• Journal of the Korean Geographical Society
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• v.35 no.2
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• pp.373-383
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• 2000

최근 인터넷의 급속한 발달로 인하여 GIS는 특정 전문가들의 도구에서 일반인들이 일상생활에서 활용할 수 있는 도구로 변모해 가고 있다. 21세기 인터넷의 사용은 우리 생활의 일부가 될 것이며, 인터넷 GIS의 도입은 우리의 삶을 보다 풍요롭게 하리라 예견된다. 본 글에서는 GIS의 발달과정을 살펴보고, 21세기를 맞이하여 인터넷 GIS의 도입으로 아니하여 행정분야, 교통분야, 문화분야, 주거분야등에 미칠 영향을 예견해 보고, 이미 인터넷 GIS서비스를 제공하고 있는 사이트를 살펴보았다.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.615-624
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• 2006

The objective of this paper is to implement parallel multi-layer ANN(Artificial Neural Network) simulator based on the mobile agent system which is executed in parallel in the virtual parallel distributed computing environment. The Multi-Layer Neural Network is classified by training session, training data layer, node, md weight in the parallelization-level. In this study, We have developed and evaluated the simulator with which it is feasible to parallel the ANN in the training session and training data parallelization because these have relatively few network traffic. In this results, we have verified that the performance of parallelization is high about 3.3 times in the training session and training data. The great significance of this paper is that the performance of ANN's execution on virtual parallel computer is similar to that of ANN's execution on existing super-computer. Therefore, we think that the virtual parallel computer can be considerably helpful in developing the neural network because it decreases the training time which needs extra-time.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.338-342
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• 2007

In this paper we propose a user adaptive post-processing method to improve the accuracy of speaker dependent, isolated word speech recognition, particularly for mobile devices. Our method considers the recognition result of the basic recognizer simply as a high-level speech feature and processes it further for correct recognition result. Our method learns correlation between the output of the basic recognizer and the correct final results and uses it to correct the erroneous output of the basic recognizer. A multi-layer perceptron model is built for each incorrectly recognized word with high frequency. As the result of experiments, we achieved a significant improvement of 41% in recognition accuracy (41% error correction rate).