• The Journal of Information Systems
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• v.19 no.1
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• pp.19-33
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• 2010

생산 수율은 비선형관계를 지닌 여러 요인들에 의해 영향을 받기 때문에 반도체 생산의 경우 예측이 어렵다. 본 논문에서 저자들은 사례기반추론과 자기조직화신경망 기반의 데이터마이닝 기법을 활용하여 수율의 높고 낮음을 밝히는 지능화된 수율예측시스템을 제시한다. 이 시스템은 자기조직회신경망을 사용하여 생산 로트의 공정파라미터 패턴을 파악하고 속성가중치 기반의 사례기반추론을 통해 신규 로트의 수율 수준을 예측한다. 이때 속성가중치는 역전파인공신경망을 통해 계산된다. 웹기반 시스템이 개발되고, 반도체 생산 기업의 실제 자료를 적용하여 본 시스템의 효율을 검증하고 평가한다.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.649-658
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• 2005

Since the Network based attack Is extensive in the real state of damage, It is very important to detect intrusion quickly at the beginning. But the intrusion detection using supervised learning needs either the preprocessing enormous data or the manager's analysis. Also it has two difficulties to detect abnormal traffic that the manager's analysis might be incorrect and would miss the real time detection. In this paper, we propose a traffic attributes correlation analysis mechanism based on self-organizing maps(SOM) for the real-time intrusion detection. The proposed mechanism has three steps. First, with unsupervised learning build a map cluster composed of similar traffic. Second, label each map cluster to divide the map into normal traffic and abnormal traffic. In this step there is a rule which is created through the correlation analysis with SOM. At last, the mechanism would the process real-time detecting and updating gradually. During a lot of experiments the proposed mechanism has good performance in real-time intrusion to combine of unsupervised learning and supervised learning than that of supervised learning.

• Journal of Industrial Technology
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• v.24 no.A
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• pp.99-107
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• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and the environments as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to parition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum of the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.680-685
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• 2000

최근 그 심각성이 커지고 있는 해킹피해를 줄이기 위한 한 방법으로 시스템에 침입한 불법적 사용을 탐지하는 연구가 활발히 진행되고 있다. 침입을 탐지하는 방법으로는 오용탐지와 비정상행위 탐지가 있는데 비정상행위 탐지를 위해서는 정보수집의 정확성, 신속성과 함께 다량의 정보들로부터 필요한 정보를 추출하고 축약하는 것이 중요하다. 본 논문에서는 감사기록 도구인 BSM으로부터 정보를 추출하고 자기조직화 신경망을 이용하여 다차원의 정보를 저차원정보로 축약.변환하는 방법에 대한 실험적인 검증을 시도하였다. 또한 BSM에서 얻을 수 있는 데이터의 유용성을 조사하기 위하여 축약된 감사자료에 의한 탐지성능을 살펴보았다. 실험결과, 시스템 호출 및 파일관련 정보의 축약이 탐지성능향상에 크게 기여하는 중요한 척도임을 알 수 있었으며 각 척도마다 탐지성능이 좋은 맵의 크기가 다름을 알 수 있었다. 이러한 축약된 정보는 여러 정상행위 모델링방법에 의해 유용하게 사용될 수 있을 것이다.

• The Journal of Korean Association of Computer Education
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• v.9 no.2
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• pp.101-109
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• 2006

We propose an adaptive digital watermarking algorithm using HVS(human visual system) and SOM(Self-Organizing Map) among neural networks. This method adjusts adaptively the strength of the watermark which is embedded in different blocks according to block classification in DCT(Discrete Cosine Transform) domain. All blocks in 3 classes out of 4 are selected to embed a watermark. Watermark sequences are embedded in 6 lowest frequency coefficients of each block except the DC component. The experimental results are excellent.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.572-587
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• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.5
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• pp.512-520
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• 2019

It is quite useful to represent speeches visually for learners who study foreign languages as well as the hearing impaired who cannot directly hear speeches, and a number of researches have been presented in the literature. They remain, however, at the level of representing the characteristics of speeches using colors or showing the changing shape of lips and mouth using the animation-based representation. As a result of such approaches, those methods cannot tell the users how far their pronunciations are away from the standard ones, and moreover they make it technically difficult to develop such a system in which users can correct their pronunciation in an interactive manner. In order to address these kind of drawbacks, this paper proposes a speech visualization model based on the relative distance between the user's speech and the standard one, furthermore suggests actual implementation directions by applying the proposed model to the visualization of Korean vowels. The method extract three formants F1, F2, and F3 from speech signals and feed them into the Kohonen's SOM to map the results into 2-D screen and represent each speech as a pint on the screen. We have presented a real system implemented using the open source formant analysis software on the speech of a Korean instructor and several foreign students studying Korean language, in which the user interface was built using the Javascript for the screen display.