• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.32-40
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• 2008

Fuzzy rules are very useful and efficient to describe classification rules especially when the attribute values are continuous and fuzzy in nature. However, it is generally difficult to determine membership functions for generating efficient fuzzy classification rules. In this paper, we propose a method of automatic generation of efficient fuzzy classification rules using evolutionary algorithm. In our method we generate a set of initial membership functions for evolutionary algorithm by supervised clustering the training data set and we evolve the set of initial membership functions in order to generate fuzzy classification rules taking into consideration both classification accuracy and rule comprehensibility. To reduce time to evaluate an individual we also propose an evolutionary algorithm with data partition evaluation in which the training data set is partitioned into a number of subsets and individuals are evaluated using a randomly selected subset of data at a time instead of the whole training data set. We experimented our algorithm with the UCI learning data sets, the experiment results showed that our method was more efficient at average compared with the existing algorithms. For the evolutionary algorithm with data partition evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that evaluation time was reduced by about 70%. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• The Journal of the Acoustical Society of Korea
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• v.31 no.4
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• pp.266-272
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• 2012

Speaker segmentation is a process of automatically detecting the speaker boundary points in the audio data. Speaker segmentation methods are divided into two categories depending on whether they use a prior knowledge or not: One is the model-based segmentation and the other is the metric-based segmentation. In this paper, we introduce the eigenvoice-based speaker weight distance method and compare it with the representative metric-based methods. Also, we employ and compare the Euclidean and cosine similarity functions to calculate the distance between speaker weight vectors. And we verify that the speaker weight distance method is computationally very efficient compared with the method directly using the distance between the speaker adapted models constructed by the eigenvoice technique.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1011-1020
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• 2004

A lot of work has been done to improve the I/O performance of such a system that store and manage a massive amount of data by distributing them across multiple disks and access them in parallel. Most of the previous work has focused on an efficient mapping from a grid ceil, which is determined bY the interval number of each dimension, to a disk number on the assumption that each dimension is split into disjoint intervals such that entire data space is GRID-like partitioned. However, they have ignored the effects of a GRID partitioning scheme on declustering performance. In this paper, we enhance the performance of mapping function based declustering algorithms by applying a good GRID par-titioning method. For this, we propose an estimation model to count the number of grid cells intersected by a range query and apply a GRID partitioning scheme which minimizes query result size among the possible schemes. While it is common to do binary partition for high-dimensional data, we choose less number of dimensions than needed for binary partition and split several times along that dimensions so that we can reduce the number of grid cells touched by a query. Several experimental results show that the proposed estimation model gives accuracy within 0.5% error ratio regardless of query size and dimension. We can also improve the performance of declustering algorithm based on mapping function, called Kronecker Sequence, which has been known to be the best among the mapping functions for high-dimensional data, up to 23 times by applying an efficient GRID partitioning scheme.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.769-778
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• 2004

Object segmentation is a challenging Problem when the background is cluttered and the objects are overlapped one another. Recent develop-ment using snake algorithms proposed to segment objects from a 2-D Image presents a higher possibilityfor getting better contours. However, the performance of those snake algorithms degrades rapidly when the background is cluttered and objects are overlapped one another, Moreover, the initial snake point placement is another difficulty to be resolved. Here, we propose a novel snake algorithm for object segmentation using disparity information taken from a set of stereo images. By applying our newly designed snake energy function defined in the disparity space, our algorithmeffectively circumvents the limitations found in the previous methods. The performance of the proposed algorithm has been verified by computer simulation using various stereo image sets. The experiment results have exhibited a better performance over the well-known snake algorithm in terms of segmentation accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.195-202
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• 2005

An adaptive segmentation algorithm is used to efficiently target decisions in local non-stationary images. Until now, several adaptive approaches have been proposed as a method of segmentation. However, they can't be directly used for radar target detection because a radar signal has different characteristics from general images. Generally, a histogram of radar signal shows that targets have a relatively small number of frequency functions compared to the background and distribution of background, which have several shapes as the environment changes. In this paper, we propose an adaptive segmentation algorithm using a histogram chord which is a right-down line from maximum pick of frequency function. The proposed method provides thresholds which are optimum for several radar environments because the used chord for threshold search is not significantly effected by interference conditions. Simulation results show that the proposed method is superior to the traditional algorithms, global threshold method and distribution median method, with respect to detection performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.470-471
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• 2018

