• Structural Engineering and Mechanics
• /
• v.63 no.2
• /
• pp.237-249
• /
• 2017

The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.11a
• /
• pp.382.1-382
• /
• 2002

This paper presents a new approach integrated Case-Based Reasoning with Self- Organization Map(SOM) in diagnosis systems. The causes of faults are obtained by case-base trained from SOM. When the vibration problem of rotating machinery occurs, this provides an exact diagnosis method that shows the fault cause of vibration problem. In order to verify the performance of algorithm, we applied it to diagnose the fault cause of the electric motor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.11b
• /
• pp.832-835
• /
• 2002

This paper presents a new approach integrated Case-Based Reasoning with Self. Organization Map(SOM) in diagnosis systems. The causes of faults are obtained by case-base trained from SOM. When the vibration problem of rotating machinery occurs, this provides an exact diagnosis method that shows the fault cause of vibration problem. In order to verify the performance of algorithm, we applied it to diagnose the fault cause of the electric motor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.995-998
• /
• 2013

Aerial imaging is one of the most common and versatile ways of obtaining information from the Earth surface. In this paper, we present an approach by SOM(Self Organization Map) algorithm with 2 phase learning to be applied successfully to aerial images clustering due to its signal-to-noise independency. A comparison with other classical method, such as K-means and traditional SOM, of real-world areal image clustering demonstrates the efficacy of our approach.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.6
• /
• pp.43-54
• /
• 2005

This paper proposes a novel texture segmentation method using Bayesian image segmentation method and SOM(Self Organization feature Map). Multi-scale wavelet coefficients are used as the input of SOM, and likelihood and a posterior probability for observations are obtained from trained SOMs. Texture segmentation is performed by a posterior probability from trained SOMs and MAP(Maximum A Posterior) classification. And the result of texture segmentation is improved by context information. This proposed segmentation method shows better performance than segmentation method by HMT(Hidden Markov Tree) model. The texture segmentation results by SOM and multi-sclae Bayesian image segmentation technique called HMTseg also show better performance than by HMT and HMTseg.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.25 no.4
• /
• pp.97-109
• /
• 2000

This work proposes a neural network approach to solve vehicle routing problems which have diverse application areas such as vehicle routing and robot programming. In solving these problems, classical mathematical approaches have many difficulties. In particular, it is almost impossible to implement a real-time vehicle routing with multiple vehicles. Recently, many researchers proposed methods to overcome the limitation by adopting heuristic algorithms, genetic algorithms, neural network techniques and others. The most basic model for path planning is the Travelling Salesman Problem(TSP) for a minimum distance path. We extend this for a problem with dynamic upcoming of new positions with multiple vehicles. In this paper, we propose an algorithm based on SOM(Self-Organization Map) to obtain a sub-optimal solution for a real-time vehicle routing problem. We develope a model of a generalized multiple TSP and suggest and efficient solving procedure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2003.10a
• /
• pp.188-191
• /
• 2003

In this paper, we proposed data generalization and data cube mechanism for efficient data mining in distribute environment. We also proposed active Self Organization Map applying traditional Self Organization Map of Neural network for searching the most Informative data created from data cube after the generalization procedure and designed the system architecture for that.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.39 no.8
• /
• pp.689-701
• /
• 2011

The relations between ambient conditions and ice accretion shapes are quantitatively analyzed by employing self-organization maps and analysis of variance. Liquid water contents(LWC), mean volumetric droplet diameter(MVD), ambient temperature and free-stream velocity are chosen as ambient conditions which change ice accretion shapes. The parameters of ice accretion shape are selected as maximum thickness, icing limits, ice heading, and ice accretion area. Qualitative analysis was conducted by employing self-organization maps which show the qualitative relations between ice shapes and ambient conditions. The quantitative results of analysis of variance yield intensity of ambient conditions to the parameters of ice accretion shapes.

• Journal of Korean Society on Water Environment
• /
• v.24 no.5
• /
• pp.611-617
• /
• 2008

It is necessary to analysis the data characteristics of discharge and water quality for efficient water resources management, aggressive alternatives to inundation by flood and various water pollution accidents, the basic information to manage water quality in lakes and to make environmental policy. Therefore, the present study applied Self-Organizing Map (SOM) showing excellent performance in classifying patterns with weights estimated by self-organization. The result revealed five patterns and TOC versus rainfall-storage data according to the respective patterns were depicted in two-dimensional plots. The visualization presented better understanding of data distribution pattern. The result in the present study might be expected to contribute to the modeling procedure for data prediction in the future.