• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.30 no.1
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• pp.7-17
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• 1998

With the advent of CLSM in the end of 1980s, it has been applied to the field of pulp and paper science in various ways. This study showed the potentials of CLSM In analyzing a change of pulp fiber and paper properties before and after mechanical treatment. In particular, a quantification of internal fibrillation has been done using cross-sectional images of fibers and image analysis technique, then evaluated the effects of fiber wall delamination on fiber and paper properties. It showed that the delaminated fibers were closely associated to development of the interfiber bonding in a fiber network. The CLSM made it possible to investigate a density profile along the sheet thickness, which was created by some papermaking processes like pressing, drying and calendering. Through the attempt to observe the forming procedure of a fiber network during handsheet making, the CLSM images showed that the pressing stage was considered greatly to contribute to generation of interfiber bonding with removing a free water and partly a bound water between fibers. In addition, the CLSM could be used to illustrate not only a surface profile of paper showing the extent of smoothness or roughness, but also a density profile in a B-direction of the network. Finally it became evident that the CLSM could be used as an excellent tool to predict development in fiber and paper properties before and after mechanical treatment during papermaking processes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.549-557
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• 2013

An flood inundation map is able to convey spatial distribution of inundation to a decision maker for flood risk management. A roughness coefficient with unclear values and a discharge obtained from the stage-discharge rating equation are key sources of uncertainty in flood inundation mapping by using a hydraulic model. Also, the uncertainty analysis needs an observation for the flood inundation, and satellite images is useful to obtain spatial distribution of flood. Accordingly, the objective of this study is to quantify uncertainty arising roughness and discharge in flood inundation mapping by using a hydraulic model and a satellite image. To perform this, flood inundations were simulated by HEC-RAS and terrain analysis, and ISODATA (Iterative Self-Organizing Data Analysis) was used to classify waterbody from Landsat 5TM imagery. The classified waterbody was used as an observation to calculate F-statistic (likelihood measure) in GLUE (Generalized Likelihood Uncertainty Estimation). The results from GLUE show that flood inundation areas are 74.59 $km^2$ for lower 5 % uncertainty bound and 151.95 $km^2$ for upper 95% uncertainty bound, respectively. The quantification of uncertainty in flood inundation mapping will play a significant role in realizing the efficient flood risk management.

• Applied Microscopy
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• v.41 no.4
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• pp.223-228
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• 2011

White light scanning interferometry has been employed to analyze surface features of diverse specimens. Long established in the field of materials engineering, the technique provides quantitative three-dimensional data as well as qualitative morphological images. It uses white light that is split and reflected from a reference mirror and an object. Merged together, the light generates interference patterns representing topographical contours of the object surface. The amplitude of the z-axis data is differentiated by gray scale. The technique allows the rapid, noncontact, and wide-field measurements for morphometry of biological specimens including chondrocytes, tooth enamel, and plant leaves. Quantification of the dimension of surface structures such as width, length, and elevation angle could be achievable by white light scanning interferometry. The light reflection from plant leaves has been assumed to be sufficient for the technique. Without special specimen preparations like conductive metal coating, the technique can be increasingly used for quantitative three-dimensional surface measurements of biological specimens.

• Korean Journal of Soil Science and Fertilizer
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• v.12 no.1
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• pp.15-23
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• 1979

In the first paper of the series the five soil fertility factors were evaluated by means of principal component analysis and varimax method. They are interpreted as representing, 1) skeletal available phosporus status, 2) organnic matter status, 3) salt status 4) base status, and 5) free oxide status. In order to resynthesize such fragmented information for the overall soil fertility evaluation, the method of multiple regression analysis was adopted, using the five factor scores and yield data for Korean paddy soils as independent and dependent variables respectively. As test of linear models with different combinations of independent variables the results of t-test of regression coefficient were revealed that the organic matter status (FII) has no relevance to the yield of paddy and that the free oxides and salt supply has by it self only an insignificant contribution to the yield. The multiple correlation coefficient (R) revealed its multiple regression analysis was as low as 0.43. Introduction of quadratic terms to the linear model bettered the result. Thus multiple correlation coefficient (R) was increased as 0.59. Therefore, a coefficient of determination 0.35 was obtained by a quadratic model with interaction terms among the five fertility constituents. Generally we think that the fertility factor has more contribution to raise the rice yield in paddy and that the failure of yield prediction by fertility factor scores was caused by one of follows; 1) the roughness of the yield inspection, and 2) missextraction of fertility constituents. The second step in this study, assuming that the residuals by multiple regression analysis were due to factors other than soil fertility, we can now proceed to predicting the yield from the field characters with the classified fertility groups by means of Hayashi's theory of quantification No. 1. Such variables as fertility groups (FTYG), water availability (WATER), soil drainage (DRNG), climatic zone (CLIZ), surface soil's stickiness (STCKT), surface soil's dry consistence (DCNST), and surface soil's texture (FTEXT) are taken up as the explanatory variables. The quantification appears reasonable; the well to extremely well in soil drainage, very sticky of surface soil, inefficiency in water availability, coarse texture, and very hard to extremely hard dry consistence in soil are detrimental to the rice yield. The R was as high as 0.90 for the set of variables. But the given explanatory variables in this study were not quite effective in explaining rice yield. The method developed seems to be promising only if properly collected data are available. Conditions that should be satisfied in the yield inspection obtained from common cultivator for the purpose of deriving a prediction equation were put forward.