• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.33-38
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• 2014

The FCM based hybrid RBF network is a heterogeneous learning network model that applies FCM algorithm between input and middle layer and applies Max_Min algorithm between middle layer and output. The Max-Min neural network uses winner nodes of the middle layer as input but shows inefficient learning in performance when the input vector consists of too many patterns. To overcome this problem, we propose a dynamic learning rate control based on fuzzy logic. The proposed method first classifies accurate/inaccurate class with respect to the difference between target value and output value with threshold and then fuzzy membership function and fuzzy decision logic is designed to control the learning rate dynamically. We apply this proposed RBF network to the character recognition problem and the efficacy of the proposed method is verified in the experiment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.229-232
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• 2012

This paper describes performance evaluation using fixed-point Matlab modeling and simulation, and hardware design of LDPC decoder which is based on Improved Normalized Min-Sum(INMS) decoding algorithm. The designed LDPC decoder supports 19 block lengths(576~2304) and 6 code rates(1/2, 2/3A, 2/3B, 3/4A, 3/4B, 5/6) of IEEE 802.16e mobile WiMAX standard. Considering hardware complexity, it is designed using a block-serial(partially parallel) architecture which is based on layered decoding scheme. A DFU based on sign-magnitude arithmetic is adopted to minimize hardware area. Hardware design is optimized by using INMS decoding algorithm whose performance is better than min-sum algorithm.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.77-82
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• 2007

In this paper, we proposed the image processing techniques for extracting the cracks in a concrete surface crack image and the enhanced Max-Min neural network for recognizing the directions of the extracted cracks. The image processing techniques used are the closing operation or morphological techniques, the Sobel masking for extracting for edges of the cracks, and the iterated binarization for acquiring the binarized image from the crack image. The cracks are extracted from the concrete surface image after applying two times of noise reduction to the binarized image. We proposed the method for automatically recognizing the directions of the cracks with the enhanced Max-Min neural network. Also, we propose an enhanced Max-Min neural network by auto-tuning of learning rate using delta-bar-delta algorithm. The experiments using real concrete crack images showed that the cracks in the concrete crack images were effectively extracted and the enhanced Max-Min neural network was effective in the recognition of direction of the extracted cracks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.243-250
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• 2014

In this paper, I devise a balance-swap optimization (BSO) algorithm to solve economic load dispatch with a quadratic fuel cost function. This algorithm firstly sets initial values to $P_i{\leftarrow}P_i^{max}$, (${\Sigma}P_i^{max}$ > $P_d$) and subsequently entails two major processes: a balance process whereby a generator's power i of $_{max}\{F(P_i)-F(P_i-{\alpha})\}$, ${\alpha}=_{min}(P_i-P_i^{min})$ is balanced by $P_i{\leftarrow}P_i-{\alpha}$ until ${\Sigma}P_i=P_d$; and a swap process whereby $_{max}\{F(P_i)-F(P_i-{\beta})\}$ > $_{min}\{F(P_i+{{\beta})-F(P_j)\}$, $i{\neq}j$, ${\beta}$ = 1.0, 0.1, 0.1, 0.01, 0.001 is set at $P_i{\leftarrow}P_i-{\beta}$, $P_j{\leftarrow}P_j+{\beta}$. When applied to 15, 20, and 38-generators benchmark data, this simple algorithm has proven to consistently yield the best possible results. Moreover, this algorithm has dramatically reduced the costs for a centralized operation of 73-generators - a sum of the three benchmark cases - which could otherwise have been impossible for independent operations.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.481-488
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• 2007

The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. The k-modes algorithm is to extend the k-means paradigm to categorical domains. The algorithm requires a pre-setting or random selection of initial points (modes) of the clusters. This paper improved the problem of k-modes algorithm, using the Max-Min method that is a kind of methods to decide initial values in k-means algorithm. we introduce new similarity measures to deal with using the categorical data for clustering. We show that the mushroom data sets and soybean data sets tested with the proposed algorithm has shown a good performance for the two aspects(accuracy, run time).

