• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1A
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• pp.1-10
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• 2012

Along with the development of coasts, islands and mountains, the demand of long-span bridges increases which, in turn, brings forth the construction of cable-supported bridges like suspension and cable-stayed bridges. There are various types of statically indeterminate structures widely applied that supported the main girder with stay cables, main cables, hanger cables with aesthetic structural appearance. As to the cable-supported bridges, the health monitoring of a bridge can be identified by measuring tension force on cable repeatedly. The tension force on cable is measured either by direct measurement of stress of cable using load cell or hydraulic jack, or by vibration method estimating tension force using cable shape and measured dynamic characteristics. In this study, a method to estimate dynamic characteristics of hanger cables by using a digital image processing is suggested. Digital images are acquired by a portable digital camcorder, which is the sensor to remotely measure dynamic responses considering convenient and economical aspects for use. A digital image correlation(DIC) technique is applied for digital image processing, and an image transform function(ITF) to correct the geometric distortion induced from the deformed images is used to estimate subpixel. And, the correction of motion of vision-based measurement system using a fixed object in an image without installing additional sensor can be enhanced the resolution of dynamic responses and modal frequencies of hanger cables.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.721-732
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• 2017

With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%),respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR(Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.