• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1168-1171
• /
• 2003

The visuo-motor map is based on the Kohonen's self-organizing map. The map is learned the relation of the end effecter coordinates and the joint angles. In this paper, a 3 d-o-fmanipulator which moves in the 2D space is targeted. A CCD camera is set beside the manipulator, and the end effecter coordinates are given from the image of a manipulator. As a result of learning, the end effecter can be moved to the destination without exact teaching.

• Proceedings of the Korea Contents Association Conference
• /
• 2015.05a
• /
• pp.3-4
• /
• 2015

A self-organizing map (SOM) is a good tool to visualize applied data in the form of a feature map. With the help of such functions, a disaster risk diagnosis based on the residential house structure is tried in this study. According to some computer simulations with actual residential data, it is found that overall tendencies in the developed feature map are acceptable. Then, it is concluded that the proposed method is an effective means to estimate disaster risk appropriately.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.124-129
• /
• 2005

The Growing Self-Organizing Map (GSOM), an extended type of the Self-Organizing Map, is a widely accepted tool for clustering high dimensional data. It is also suitable for the clustering of short DNA sequences of phylogenetic genomes by their oligonucleotide frequency. The GSOM presents the result of the clustering process visually on a coloured map, where the clusters can be identified by the user. This paper describes a proposal for automatic cluster detection on this map without any participation by the user. It has been applied with good success on 20 different data sets for the purpose of species separation.

• Journal of Korea Water Resources Association
• /
• v.51 no.5
• /
• pp.383-393
• /
• 2018

The regional frequency analysis is the method which uses not only sample of target station but also sample of neighborhood stations in which are classified as hydrological homogeneous regions. Consequently, identification of homogeneous regions is a very important process in regional frequency analysis. In this study, homogeneous regions for regional frequency analysis of precipitation were identified by the self-organizing map (SOM) which is one of the artificial neural network. Geographical information and hourly rainfall data set were used in order to perform the SOM. Quantization error and topographic error were computed for identifying the optimal SOM map. As a result, the SOM model organized by $7{\times}6$ array with 42 nodes was selected and the selected stations were classified into 6 clusters for rainfall regional frequency analysis. According to results of the heterogeneity measure, all 6 clusters were identified as homogeneous regions and showed more homogeneous regions compared with the result of previous study.

• Proceedings of the Korean Information Science Society Conference
• /
• 2010.06a
• /
• pp.254-255
• /
• 2010

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.394-394
• /
• 2000

This Paper proposes a new nonlinear partial least square method that extends the linear PLS. Proposed nonlinear PLS uses self-organizing feature map as PLS outer relation and multilayer neural network as PLS inner regression method.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1315-1318
• /
• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.

• Journal of the Korea Society of Computer and Information
• /
• v.7 no.4
• /
• pp.204-210
• /
• 2002

The self-organizing feature map of Kohonen has disadvantage that needs too much input patterns in order to converge into the equilibrium state when it trains. In this paper we proposed the method of improving the convergence speed and rate of self-organizing feature map converting the interaction set into Dynamic Gaussian function. The proposed method Provides us with dynamic Properties that the deviation and width of Gaussian function used as an interaction function are narrowed in proportion to learning times and learning rates that varies according to topological position from the winner neuron. In this Paper. we proposed the method of improving the convergence rate and the degree of self-organizing feature map.

• Communications for Statistical Applications and Methods
• /
• v.10 no.2
• /
• pp.507-517
• /
• 2003

Self-organizing map (SOM) has been developed mainly by T. Kohonen and his colleagues as a unsupervised learning neural network. Because of its topological ordering property, SOM is known to be very useful in pattern recognition and text information retrieval areas. Recently, data miners use Kohonen´s mapping method frequently in exploratory analyses of large data sets. One problem facing SOM builder is that there exists no sensible criterion for evaluating goodness-of-fit of the map at hand. In this short communication, we propose valid evaluation procedures for the Kohonen SOM of any size. The methods can be used in selecting the best map among several candidates.

• The Korean Journal of Applied Statistics
• /
• v.15 no.2
• /
• pp.251-267
• /
• 2002

Self Organizing Maps(SOM) by Kohonen is very fast algorithm in neural networks. But it doesn't show sure rules of training results. In this paper, we introduce to Bayesian Learning for Self Organizing Maps(BLSOM) which combines self organizing maps with Bayesian learning. So it supports explanatory power of models and improves prediction. BLSOM has global optima anywhere but SOM has not. This is proved by experiment in this paper.