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.392-392
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• 2004

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.976-978
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• 1999

Recently PWM invertor is broadly used for control of induction motor. The invertor is able to generate sin wave current from high speed switching power device such as IGBT. However the invertor is disturbed by dead time inevitably needed to prevent a short of the DC link voltage, and the dead time mainly causes distortions of the output current. In this Paper the dead time compensation method which corrects the voltage error from dead time, and Min Max algorithm enlarging the operating voltage of PWM were Proposed. This method can be implemented by software programming without any additional hardware circuit. The proposed algorithms were implemented by DSP(TMS320C31, 40MHz) and FPGA(QL2007, Quick Logic) described in VHDL. and applied to 3 phase induction motor(2.2 KW) to show the superior performance

• Journal of the korean Society of Automotive Engineers
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• v.15 no.1
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• pp.81-88
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• 1993

A direct treatment of the min-max type objective function of the dynamic response optimization problem is proposed. Previously, the min-max type objective function was transformed to an artificial design variable and an additional point-wise state variable constraint function was imposed, which increased the complexity of the optimization problem. Especially, the design sensitivity analysis for the augmented Lagrangian functional with the suggested treatment is established by using the adjoint variable method and a computer program to implement the proposed algorithm is developed. The optimization result of the proposed treatment are obtained for three typical problems and compared with those of the previous treatment. It is concluded that the suggested treatment in much more efficient in the computational effort than the previous treatment with giving the similar optimal solutions.

• Korean Management Science Review
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• v.26 no.1
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• pp.65-76
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• 2009

The undirected Steiner tree problem in graphs is known to be NP-hard. The objective of this problem is to find a shortest tree containing a subset of nodes, called terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step. graph reduction rules eliminate useless nodes and edges which do not contribute to make an optimal solution. In the second step, a max-min ant colony optimization combined with Prim's algorithm is developed to solve the reduced problem. The proposed algorithm is tested in the sets of standard test problems. The results show that the algorithm efficiently presents very correct solutions to the benchmark problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.253-262
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• 2016

This paper proposes a balance-swap method for the dynamic economic load dispatch problem. Based on the premise that all generators shall be operated at valve-points, the proposed algorithm initially sets the maximum generation power at $P_i{\leftarrow}P_i^{max}$. As for generator i with $_{max}c_i$, which is the maximum operating cost $c_i=\frac{F(P_i)-F(P_{iv_k})}{(P_i-P_{iv_k})}$ produced when the generation power of each generator is reduced to the valve-point $v_k$, the algorithm reduces i's generation power down to $P_{iv_k}$, the valve-point operating cost. When ${\Sigma}P_i-P_d$ > 0, it reduces the generation power of a generator with $_{max}c_i$ of $c_i=F(P_i)-F(P_i-1)$ to $P_i{\leftarrow}P_i-1$ so as to restore the equilibrium ${\Sigma}P_i=P_d$. The algorithm subsequently optimizes by employing an adult-step method in which power in the range of $_{min}\{_{max}(P_i-P_i^{min}),\;_{max}(P_i^{max}-P_i)\}$>${\alpha}{\geq}10$ is reduced by 10; a baby step method in which power in the range of 10>${\alpha}{\geq}1$ is reduced by 1; and a swap method for $_{max}[F(P_i)-F(P_i-{\alpha})]$>$_{min}[F(P_j+{\alpha})-F(P_j)]$, $i{\neq}j$ of $P_i=P_i{\pm}{\alpha}$, in which power is swapped to $P_i=P_i-{\alpha}$, $P_j=P_j+{\alpha}$. It finally executes minute swap process for ${\alpha}=\text{0.1, 0.01, 0.001, 0.0001}$. When applied to various experimental cases of the dynamic economic load dispatch problems, the proposed algorithm has proved to maximize economic benefits by significantly reducing the optimal operating cost of the extant Heuristic algorithm.