• Title, Summary, Keyword: Maximum likelyhood method

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Characteristics of Fatigue Life Distribution for Carbon/Epoxy Composite Laminates (탄소섬유/에폭시 복합적층판의 피로수명 분포특성)

  • 김영기;박병준;김재훈;이영신;전제춘
    • Proceedings of the Korean Society For Composite Materials Conference
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    • pp.119-123
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    • 2000
  • The characteristics of fatigue life distribution for Carbon/epoxy composite laminates was investigated under tension-tension loading(R=0.1). The statistical nature of the fatigue life of the composite materials was analyzed by Weibull, normal, lognormal distributions As a result, it was observed that the correlation between the experimental results and the theoretical predictions for the fatigue life is good. The distribution of the static ultimate strength has the characteristic of lognormal distribution and distribution of the fatigue life has characteristics of the weibull distribution.

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Land cover classification using LiDAR intensity data and neural network

  • Minh, Nguyen Quang;Hien, La Phu
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.29 no.4
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    • pp.429-438
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    • 2011
  • LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

New Hierarchical Modulation Scheme Using a Constellation Rotation Method (성상회전 변조기법을 이용한 새로운 계층변조 기법)

  • Kim, Hojun;Shang, Yulong;Park, Jaehyung;Jung, Taejin
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.1
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    • pp.66-76
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    • 2016
  • In this paper, we propose a new hierarchical modulation scheme for DVB-NGH to improve the performance of LP (Low-Parity) signals by applying a conventional constellation-rotation method to the LP signals without virtually a loss of performance of a HP (High-Parity) signals. The improvement of the LP signals is mainly due to the increased divesity gain caused by the constellation-rotation method which barely affect the performance of the HP signals. For the new scheme, we also propose a hardware-efficient ML (Maximum-Likelihood) detection algorithm that first decodes the HP signals by using a conventional HP receiver, and then simply decodes the precoded LP signals based on the pre-detected HP signals.

A Study on the Effect of Image Resampling in Land Cover Classification (토지피복분류에 있어서 이미지재배열의 영향에 관한 연구)

  • Yang, In-Tae;Kim, Yeon-Jun
    • Journal of Korean Society for Geospatial Information Science
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    • v.1 no.1
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    • pp.181-192
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    • 1993
  • Image is composed of the digital numbers including information on natural phenomena, their condition and the kind of objects. Digital numbers change in geometric correction(that is preprocessing). This change of digital numbers gave an effect on results of land-cover classification. We intend to know the influence of resampling as classifying land-cover using the image reconstructed by geometric correction in this paper. Chun-cheon basin was selected the study area having most variable land-cover pattern in North-Han river valley and made on use of RESTEC data resampled in preprocessing. Land-cover is classified as six classes of LEVEL I using maximum likelyhood classification method. We classified land-cover using the image resampled by two methods in this study. Bilinear interpolation method was most accurate in five classes except bear-land in the result of comparing each class with topographic map. We should choose the method of resampling according to the class in which we put the importance in the image resampling of geometric correction. And if we use four-season's image, we may classify more accurately in case of the confusion in case of the confusion in borders of rice field and farm.

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