• The KIPS Transactions:PartB
• /
• v.15B no.5
• /
• pp.407-412
• /
• 2008

Color segmentation is one of the most challenging problems in image processing especially in case of handling the images with cluttered background. Great amount of color segmentation methods have been developed and applied to real problems. In this paper, we suggest a new methodology. Our approach is focused on background extraction, as a complimentary operation to standard foreground object segmentation, using self-organizing feature selective property of unsupervised self-learning paradigm based on the competitive algorithm. The results of our studies show that background segmentation can be achievable in efficient manner.

• Proceedings of the KIEE Conference
• /
• 1997.11a
• /
• pp.591-593
• /
• 1997

When input circumstance is changed abrubtly, many nodes of Competitive Learning Neural Networks far from new input vector may never win, and therefore never learn. Various techniques to prevent these phenomena have been reported. We proposed a new technique based on Self Creating and Organizing Neural Networks, and which is compared to Self Organizing Feature Map and Frequency Sensitive Neural Networks.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.05b
• /
• pp.9-18
• /
• 2005

In order to systematically and visually understand well-known but qualitative and rotatively complicated relationships between synoptic fields in the BAIU season and heavy rainfall events in Japan, these synoptic fields were classified using the Self-Organizing Map (SOM) algorithm. This algorithm can convert complex nonlinear features into simple two-dimensional relationships, and was followed by the application of the clustering techniques of the U-matrix and the K-means. It was assumed that the meteorological field patterns be simply expressed by the spatial distribution of wind components at the 850 hPa level and Precipitable Water (PW) in the southwestern area including Kyushu in Japan. Consequently, the synoptic fields could be divided into eight kinds of patterns (clusters). One of the clusters has the notable spatial feature represented by high PW accompanied by strong wind components known as Low-Level Jet (LLJ). The features of this cluster indicate a typical meteorological field pattern that frequently causes disastrous heavy rainfall in Kyushu in the rainy season. From these results, the SOM technique may be an effective tool for the classification of complicated non-linear synoptic fields.

• Journal of the Korea Society for Simulation
• /
• v.21 no.2
• /
• pp.79-90
• /
• 2012

In this paper, a feature signal extraction method is proposed in order to enhance the low performance of fault detection caused by unbalanced data which denotes the situations when severe disparity exists between the numbers of class instances. Most of the cyclic signals gathered during the process are recognized as normal, while only a few signals are regarded as fault; the majorities of cyclic signals data are unbalanced data. SOM(Self-Organizing Map)-based feature signal extraction method is considered to fix the adverse effects caused by unbalanced data. The weight neurons, mapped to the every node of SOM grid, are extracted as the feature signals of both class data which are used as a reference data set for fault detection. kNN(k-Nearest Neighbor) and SVM(Support Vector Machine) are considered to make fault detection models with comparisons to Hotelling's $T^2$ Control Chart, the most widely used method for fault detection. Experiments are conducted by using simulated process signals which resembles the frequent cyclic signals in semiconductor manufacturing.

• Journal of Korean Institute of Industrial Engineers
• /
• v.31 no.3
• /
• pp.257-264
• /
• 2005

This paper suggests a heuristic algorithm for the clustering problem. Clustering involves grouping similar objects into a cluster. Clustering is used in a wide variety of fields including data mining, marketing, and biology. Until now there are a lot of approaches using Self-Organizing Feature Maps(SOFMs). But they have problems with a small output-layer nodes and initial weight. For example, one of them is a one-dimension map of k output-layer nodes, if they want to make k clusters. This approach has problems to classify elaboratively. This paper suggests one-dimensional output-layer nodes in SOFMs. The number of output-layer nodes is more than those of clusters intended to find and the order of output-layer nodes is ascending in the sum of the output-layer node's weight. We can find input data in SOFMs output node and classify input data in output nodes using Euclidean distance. We use the well known IRIS data as an experimental data. Unsupervised clustering of IRIS data typically results in 15 - 17 clustering error. However, the proposed algorithm has only six clustering errors.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.51 no.6
• /
• pp.276-283
• /
• 2002

This Paper Presents the method for detecting a partial discharge(PD) using an electromagnetic wave measured by an antenna. The various electromagnetic waves are measured in the laboratory and wavelet transform, which is provides a direct quantitative measure of spectral content in the time-frequency domain, are applied for identifying the property of electromagnetic waves. Also, the statistical method and self-organizing feature map(SOFM) are applied for the pattern recognition of electromagnetic waves. The proposed method is shown to be useful for detecting electromagnetic waves emitted for PD in test data.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.2
• /
• pp.116-120
• /
• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.8
• /
• pp.64-70
• /
• 1993

This paper presents an efficient SOFM(self-organizing feature map) algorithm for the solution of the large scale TSPs(traveling salesman problems). Because no additional winner neuron for each city is created in the next competition, the proposed algorithm requires just only the N output neurons and 2N connections, which are fixed during the whole process, for N-city TSP, and it does not requires any extra algorithm of creation of deletion of the neurons. And due to direct exploitation of the output potential in adaptively controlling the neighborhood, the proposed algorithm can obtain higher convergence rate to the suboptimal solutions. Simulation results show about 30% faster convergence and better solution than the conventional algorithm for solving the 30-city TSP and even for the large scale of 1000-city TSPs.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.21 no.7
• /
• pp.75-83
• /
• 2007

IEC code based decision nile have been widely applied to detect incipient faults in power transformers. However, this method has a drawback to achieve the diagnosis with accuracy without experienced experts. In order to resolve this problem, we propose an artificial diagnosis algorithm to detect faults of power transformers using Self-Organizing Feature Map(SOM). The proposed method has two stages such as model construction and diagnostic procedure. First, faulty model is constructed by feature maps obtained by unsupervised learning for training data. And then, diagnosis is performed by compare feature map with it obtained for test data. Also the proposed method usぉms the possibility and degree of aging as well as the fault occurred in transformer by clustering and distance measure schemes. To demonstrate the validity of proposed method, various experiments are unformed and their results are presented.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.9A
• /
• pp.1411-1418
• /
• 1999

In this paper, we propose a fast motion estimation algorithm using motion prediction and neural network. Considering that the motion vectors have high spatial correlation, the motion vector of current block is predicted by those of neighboring blocks. The codebook of motion vector is designed by Kohonen self-organizing feature map(KSFM) learning algorithm which has a fast learning speed and 2-D adaptive chararteristics. Since the similar codevectors are closely located in the 2-D codebook the motion is progressively estimated from the predicted codevector in the codebook. Computer simulation results show that the proposed method has a good performance with reduced computational complexity.