• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.183-184
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• 1998

본 논문에서는 목적어-동사 관계의 분포에 따라 한국어 동사를 자동적으로 클러스터링하는 방법을 제시한다. SOM(Self-Organizing Map)이 입력 패턴을 분석하고 가시화하는데 뛰어난 성능을 보이므로, 본 논문에서는 클러스터링하는 방법으로 SOM을 채택하였다. 일단 맵(map)이 만들어지고 나면 학습하는 동안 경험하지 못한 동사도 쉽게 적당한 클러스터로 분류될 수 있고 클러스터들 간의 의미 거리도 맵을 이용하여 쉽게 계산할 수 있다. 본 논문에서 제안한 방법을 명사 확률 분포의 상대 엔트로피(relative entropy)에 기반한 클러스터링 방법과 비교해 본 결과, SOM에 의해 만들어진 동사 클러스터가 상대 엔트로피를 이용해서 만들어진 클러스터를 잘 반영한다는 것을 알 수 있었다.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.38-43
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• 1991

In order to cluster the Input pattern neatly, some neural network modified from Kohonen's self organizing feature map is introduced and Korean phoneme recognition experiments are performed using the modified self organizing feature map(MSOFM) and the auditory model.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1052-1057
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• 1994

Dynamically localized self-organizing map model (DLSMM) is a new speech recognition model based on the well-known self-organizing map algorithm and dynamic programming technique. The DLSMM can efficiently normalize the temporal and spatial characteristics of speech signal at the same time. Especially, the proposed can use contextual information of speech. As experimental results on ten Korean digits recognition task, the DLSMM with contextual information has shown higher recognition rate than predictive neural network models.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.817-822
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• 2004

This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.2
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• pp.65-70
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• 2003

In this study, researchers developed the estimative algorithm for artificial defect in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages : Crack, Delamination and Normal. According to the results, we were confirmed that estimative algerian was provided the recognition rates of 75.7% (for Crack) and 83.4% (for Delamination) and 87.2 % (for Normal).

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.162-170
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• 2002

Detecting straight lines in an image is frequently required for various machine vision applications such as restoring CAD drawings from scanned images and object recognition. The standard Hough transform has been dominantly used to that purpose. However, massive storage requirement and low precision in estimating line parameters due to the quantization of parameter space are the major drawbacks of the Hough transform technique. In this paper, to overcome the drawbacks, an iterative algorithm based on a self-organizing map is presented. The self-organizing map can be adaptively learned such that image points are clustered by prominent lines. Through the procedure of the algorithm, a set of lines are sequentially detected one at a time. The algorithm can produce highly precised estimates of line parameters using very small amount of storage memory. Computational results for synthetically generated images are given. The promise of the algorithm is also demonstrated with its application to two natural images of inserts.

• 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 Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.402-405
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• 2000

In this paper we propose a method ef pattern classification of the hand movement using EMG signals through Self-organizing feature map. Self-organizing feature map is an artificial neural network which organizes its output neuron through leaning and therefore it can classify input patterns. The raw EMC signals become direct input to the Self-organizing feature map. The simulation and experiment results showed the effectiveness of the classification of EMG signal using the Self-organizing feature map.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.4
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• pp.31-36
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• 2001

In this paper we propose a method of pattern classification of the hand movement using EMG signals through Self-organizing feature map. Self-organizing feature map is an artificial neural network which organizes its output neuron through learning and therefore it can classify input patterns. The raw EMG signals become direct input to the Self-organizing feature map. The simulation and experiment results showed the effectiveness of the classification of EMG signal using the Self-organizing feature map.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.77-84
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• 2018

Report documents of industrial and occupational accidents have continuously been accumulated in private and public institutes. Amongst others, information on narrative-texts of accidents such as accident processes and risk factors contained in disaster report documents is gaining the useful value for accident analysis. Despite this increasingly potential value of analysis of text information, scientific and algorithmic text analytics for safety management has not been carried out yet. Thus, this study aims to develop data processing and visualization techniques that provide a systematic and structural view of text information contained in a disaster report document so that safety managers can effectively analyze accident risk factors. To this end, the risk factor map using text mining and self-organizing map is developed. Text mining is firstly used to extract risk keywords from disaster report documents and then, the Self-Organizing Map (SOM) algorithm is conducted to visualize the risk factor map based on the similarity of disaster report documents. As a result, it is expected that fruitful text information buried in a myriad of disaster report documents is analyzed, providing risk factors to safety managers.