• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.3
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• pp.211-215
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• 2004

Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. This study was conducted to determine tocopherol and tocotrienol contents in rice bran by using NIRS system. Total 80 rice bran samples previously analyzed by HPLC were scanned by NIRS and over 60 samples were selected for calibration and validation equation. A calibration equation calculated by MPLS(modified partial least squares) regression technique was developed and coefficient of determination for tocopheyol and tocotyienol content were 0.975 and 0.984, respectively, in calibration sets. Each calibration equation was fitted to validation set that was performed with the remaining samples not included is the calibration set, which showed high positive correlation both in tocopherol and tocotrienol content file. This results demonstrate that the developed NIRS equation can be practically used as a rapid screening method for quantification of tocopherol and tocotrienol contents in rice bran.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.5
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• pp.401-406
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• 2001

The applicability of non-destructive near infrared reflectance spectroscopic (NIRS) method was tested to determine the protein and oil contents of intact soybean [Glycine max (L.) Merr.] seeds. A total of 198 soybean calibration samples and 101 validation samples were used for NIRS equation development and validation, respectively. In the developed non-destructive NIRS equation for analysis of protein and oil contents, the most accurate equation was obtained at 2, 8, 6, 1(2nd derivative, 8 nm gap, 6 points smoothing, and 1 point second smoothing) and 2, 1, 20, 10 math treatment conditions with Standard Normal Variate and Detrend (SNVD) scatter correction method and entire spectrum (400-2500 nm) by using Modified Partial Least Squares (MPLS) regression, respectively. Validation of these non-destructive NIRS equations showed very low bias (protein: 0.060%, oil: -0.017%) and standard error of prediction (SEP, protein: 0.568 %, oil : 0.451 %) as well as high coefficient of determination ($R^2$, protein: 0.927, oil: 0.906). Therefore, these non-destructive NIRS equations can be applicable and reliable for determination of protein and oil content of intact soybean seeds, and non-destructive NIRS method could be used as a mass screening technique for selection of high protein and oil soybean in breeding programs.

• Journal of Radiation Protection and Research
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• v.42 no.1
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• pp.48-55
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• 2017

Background: Simultaneous detection of neutrons and gamma rays have become much more practicable, by taking advantage of good gamma-ray discrimination properties using pulse shape discrimination (PSD) technique. Recently, we introduced a commercial CLYC system in Korea, and performed an initial characterization and simulation studies for the CLYC detector system to provide references for the future implementation of the dual-mode scintillator system in various studies and applications. Materials and Methods: We evaluated a CLYC detector with 95% $^6Li$ enrichment using various gamma-ray sources and a $^{252}Cf$ neutron source, with validation of our Monte Carlo simulation results via measurement experiments. Absolute full-energy peak efficiency values were calculated for gamma-ray sources and neutron source using MCNP6 and compared with measurement experiments of the calibration sources. In addition, behavioral characteristics of neutrons were validated by comparing simulations and experiments on neutron moderation with various polyethylene (PE) moderator thicknesses. Results and Discussion: Both results showed good agreements in overall characteristics of the gamma and neutron detection efficiencies, with consistent ~20% discrepancy. Furthermore, moderation of neutrons emitted from $^{252}Cf$ showed similarities between the simulation and the experiment, in terms of their relative ratios depending on the thickness of the PE moderator. Conclusion: A CLYC detector system was characterized for its energy resolution and detection efficiency, and Monte Carlo simulations on the detector system was validated experimentally. Validation of the simulation results in overall trend of the CLYC detector behavior will provide the fundamental basis and validity of follow-up Monte Carlo simulation studies for the development of our dual-particle imager using a rotational modulation collimator.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.183-194
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• 2019

