• Journal of applied mathematics & informatics
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• v.35 no.1_2
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• pp.165-180
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• 2017

In this study, Taylor matrix algorithm is designed for the approximate solution of linear and non-linear differential equation systems. The algorithm is essentially based on the expansion of the functions in differential equation systems to Taylor series and substituting the matrix forms of these expansions into the given equation systems. Using the Mathematica program, the matrix equations are solved and the unknown Taylor coefficients are found approximately. The presented numerical approach is discussed on samples from various linear and non-linear differential equation systems as well as stiff systems. The computational data are then compared with those of some earlier numerical or exact results. As a result, this comparison demonstrates that the proposed method is accurate and reliable.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.47-61
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• 1996

We propose a parallel neural network model in which patterns are clustered and patterns in a cluster are studied in a parallel neural network. The learning algorithm used in this paper is based on LVQ algorithm of Kohonen(1990) for clustering and ADALINE(Adaptive Linear Neuron) network of Widrow and Hoff(1990) for parallel learning. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists of 250 patterns of ASCII characters normalized into $8\times16$ and 1124. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists 250 patterns of ASCII characters normalized into $8\times16$ and 1124 samples acquired from signals generated from 9 car models that passed Inductive Loop Detector(ILD) at 10 points. In ASCII character experiment, 191(179) out of 250 patterns are recognized with 3%(5%) noise and with 1124 car model data. 807 car models were recognized showing 71.8% recognition ratio. This result is 10.2% improvement over backpropagation algorithm.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2142-2153
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• 2014

Linearization of transducer characteristic plays a vital role in electronic instrumentation because all transducers have outputs nonlinearly related to the physical variables they sense. If the transducer output is nonlinear, it will produce a whole assortment of problems. Transducers rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. Attempts have been made by many researchers to increase the range of linearity of transducers. This paper presents a method to compensate nonlinearity of Linear Variable Displacement Transducer (LVDT) based on Extreme Learning Machine (ELM) method, Differential Evolution (DE) algorithm and Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Because of the mechanism structure, LVDT often exhibit inherent nonlinear input-output characteristics. The best approximation capability of optimized ANN technique is beneficial to this. The use of this proposed method is demonstrated through computer simulation with the experimental data of two different LVDTs. The results reveal that the proposed method compensated the presence of nonlinearity in the displacement transducer with very low training time, lowest Mean Square Error (MSE) value and better linearity. This research work involves less computational complexity and it behaves a good performance for nonlinearity compensation for LVDT and has good application prospect.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.93-107
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• 2005

Based on the recently developed ABS algorithm for solving linear Diophantine equations, we present a special ABS algorithm for solving such equations which is effective in computation and storage, not requiring the computation of the greatest common divisor. A class of equations always solvable in integers is identified. Using this result, we discuss the ILP problem with upper and lower bounds on the variables.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.165-168
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• 2000

First, we give a linear-time algorithm to find the first overlap in an arbitrary binary word. Second, we implement the algorithm in the C language and show that the number of comparisons in this algorithm is less than 31n, where n$\geq$3 is the length of the input word.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.187-201
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• 1994

This paper introduces an effective hybridization of the usual simplex method and an interior point method in the convergent framework of Dembo and Sahi. We formulate a specific and detailed algorithm (HYBRID) and report the results of some preliminary testing on small dense problems for its viability. By piercing through the feasible region, the newly developed hybrid algorithm avoids the combinatorial structure of linear programs, and several other interesting and important characteristics of this algorithm are also discussed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.48-54
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• 1994

A recursive on-line algorithm with orthogonal ARMA identification is proposed for linear systems with unkonwn time delay, order, and parameters. The algorithm is based on the Gram-Schmidt orthogonalization of basis functions, and extendedto recursive form by using two dimensional autocorrelations and crosscorrelations of input and output with constant data length. The proposed algorith can cope with slowly time-varying or order-varying delayed system. Various simulations reveal the performance of the algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.84-90
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• 1997

We proposed a new bit-rate control algorithm to improve MPEG-2 video software encoder. Bit-rate Control plays an improtant role in picture quality of MPEG-2 encoder. To achieve better encoding performance such as controlling picture quality and using bity properly, we proposed a MPEG-2 bit-rate control algorithm using linear average Step-Size. Using a benchmark Program, we compared our algorithm with MPEG-2 Test Model 5. Our proposed algorithm showed better Bit-Rate Control with respect to used bits, picture quality.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.188-192
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• 2002

The ellipsoid algorithm solves some feasibility(or optimization) problems with LMI(Linear Matrix Inequality) constraint in polynomial time. Recently, it has been replaced by interior point algorithm due to its slow convergence and incapability of verifying feasibility. This paper proposes a method to improve its convergence by using the deep-cut method of linear programming. Simulation results show that the improved algorithm is more effective than the original one.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9A
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• pp.917-923
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• 2008

The D-STTD system obtains the diversity gain through the STTD scheme and the Multiplexing gain through parallel structure of the encoder using the STTD scheme known Alamouti Code. We are difficult to use Combining scheme of the STTD scheme for the D-STTD detection in the decoder because the D-STTD system transmits mutually different data in each other STTD encoder for multiplexing gain. Therefore, in this paper we combine the D-STTD system with Linear algorithm, SIC algorithm and OSIC algorithm known multiplexing detection scheme based on MMSE scheme and compare the performance of each system. And we propose the detection scheme of the D-STTD using MAP Algorithm and analyze the performance of each system. The simulation results show that the detector using iterative algorithm has better performance than Linear MMSE Detector. Especially, we show that the detector using MAP algorithm outperforms conventional detector.