• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.637-640
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• 2011

The hybrid neural networks have characteristics such as fast learning times, generality, and simplicity, and are mainly used to classify learning data and to model non-linear systems. The middle layer of a hybrid neural network clusters the learning vectors by grouping homogenous vectors in the same cluster. In the clustering procedure, the homogeneity between learning vectors is represented as the distance between the vectors. Therefore, if the distances between a learning vector and all vectors in a cluster are smaller than a given constant radius, the learning vector is added to the cluster. However, the usage of a constant radius in clustering is the primary source of errors and therefore decreases the recognition success rate. To improve the recognition success rate, we proposed the enhanced hybrid network that organizes the middle layer effectively by using the enhanced ART1 network adjusting the vigilance parameter dynamically according to the similarity between patterns. The results of experiments on a large number of calling card images showed that the proposed algorithm greatly improves the character extraction and recognition compared with conventional recognition algorithms.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.121-128
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• 2005

This study presents an effective stiffness-based optimal technique to control quantitatively lateral drift for shear wall structures subject to lateral loads. To this end the displacement sensitivity depending on behavior characteristics of shear wall structures is established. Also, the approximation concept that can preserve the generality of the mathematical programming and can efficiently solve large scale problems is introduced. Resizing sections in the stiffness-based optimal design are assumed to be uniformly varying in size and the technique of member grouping is considered for the improvement of construction efficiency Two types of 11-story shear wall structures are presented to illustrate the features of the quantitative lateral drift control technique proposed in this study.

• IE interfaces
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• v.15 no.2
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• pp.182-188
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• 2002

Clustering is widely used in various fields in order to investigate structural characteristics of the given data. One of the main tasks of clustering is to partition a set of objects into homogeneous groups for the purpose of data reduction. In this paper a simple but computationally efficient clustering procedure is devised and some statistical techniques to validate its clustered results are discussed. In the given procedure, the proper number of clusters and the clustered groups can be determined simultaneously. The whole procedure is applied to a practical clustering problem for the classification of reusable fuels in nuclear power plants.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.58-69
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• 1995

In this paper GFI (Generalized Fuzzy Isodata) and FI (Fuzzy Isodata) algorithms are studied and applied to the tire tread pattern classification problem. GFI algorithm which repeatedly grouping the partitioned cluster depending on the fuzzy partition matrix is general form of GI algorithm. In the constructing the binary tree using GFI algorithm cluster validity, namely, whether partitioned cluster is feasible or not is checked and construction of the binary tree is obtained by FDH clustering algorithm. These algorithms show the good performance in selecting the prototypes of each patterns and classifying patterns. Directions of edge in the preprocessed image of tire tread pattern are selected as features of pattern. These features are thought to have useful information which well represents the characteristics of patterns.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.18-23
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• 2008

A FCM with locational price and regional information between locations are proposed in this paper. Any point in a networked system has its own values indicating the physical characteristics of that networked system and regional information at the same time. The similarity measure used for FCM in this paper is defined through the system-wide characteristic values at each point. To avoid the grouping of geometrically distant locations with similar measures, the locational information are properly considered and incorporated in the proposed similarity measure. We have verified that the proposed measure has produced proper classification of a networked system, followed by an example of a networked electricity system.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.831-844
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• 2008

Since cooperatives consist of many distinct members in the management environment and characteristics, it is necessary to make similar cooperatives into a few groups for the effective risk management of cooperatives' mutuality bank. This paper is a priori research for suggesting a guidance for effective risk management of cooperatives with different management strategy. For such purpose, we propose a way to group the members of cooperative's mutuality bank. The 30 continuous variables which is relative to cooperatives' management status are considered and six factors are extracted from those variables through factor analysis with empirical consideration to avoid wrong grouping and to enhance the practical interpretation. Based on extracted six factors and additional 3 categorical variables, six representative groups are derived by the two step clustering analysis. These findings are useful to execute a discriminatory risk management and other management strategy for a mutuality bank and others.

• Journal of Korean Society for Quality Management
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• v.36 no.2
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• pp.28-35
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• 2008

This study deals with an Excel-based scheduling system for a small and medium sized manufacturing factory without sufficient capability for managing full-scale information systems. The factory has the bottleneck with identical machines and unique batching characteristics. The scheduling problem is formulated as a variation of the parallel-machine scheduling system. It can be solved by a two-phase method: the first phase with an ant colony optimization (ACO) heuristic for order grouping and the second phase with a mixed integer programming (MIP) algorithm for scheduling groups on machines.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.245-245
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• 2005

This paper addresses the use of directional textural information for connecting road segments. In urban scene, some roads are occluded by buildings, casting shadow of buildings, trees, and cars on streets. Automatic extraction of road network from remotely sensed high resolution imagery is generally hindered by them. The results of automatic road network extraction will be incomplete. To overcome this problem, several perceptual grouping algorithms are often used based on similarity, proximity, continuation, and symmetry. Roads have directions and are connected to adjacent roads with certain angles. The directional information is used to guide road fragments connection based on roads directional inertia or characteristics of road junctions. In the primitive stage, roads are extracted with textural and direction information automatically with certain length of linearity. The primitive road fragments are connected based on the directional information to improve the road network. Experimental results show some contribution of this approach for completing road network, specifically in urban area.

• Journal of the military operations research society of Korea
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• v.37 no.1
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• pp.147-159
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• 2011

Recently, the military service period has been shortened continuously. Meanwhile, more skilled airmen are needed as the complexity of weapon systems increase. This phenomenon could lead to a disastrous result such as deteriorating the level of the readiness and the fighting power. We suggest a method to improve recruit's maintenance capability rapidly by assigning airmen to jobs appropriate to their characteristics using Datamining methods (K-menas and CART). We focus on the assigning method for air force's air-munition maintenance airmen since they are requested more skilled than other airmen. Grouping airmen with k-means method and devising classification rule with CART algorithm, we found that airmen's proficiency arrival period could be shortened by 1.79 months when they are assigned in the suggested way.

• Journal of Industrial Technology
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• v.31 no.B
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• pp.37-43
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• 2011

In pattern analysis, dimensionality reduction and reasonable identification rule generation are very important parts. This paper performed effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, this paper constructed the hierarchically identifiable rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, this paper demonstrated the effectiveness of the proposed methods by identifying five types of gases.