• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.5
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• pp.85-91
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• 1989

In this paper, we propose a neural net system for speech recognition, which is composed of two neural networks. Firstly the self-supervised BP(Back Propagation) network generates the distributed concept corresponding to the activity pattern in the hidden units. And then the self-organizing neural network forms a concept map which directly displays the similarity relations between concepts. By doing the above, the difficulty in learning the conventional BP network is solved and the weak side of BP falling into a pattern matcher is gone, while the strong point of generating the various internal representations is used. And we have obtained the concept map which is more orderly than the Kohonen's SOFM. The proposed neural net system needs not any special preprocessing and has a self-learning ability.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1E
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• pp.20-24
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• 1994

A new speech recognition model, DLSMM(Dynamically Localized Self-organizing Map Model) and its effective training algorithm are proposed in this paper. In DLSMM, temporal and spatial distortions of speech are efficiently normalized by dynamic programming technique and localized self-organizing maps, respectively. Experiments on Korean digits recognition have been carried out. DLSMM has smaller Experiments on Korean digits recognition have been carried out. DLSMM has smaller connections than predictive neural network models, but it has scored a little high recognition rate.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.311-320
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• 2003

The Optimal filter yielding optimal texture feature separation is a most effective technique for extracting the texture objects from multiple textures images. But, most optimal filter design approaches are restricted to the issue of supervised problems. No full-unsupervised method is based on the recognition of texture objects in image. We propose a novel approach that uses unsupervised learning schemes for efficient texture image analysis, and the band-pass feature of Gabor-filter is used for the optimal filter design. In our approach, the self-organizing neural network for multiple texture image identification is based on block-based clustering. The optimal frequency of Gabor-filter is turned to the optimal frequency of the distinct texture in frequency domain by analyzing the spatial frequency. In order to show the performance of the designed filters, after we have attempted to build a various texture images. The texture objects extraction is achieved by using the designed Gabor-filter. Our experimental results show that the performance of the system is very successful.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.24-26
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• 1996

뇌는 신경세포로 이루어진 거대한 시스템이다. 이러한 뇌의 특징은 자기조직 시스템이면서 외계의 정보구조에 맞추어서 자신의 능력을 높일 수 있다는 것이다. 또한 뇌는 병렬정보처리 방식을 대폭적으로 채용한 시스템으로서 제어기구가 전체적으로 분산되어 있다. 이러한 뇌의 동작은 구조적으로 안정적이며 그 구성소자가 어느 정도 파괴되더라도 우수한 동작특성을 유지할 수 있다. 이것은 뇌에 있어서 정보가 거시화 및 분산화 되어 있다는 증거이며, 연상기억과 내용 어드레스 기억 등과 같은 탁월한 기억방식을 실현할 뿐만 아니라 망각능력도 가지고 있다. 현실의 뇌 그 자체를 조사하는 것이 어려운 상황에서는 뇌에 관한 여러 가지 모델을 만들고 이 모델을 구체적으로 상세히 조사함으로써 현실의 뇌를 이해하고자하는 방법이 중요시 된다. 본 강연에서는 이러한 구성적 방법론의 필요성 및 뇌의 생리학적 측면, 뇌의 모델로서의 측면 그리고 신경회로망의 발전단계와 뇌 과학의 세계적 연구동향에 관하여 살펴본다.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.72-79
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• 2010

A EAARS(Electronic Attendance-Absence Recording System) is the important LSS(Learning Support System) for blending a on-line learning in the face-to-face classroom. However, the EAARS based on the smart card can not identify a real owner of the checked card. Therefore, we develop the CS(Client-Sever) system that manages the attendance-absence checking automatically, which is used the self-organizing neural network for the face recognition. A client system creates the ID file by extracting the face feature, a server system analyzes the ID file sent from client system, and performs a student identification by using the Recognized weight file saved in Database. As a result, The proposed CS EAARS shows the 92% efficiency in the CS environment that includes the various face image database of the real classroom.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.4
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• pp.170-179
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• 1993

In this study, self-organized neural network is used to solve the vorrespondence problem of the axial stereo image. Edge points are extracted from a pair of stereo images and then the edge points of rear image are assined to the output nodes of neural network. In the matching process, the two input nodes of neural networks are supplied with the coordi- nates of the edge point selected randomly from the front image. This input data activate optimal output node and its neighbor nodes whose coordinates are thought to be correspondence point for the present input data, and then their weights are allowed to updated. After several iterations of updating, the weights whose coordinates represent rear edge point are converged to the coordinates of the correspondence points in the front image. Because of the feature map properties of self-organized neural network, noise-free and smoothed depth data can be achieved.