• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.48.3-48.3
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• 2019

In the early stages of star formation, a protostar is deeply embedded in an optically thick envelope such that it is not directly observable. Variations in the protostellar accretion rate, however, will cause luminosity changes that are reprocessed by the surrounding envelope and are observable at submillimeter wavelengths. We searched for submillimeter flux variability toward 12 Planck Galactic Cold Clumps detected by the James Clerk Maxwell Telescope (JCMT)-SCUBA-2 Continuum Observations of Pre-protostellar Evolution (SCOPE) survey. These observations were conducted at 850 ㎛ using the JCMT/SCUBA-2. Each field was observed three times over about 14 months between 2016 April and 2017 June. We applied a relative flux calibration and achieved a calibration uncertainty of ~3.6% on average. We identified 136 clumps across 12 fields and detected four sources with flux variations of ~30%. For three of these sources, the variations appear to be primarily due to large-scale contamination, leaving one plausible candidate.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.628-635
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• 2008

A series of cone penetration tests in large calibration chamber were performed to investigate the effect of cementation level, relative density and vertical confining stress on cone resistance. From the experimental results, it was observed that the cone resistance is increased with increasing gypsum content, relative density, and confining stress. The increasing ratio on cone resistance of cemented sand compared with that of uncemented sand, that is IR($q_c$), was increased with increasing gypsum content and relative density, whereas it was decreased as the vertical confining stress increases. It was also observed that the cementation of granular soil influences the behavior of ground at low level of confining stress and its effect is diminished with depth.

• Computers and Concrete
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• v.21 no.1
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.331-340
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• 2009

This study evaluates the relationship between cone tip resistance ($q_c$) and small-strain shear modulus ($G_{max}$) of cemented sand. For this purpose, a series of miniature cone penetration and bender element tests are performed in calibration chamber specimens with various gypsum contents. Experimental results show that both $q_c$ and $G_{max}$ of sand increase with increasing cementation level as well as relative density and vertical confining stress. However, the relative density and vertical confining stress has more significant influence on $G_{max}$ and $q_c$ of uncemented sand than those of cemented sand. It is observed that the $G_{max}/q_c$ ratio of cemented sand decreases with increasing relative density. This result means that state variables have more affect on $q_c$ than $G_{max}$ of cemented sand. Test results also show that the effect of vertical stress on $G_{max}-q_c$ relation is reduced by cementation effect.

• International Journal of Highway Engineering
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• v.8 no.4 s.30
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• pp.87-99
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• 2006

This study was conducted to investigate whether the moisture variation aspects in pavement materials can be analyzed based on the convenient and reliable relative humidity(RH) measurement techniques. First, the ambient RH was measured using various sensors and the accuracies and calibration methods of the sensors were examined. Then, the RH of a cement mortar specimen was measured using the reliable sensors and the data was analyzed. In addition, the feasibility of using the RH measurement sensors to analyze the permeability of pavement materials was investigated. From this study, it was found that the Hygrochron was the most appropriate sensor to measure the RH of pavement materials, and the proper installation and calibration methods were developed. The RH of the cement mortar specimen tended to approach the ambient RH and was not much affected by the variation of the ambient RH. The specimen's RH variations at the surface and at the center showed a clear time lag. The RH measurement sensor was also found to be an appropriate tool for water permeability tests, and the methodologies to evaluate the permeability of pavement materials were proposed.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1923-1932
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• 2013

In order to solve the problem of severely distorted images from a wide-angle camera, we propose a calibration method which corrects a radial distortion in wide-angle images by estimation and validation of camera model. First, we estimate a camera model consisting of intrinsic and extrinsic parameters from calibration patterns, where intrinsic parameters are the focal length, the principal point and so on, and extrinsic parameters are the relative position and orientation of calibration pattern from a camera. Next we validate the estimated camera model by re-extracting corner points by inversing the model to images. Finally we correct the distortion of the image using the validated camera model. We confirm that the proposed method can correct the distortion more than 80% by the calibration experiments using the lattice shaped pattern images captured from a general web camera and a wide-angle camera.

