real solutions. Through the construction of L, it is shown that certain generically pole-assignable strictly proper mp > n system is actually none pole-assignable over any (real and complex) output feedbacks, by intrinsic rank deficiency of some submatrix of L. And it is also concretely illustrated that this none pole-assignable mp > n system by static output feedback can be arbitrary pole-assignable system via minimum d-th order dynamic output feedback, which is constructed by deterministic computation under full­rank of some submatrix of $L^{aug}$.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.41-50
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• 2004

Since the MPEG-4 visual standard enables content-based functionalities, it is necessary to extract video objects from video sequences. Segmentation algorithms can largely be classified into two different categories: automatic segmentation and user-assisted segmentation. In this paper, we propose a new user-assisted image segmentation method based on the active contour. If we define a shape space as a set of all possible variations from the initial curve and we assume that the shape space is linear, it can be decomposed into the column space and the left null space of the shape matrix. In the proposed method, the shape space vector in the column space describes changes from the initial curve to the imaginary feature curve, and a dynamic graph search algorithm describes the detailed shape of the object in the left null space. Since we employ the shape matrix and the SUSAN operator to outline object boundaries, the proposed algorithm can ignore unwanted feature points generated by low-level image processing operations and is, therefore, applicable to images of complex background. We can also compensate for limitations of the shape matrix with a dynamic graph search algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.779-784
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• 2008

This paper presents Kalman filter based system modeling algorithm for autonomous robot systems. State of the robot system is measured using embedded sensor systems and then carried to a host computer via ubiquitous sensor network (USN). We settle a linear state-space motion equation for unknown robot system dynamics and modify a popular Kalman filter algorithm in deriving suitable parameter estimation mechanism. To represent time-delay nature due to network media in system modeling, we construct an augmented state-space model which is mainly composed of original state and estimated parameter vectors. We conduct real-time experiment to test our proposed estimation algorithm where speed state of the constructed robot is used as system observation.

• Wind and Structures
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• v.2 no.3
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• pp.189-200
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• 1999

In this paper, a new algorithm for active control design of structures is proposed and investigated. The algorithm preserves the decoupling property of the modal vibration equation and eliminates the spillover problem, which is the main shortcoming in the independent modal space control(IMSC) algorithm. With linear quadratic regulator(LQR) control law, the analytical solution of algebraic Riccati equation and the optimal actuator control force are obtained, and the control design procedure is significantly simplified. A numerical example for the control design of a tall building subjected to wind loads demonstrates the effectiveness of the proposed algorithm in reducing the acceleration and displacement responses of tall buildings under wind actions.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.10
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• pp.529-535
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• 2004

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. A wide variety of techniques/algorithms for load forecasting has been reported in many literatures. These techniques are as follows: multiple linear regression, stochastic time series, general exponential smoothing, state space and Kalman filter, knowledge-based expert system approach (fuzzy method and artificial neural network). These techniques have improved the accuracy of the load forecasting. In recent 10 years, many researchers have focused on artificial neural network and fuzzy method for the load forecasting. In this paper, we propose an algorithm of a hybrid load forecasting method using fuzzy linear regression and general exponential smoothing and considering the sensitivities of the temperature. In order to consider the lower load of weekends and Monday than weekdays, fuzzy linear regression method is proposed. The temperature sensitivity is used to improve the accuracy of the load forecasting through the relation of the daily load and temperature. And the normal load of weekdays is easily forecasted by general exponential smoothing method. Test results show that the proposed algorithm improves the accuracy of the load forecasting in 1996.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.548-553
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• 1992

In this paper and recursive version of orthogonal ARMA identification algorithm is proposed. The basic algorithm is based on Gram-Schmidt orthogonalization of automatically selected basis functions from specified function space, but does not require explicit creation of orthogonal functions. By using two dimensional autocorrelations and crosscorrelations of input and output with constant data length, identification algorithm is extended to cope slowly time-varying or order-varying delayed system.