• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.180-188
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• 1995

A modified Kohonen's self-organizing feature map (SOFM) algorithm which has binary reinforcement function and a constant learning rate is proposed. In contrast to the time-varing adaptaion gain of the original Kohonen's SOFM algorithm, the proposed algorithm uses a constant adaptation gain, and adds a binary reinforcement function in order to compensate for the lowered learning ability of SOFM due to the constant learning rate. Since the proposed algorithm does not have the complicated multiplication, it's digital hardware implementation is much easier than that of the original SOFM.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.10
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• pp.515-522
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• 2004

In this study, we tried to investigate that the advantages of SOM(Self Organizing Map) algorithm such as data accumulation ability and the degradation trend trace ability would be adaptable to the analysis of partial discharge pattern recognition. For the purpose, we analyzed partial discharge data obtained from the typical artificial defects in GIS and XLPE power cable system through SOM algorithm. As a result, partial discharge pattern recognition could be well carried out with an acceptable error by use of Kohonen map in SOM algorithm. Also, it was clarified that the additional data could be accumulated during the operation of the algorithm. Especially, we found out that the data accumulation ability of Kohonen map could make it possible to suggest new patterns, which is impossible through the conventional BP(Back Propagation) algorithm. In addition, it is confirmed that the degradation trend could be easily traced in accordance with the degradation process. Therefore, it is expected to improve on-site applicability and to trace real-time degradation trends using SOM algorithm in the partial discharge pattern recognition

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

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.339-348
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• 1997

In this paper, we propose a coding method to improve compression efficiency for MR image. This can be achieved by combining coding scheme and segmentation scheme which removes noisy background region, which is meaningless for diagnosis in the MR image. In segmentation algoritm, we use full-resolution wavelet transform to extract features of regions in image and Kohonen self-organizing map to classify the features. The subsequent wavelet coder encodes only diagnostically significant foreground regions refering to segmentation map. Our proposed algorithm provides about 15% of bit rate reduction when compared with the same coder which is not combined with segmentation scheme. And the proposed scheme shows better reconstructed image quality than JPEG at the same compression ratio.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2033-2036
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• 2004

A novel approach to estimate the real time moving trajectory of an object is proposed in this paper. The object position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Kalman filter and neural networks are utilized. Since the Kalman filter needs to approximate a non-linear system into a linear model to estimate the states, there always exist errors as well as uncertainties again. To resolve this problem, the neural networks are adopted in this approach, which have high adaptability with the memory of the input-output relationship. Kohonen Network(Self-Organized Map) is selected to learn the motion trajectory since it is spatially oriented. The superiority of the proposed algorithm is demonstrated through the real experiments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.347-356
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• 2009

This paper is described clustering analysis of traffic characteristics-based highway classification in order to deviate from methodologies of existing highway functional classification. This research focuses on comparing the clustering techniques performance based on the total within-group errors and deriving the optimal number of cluster. This research analyzed statistical clustering method (Hierarchical Ward's minimum-variance method, Nonhierarchical K-means method) and Kohonen self-organizing maps clustering method for highway characteristic classification. The outcomes of cluster techniques compared for the number of samples and traffic characteristics from subsets derived by the optimal number of cluster. As a comprehensive result, the k-means method is superior result to other methods less than 12. For a cluster of more than 20, Kohonen self-organizing maps is the best result in the cluster method. The main contribution of this research is expected to use important the basic road attribution information that produced the highway characteristic classification.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.239-241
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• 1999

This paper proposes a novel wavelet transform and Kohonen neural network based technique for short-time load forecasting of power systems. Firstly. Kohonen Self-organizing map(KSOM) is applied to classify the loads and then the Daubechies D2, D4 and D10 wavelet transforms are adopted in order to forecast the short-term loads. The wavelet coefficients associated with certain frequency and time localisation are adjusted using the conventional multiple regression method and then reconstructed in order to forecast the final loads through a four-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of Kohonen neural network and wavelet transform approach can be used as an attractive and effective means for short-term load forecasting.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2516-2518
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• 2003

본 논문은 고차 뉴런의 문제점으로 지적되고 있는 뉴런이 방대하게 증가하는 문제를 해결하고자, 최적의 뉴런을 생성하고 생성되어진 고차 뉴런 중 일정 비율로 뉴런의 연결강도를 도태시켜 감에 따라 네트워크상에 나타나는 특성을 비교하였다. 본 논문은 고차 뉴런을 이용한 Kohonen의 자기 조직화 맵의 고차 뉴런부에 일정 비율로 연결강도를 도태한 후 인식률을 얻는 형태로 시뮬레이션을 하였다. 특히, 종래 형태의 고차 뉴런을 이용한 Kohonen 자기 조직화 맵의 알고리즘을 변형없이 사용하였으며 중복되는 뉴런을 최대한 억제하기 위해 2차 뉴런만을 생성한 네트워크 구조 위에 입력 데이터의 특징을 유지하고 고차 뉴런의 특징을 더욱 활성화하기 위해 일정한 양의 연결강도를 도태시킴으로써 출력면에서 국소집중 반응에 의한 정확한 인식률 향상 등을 조사하는 시뮬레이션을 하였다. 본 제안 모델의 특성을 살펴보기 위해 60개의 데이터로 이루어진 금속 소나 음데이터와 암석 소나 음 데이터를 이용하여 금속인지 암석인지를 판별하는 시뮬레이션을 하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1427-1431
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• 2007

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.315-318
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• 1996

The machine-part cell formation means the grouping of similar parts and similar machines into families in order to minimize bottleneck machines, bottleneck parts, and inter-cell part movements in cellular manufacturing systems and flexible manufacturing systems. The cell formation problem is knows as a kind of NP complete problems. This paper briefly introduces the cell-formation problem and proposes a cell formation method based on the Kohonen's self-organizing feature map which is a neural network model. It also shows some experiment results using the proposed method. The proposed method can be easily applied to the cell formation problem compared to other meta-heuristic based methods. In addition, it can be used to solve large-scale cell formation problems.