This paper presents a design method for combinational digital logic systems using time domain based multiplexing and common multi-terminal extension decision diagrams. The proposed method can reduce the 1)hardware, 2)logic levels and 3)pins. In the logic system design, we use two types of decision diagrams(DDs), that is the common binary decision diagrams and CMTEDDs. Also, we propose an algorithms to derive common multiple-terminal binary decision diagrams from CBDDs, and CMTEDDs from CMTBDDs. The CMTEDDs over CBDDs is more compactness in terms of number of non-terminal nodes, where the nodes for output selection variables are not included in the non-terminal nodes. In the logic design, each non-terminal nodes of an CBDDs and an CMTEDDs is realized by a multiplexer(MUX). In addition, we compare the proposed TDBM realization with the conventional one.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.4
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• pp.469-476
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• 2007

A domain/boundary decomposition technique is applied to carry out efficient finite element analyses of 3-D contact problems. Appropriate penalty functions are selected for connecting an interface and contact interfaces with neighboring subdomains that satisfy continuity constraints. As a consequence, all the effective stiffness matrices have positive definiteness, and computational efficiency can be improved to a considerable degree. If necessary, any complex-shaped 3-D domain can be divided into several simple-shaped subdomains without considering the conformity of meshes along the interface. With a set of numerical examples, the basic characteristics of computational efficiency are investigated carefully.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.687-688
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• 2011

This paper presents a design method for combinational digital logic systems using time domain based multiplexing(TDBM) and common multi-terminal extension decision diagrams(CMTEDD). The proposed method can reduce the 1)hardware, 2)logic levels and 3)pins. In the logic system design, we use two types of decision diagrams(DDs), that is the common binary decision diagrams(CBDDs) and CMTEDDs. Also, we propose an algorithms to derive common multiple-terminal binary decision diagrams(CMTBDD) from CBDDs, and CMTEDDs from CMTBDDs. The CMTEDDs over CBDDs is more compactness in terms of number of non-terminal nodes, where the nodes for output selection variables are not included in the non-terminal nodes. In the logic design, each non-terminal nodes of an CBDDs and an CMTEDDs is realized by a multiplexer(MUX). In addition, we compare the proposed TDBM realization with the conventional one.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.398-399
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• 2019

This paper presents a method of the circuit design of the multiple-valued digital logic switching functions based on the modular techniques. Fisr of all, we introduce the necessity, background and concepts of the modular design techniques for the digital logic systems. Next, we discuss the definitions that are used in this paper. For the purpose of the circuit design for the multiple-valued digital logic switching functions, we discuss the extraction of the partition functions. Also we describe the construction method of the building block, that is called the modules, based on each partition functions. And we apply the proposed method to the example, we compare the results with the results of the earlier methods. In result, we decrease the control functions, it means that we obtain the effective cost in the digital logic design for any other earlier methods. In the future research, we require the universal module that traet more partition functions and more compact module.

• Journal of the Korea Computer Graphics Society
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• v.18 no.2
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• pp.9-15
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• 2012

In this paper, we propose a method to segment a color image into several meaningful regions. We suppose that the meaningful region has a set of colors with high frequency in the color image. To find these colors, the color image is represented as several sets of color points in RGB space. And when we use the density of points defined in this method, color belonging to a dense region of color points in RGB space refers to the color that appeared frequently in the image. Eventually, we can find meaningful regions by looking for regions with high density of color points using our level set function in RGB space. However, if a meaningful region does not have a contiguous region of the sufficient size in the image, this is not a meaningful region but meaningless region. Thus, the pixels in the meaningless region are assigned to the biggest meaningful region belonging to its neighboring pixels in the color image. Our method divides the color image into meaningful regions by applying the density of color points to level set function in RGB space. This is different from the existing level set method that is defined only in 2D image.