• Proceedings of the Korean Ceranic Society Conference
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• 2003.04a
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• pp.18.1-18
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• 2003

• Journal of the KSME
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• v.47 no.8
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• pp.24-25
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• 2007

• Proceedings of the KSEEG Conference
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• 2002.04a
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• pp.150-152
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• 2002

• Membrane Journal
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• v.9 no.2
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• pp.63-68
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• 1999

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.26 no.1
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• pp.8-17
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• 1997

• Membrane Journal
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• v.4 no.4
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• pp.171-186
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• 1994

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.1
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• pp.73-80
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• 2016

• Korean Architects
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• no.11 s.140
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• pp.80-90
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• 1980

• 한국지구물리탐사학회:학술대회논문집
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• 1999.08a
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• pp.92-116
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• 1999

Tomographic approaches to image underground structure using electrical properties, can be divided into DC resistivity, electromagnetic, and radar tomography, based on the operating frequency. DC resistivity and radar tomography methods have been recently applied to site investigation for engineering purpose in Korea. This paper review these two tomography methods, through the case histories acquired in Korea. As another method of borehole radar survey, borehole radar reflection method is included, and its inherent problem and solution are discussed, how to find the azimuth angle of reflector using direction-finding-antenna. Since the velocity anisotropy of radar wave has been commonly encountered in field data, anisotropic radar tomography is discussed in this paper. In DC resistivity tomography, two subjects are focussed, electrode arrays, and borehole effect owing to the conductive fluid in borehole. Using the numerical modeling data, various kinds of electrode ways are compared, and borehole effect is illustrated. Most of the case histories presented in this paper are compared with known geology, core logging data, and/or Televiewer images.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.3
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• pp.101-109
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• 2021

Wave observations using a marine X-band radar are conducted by analyzing the backscattered radar signal from sea surfaces. Wave parameters are extracted using Modulation Transfer Function obtained from 3D wave number and frequency spectra which are calculated by 3D FFT of time series of sea surface images (42 images per minute). The accuracy of estimation of the significant wave height is, therefore, critically dependent on the quality of radar images. Wave observations during Typhoon Maysak and Haishen in the summer of 2020 show large errors in the estimation of the significant wave heights. It is because of the deteriorated radar images due to raindrops falling on the sea surface. This paper presents the algorithm developed to increase the accuracy of wave heights estimation from radar images by adopting convolution neural network(CNN) which automatically classify radar images into rain and non-rain cases. Then, an algorithm for deriving the Hs is proposed by creating different ANN models and selectively applying them according to the rain or non-rain cases. The developed algorithm applied to heavy rain cases during typhoons and showed critically improved results.