• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.223-226
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• 2000

최근 컴퓨터의 대중화와 인터넷환경의 구축으로 인하여 웹기반 코스웨어들의 필요가 급속히 늘고 있다. 그러나 수학 교과의 입체도형 편에 있어서는 웹기반 3D 코스웨어들의 개발은 부진한 편이다. 기존의 코스웨어들은 저작도구를 활용한 2D 위주였고, 최근 연구되어진 3D 코스웨어들은 상호작용이 부족하여 다양한 학습자의 욕구를 충족시키지 못하고 있다. 따라서 본 연구에서는 3D 입체도형의 학습과 학습자의 자극에 반응하는 상호작용적 체험학습이 되도록 VRML 과 JAVA를 이용하여 구현하였다.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.285-292
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• 2019

With the popularization of PC, SNS and IoT, a lot of data is generated and the amount is increasing exponentially. Artificial neural network learning is a topic that attracts attention in many fields in recent years by using huge amounts of data. Artificial neural network learning has shown tremendous potential in speech recognition and image recognition, and is widely applied to a variety of complex areas such as medical diagnosis, artificial intelligence games, and face recognition. The results of artificial neural networks are accurate enough to surpass real human beings. Despite these many advantages, privacy problems still exist in artificial neural network learning. Learning data for artificial neural network learning includes various information including personal sensitive information, so that privacy can be exposed due to malicious attackers. There is a privacy risk that occurs when an attacker interferes with learning and degrades learning or attacks a model that has completed learning. In this paper, we analyze the attack method of the recently proposed neural network model and its privacy protection method.

• Proceedings of the Korean Geotechical Society Conference
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• 1991.10a
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• pp.332-347
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• 1991

Accurate prediction of future settlement is essential for the settlement control of soft soil by pre-loading method. To predict future settlement in clayey soft soils, several methods like Asaoka method, Hyperbolic Method and Hoshino method are currently being used. These methods predict the future sett1ement by mathmatical treatment of the measured settlement data on the basis of consolidtion theory and empiricism. But the correlation coefficient between the measured and the predicted settlement was relatively low (0.8~0.9). Also, the prediction of future settlemet for the design load is very difficult. In this article, the measured field settlement data was treated as the the field consolidation test. Hence, condolidation coefficient(Cv) and compression index(Cc) was evaluated from the field settlement data. Cv and Cc values from field data was used to calculate the degree of consolidation and settlement at desired time. By this method, the correlation coefficent between the measured and the predicted settlement was significantly increased(0.97~0.99). Also the settlement by the design load after the improvement of soft soil could be predicted reasonably. This method is quite rational and sound but it requires thousands of calculation steps. Today, by the aid of low priced personal computers above mentioned technique could be used much acre economically and effectively than conventional methods. This article presented the mechanisms and capacities of this method and demonstrated the enhanced correlation coefficient when applied to actual field settlement data.