• Journal of Korean Society on Water Environment
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• v.34 no.3
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• pp.328-338
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• 2018

The objective of this study was to evaluate the applicability of a HSPF (Hydrological Simulation Program-Fortran) model for runoff estimation in the Namgang dam watershed. Spatial data, such as watershed, stream, land use, and a digital elevation map, were used as input for the HSPF model, which was calibrated and validated using observed runoff data from 2004 to 2015 for three stations (Sancheong, Shinan, Changchon) in the study watershed. Parameters for runoff calibration were selected based on the user's manual and references, and parameter calibration was done by trial and error. The $R^2$ (determination coefficient), RMSE (root-mean-square error), NSE (Nash-Sutcliffe efficiency coefficient), and RMAE (relative mean absolute error) were used to evaluate the model's performance. Calibration and validation results showed that annual mean runoff was within a ${\pm}5%$ error in Sancheong and Shinan, whereas there was a14% error in Changchon. The model performance criteria for calibration and validation showed that $R^2$ ranged from 0.80 to 0.92, RMSE was 2.33 to 2.39 mm/day, NSE was 0.71 to 0.85, and RMAE was 0.37 to 0.57 mm/day for daily runoff. Visual inspection showed that the simulated daily flow, monthly flow, and flow exceedance graph agreed well with observations for the Sancheong and Shinan stations, whereas the simulated flow was higher than observed at the Changchon station.

• Journal of Animal Environmental Science
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• v.20 no.3
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• pp.117-124
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• 2014

The objectives were to assess the use of near-infrared reflectance spectroscopy (NIRS) as a tool for estimating nutrient compositions of corn kernel, and to apply an NIRS-based indium gallium arsenide array detector to the system for collecting spectra and analyzing calibration equations using equipments designed for field application. Partial Least Squares Regression (PLSR) was employed to develop calibration equations based on representative data sets. The kennard-stone algorithm was applied to induce a calibration set and a validation set. As a result, the method for structuring a calibration set significantly affected prediction accuracy. The prediction of chemical composition of corn kernel resulted in the following (kennard-stone algorithm: relative) moisture ($R^2=0.82$, RMSEP=0.183), crude protein ($R^2=0.80$, RMSEP=0.142), crude fat ($R^2=0.84$, RMSEP=0.098), crude fiber ($R^2=0.74$, RMSEP=0.098), and crude ash ($R^2=0.81$, RMSEP=0.048). Result of this experiment showed the potential of NIRS to predict the chemical composition of corn kernel.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.526-532
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• 2019

In this study, an in-house $^{152}Eu$ calibration source was produced from a custom epoxy matrix with a density of ${\rho}=1.14g\;cm^{-3}$, which is chemically stable and durable form after its solidification. The homogeneity of $^{152}Eu$ in matrix was obtained better than 98%. For a Marinelli beaker geometry, an efficiency calibration procedure was applied to a n-type, coaxial, 78.5% relative efficient HPGe detector in the energy range of 121.7-1408.0 keV by using in-house $^{152}Eu$ calibration source. Then the measured efficiencies for Marinelli geometry were compared with the results calculated by MEFFTRAN and ANGLE softwares for the validation. Although MEFFTRAN and ANGLE have two different efficiency transfer algorithms to calculate the efficiencies, they usually need to use a reliable and accurate reference efficiency values as input data. Hence, reference efficiency values were obtained experimentally from a multinuclide standard source for the same detector-Marinelli geometry. In the present source characterization, the corrections required for self-absorption and true coincidence summing effects for $^{152}Eu$ gamma-rays were also obtained for a such close counting geometry condition. The experimental results confirmed the validity of efficiency calculations obtained by MEFFTRAN and ANGLE softwares that are calculation tools.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.