• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.308-318
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• 2004

A subject to recognize the damaged facial image is becoming an important issue in commercialization of automatic face recognition. The method to recognize a face on a damaged image is divided into two types. The one is to recognize remainders after removing the damaged information and the other is to recognize a total face after recovering the damaged information. On this paper, we present the reconstruction method by analyzing the main materials after extracting the damaged region through Kohonen network. The suggested algorithm in this paper estimates feature vectors of the damaged region using eigen-faces in PCA and then reconstructs the damaged image. This allows also the reconstruction under the untrained images. Through testing the artificial images where the eye and the mouth which have many effects to face recognition are damaged, the recognition rate of the proposed results showed similar results with the method which used Kohonen network, and improved about 11.8% more than symmetrical property method. Also, in case of the untrained image, our results improved about 14% more than that of the Kohonen method and about 7% more than that of the symmetrical property method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.207-210
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• 2000

학습법칙은 신경회로망의 성능을 좌우하는 중요한 요소의 하나이다. Kohonen의 합습법칙등이 개발되어 사용되어 왔으나 Underutilization 문제가 있어 실제 사용사에 문제가 있어 왔다. 본 논문에서 제시하는 학습법칙은 이를 부분적으로 해결하였다. 또한 이 학습법칙을 ART(Adaptive Resonance Theory)-1과 Kohonen의 자기 구조 특징 지도의 장점을 조합한 개선된 IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하였고, 성능을 평가하기 위해 가우시안 분포의 데이터와 IRIS 데이터를 각각 사용하여 실험하였다.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.86-94
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• 1995

Bead shape in high frequency electric resistance (HER) pipe welding gives useful information on judging current welding conditon. In most welding process, heat input is controlled by skilled operators observing color and shape of bead. In this paper, a visual monitoring system is designed to observe bead shape in HERW pipe welding process by using structured light beam and a C.I.D(Charge injection device) camera. To avoid some difficul- ties arising in extracting stable features of stripe pattern and classifying the extracted features, Kohonen neural network is used to classify such bead shapes. The experimental results show accurate classification performance of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.2
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• pp.276-283
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• 1994

Using a neural network for vector quantization, we can expect to have better codebook design algorithm for its adaptive process. Also, the designed codebook puts the codewords in order by its self-organizing characteristics, which makes it possible to partially search the codebook for real time process. To exploit these features of the neural network, in this paper, we propose a new codebook design algorithm that modified the KSFM(Kohonen`s Self-organizing Feature Map) and then combines the K-means algorithm. Experimental results show the performance improvment and the ability of the partical seach of the codebook for the real time process.

• The Korean Journal of Ecology
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• v.23 no.2
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• pp.89-100
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• 2000

On community data. sampled in regular intervals on a long-term basis. artificial neural networks were implemented to extract information on characterizing patterns of community changes. The Adaptive Resonance Theory and Kohonen Network were both utilized in learning benthic macroinvertebrate communities in the Soktae Stream of the Suyong River collected monthly for three years. Initially, by regarding each monthly collection as a separate sample unit, communities were grouped into similar patterns after training with the networks. Subsequently, changes in communities in a sequence of samplings (e.g., two-month, four-month, etc.) were given as input to the networks. After training, it was possible to recognize new data set in line with the sampling procedure. Through the comparative study on benthic macroinvertebrates with these learning processes, patterns of community changes in chironomids diverged while those of the total benthic macro-invertebrates tended to be more stable.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.164-169
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• 2008

The increasing late-payment rate of credit card customers caused by a recent economic downturn are incurring not only reduced profit of department stores but also significant loss. Under this pressure, the objective of credit forecasting is extended from presumption of good or bad customers to contribution to revenue growth. As a method of managing defaults of department store credit card, this study classifies credit delinquents into some clusters, analyzes repaying patterns of customers in each cluster, and develops credit forecasting system to manage delinquents of department store credit card using data of Korean D department store's delinquents. The model presented by this study uses Kohonen network, a kind of artificial neural network of data mining techniques to cluster credit delinquents into groups. Logistic regression model is also used to predict repayment rate of customers of each cluster per period. The accuracy of presented system for the whole clusters is 92.3%.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.591-593
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• 1995

Artificial neural network based pattern recognition method is one of the most probable candidate for on-line power system transient stability analysis. Especially, Kohonen layer is an adequate neural network for the purpose. Each node of Kehonen layer competes on the basis of which of them has its clustering center closest to an input vector. This paper discusses Kohonen's LVQ(Learning Victor Quantization) and points out a defection of the algorithm when applied to the transient stability analysis. Only the clustering centers located near the decision boundary of the stability region is needed for the stability criterion and the centers far from the decision boundary are redundant. This paper presents a new algorithm ratted boundary searching algorithm II which assigns only the points that are near the boundary in an input space to nodes or Kohonen layer as their clustering centers. This algorithm is demonstrated with satisfaction using 4-generator 6-bus sample power system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.63.5-63
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• 2001

In this paper, moving objects tracking and dynamic characteristic analysis are studied. Kohonen´s self-organizing neural network models are used for moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.864-866
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• 1997

This paper presents fault recognition and interpretation in power transformers using dissolved gas analysis embedded Kohonen feature mapping. The imprecision of gas ratio analysis in dissolved gas analysis are managed by mapping in accordance with learning of Kohonen neural network. To verify the effectiveness of the proposed system, it has been tested by the historical gas records to power transformers of Korea Electric Power Corporation. More appropriate fault types can support the maintenance personnels to increase the disgnostic performance for fault of power transformers.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.651-656
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• 2001

In this paper, we propose Improved Self-Organized Graph(Improved SOG：$SOG^*$)thinning method, which maintains the excellent thinning results of Self-organized graph(SOG) built from Self-Organizing features map and improves the performance of modified SOG using a new incremental learning method of Kohonen features map. In the experiments, this method shows the thinning results equal to those of SOG and the time complexity O((logM)3) superior to it. Therefore, the proposed method is useful for the feature extraction from digits and characters in the preprocessing step.