Adulteration of argan oil with some other cheaper oils with similar chemical compositions has resulted in increasing demands for authenticity assurance and quality control. Fast and simple analytical techniques are thus needed for authenticity analysis of high-priced argan oil. Raman spectroscopy is a potent technique and has been extensively used for quality control and safety determination for food products In this study, Raman spectroscopy in combination with a net analyte signal (NAS)-based methodology, i.e., hybrid linear analysis method developed by Goicoechea and Olivieri in 1999 (HLA/GO), was used to predict the different concentrations of olive oil (0 - 20%) added to argan oil. Raman spectra of 90 samples were collected in a spectral range of $400-400cm^{-1}$, and calibration and validation sets were designed to evaluate the performance of the multivariate method. The results revealed a high coefficient of determination ($R^2$) value of 0.98 and a low root-mean-square error (RMSE) value of 0.41% for the calibration set, and an $R^2$ of 0.97 and RMSE of 0.36% for the validation set. Additionally, the figures of merit such as sensitivity, selectivity, limit of detection, and limit of quantification were used for further validation. The high $R^2$ and low RMSE values validate the detection ability and accuracy of the developed method and demonstrate its potential for quantitative determination of oil adulteration.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1349-1360
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• 2020

In recent years, Convolutional Neural Networks (CNNs) have been successfully implemented in different tasks of computer vision. Since CNN models are the representatives of supervised learning algorithms, they demand large amount of data in order to train the classifiers. Thus, obtaining data with correct labels is imperative to attain the state-of-the-art performance of the CNN models. However, labelling datasets is quite tedious and expensive process, therefore real-life datasets often exhibit incorrect labels. Although the issue of poorly labelled datasets has been studied before, we have noticed that the methods are very complex and hard to reproduce. Therefore, in this research work, we propose Deep CleanNet - a considerably simple system that achieves competitive results when compared to the existing methods. We use K-means clustering algorithm for selecting data with correct labels and train the new dataset using a deep CNN model. The technique achieves competitive results in both training and validation stages. We conducted experiments using MNIST database of handwritten digits with 50% corrupted labels and achieved up to 10 and 20% increase in training and validation sets accuracy scores, respectively.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.54.2-54.2
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• 2016

We present a machine learning approach to estimating stellar atmospheric parameters, effective temperature (Teff), surface gravity (log g), and metallicity ([Fe/H]) for stars observed during the course of the Sloan Digital Sky Survey (SDSS). For training a neural network, we randomly sampled the SDSS data with stellar parameters available from SEGUE Stellar Parameter Pipeline (SSPP) to cover the parameter space as wide as possible. We selected stars that are not included in the training sample as validation sample to determine the accuracy and precision of each parameter. We also divided the training and validation samples into four groups that cover signal-to-noise ratio (S/N) of 10-20, 20-30, 30-50, and over 50 to assess the effect of S/N on the parameter estimation. We find from the comparison of the network-driven parameters with the SSPP ones the range of the uncertainties of 73~123 K in Teff, 0.18~0.42 dex in log g, and 0.12~0.25 dex in [Fe/H], respectively, depending on the S/N range adopted. We conclude that these precisions are high enough to study the chemical and kinematic properties of the Galactic disk and halo stars, and we will attempt to apply this technique to Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which plans to obtain about 8 million stellar spectra, in order to estimate stellar parameters.

• Journal of Ocean Engineering and Technology
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• v.26 no.1
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• pp.9-16
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• 2012

This paper presents the numerical results of a simulation of the free surface flow around a blunt bow ship model and focuses on the validation of the proposed method with a brief investigation of the relation between the resistance and free surface behavior. A finite volume method based on the Reynolds Averaged Navier-Stokes (RANS) approach is used to solve the governing flow equations, where the free surface, including wave breaking,is captured by using a two-phase Level-Set (LS) method. For turbulence closure, a two equation k-${\varepsilon}$ model with the standard wall function technique is used. Finally, the numerical results are compared with the available experimental data, showing good agreement.

• Research in Community and Public Health Nursing
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• v.29 no.2
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• pp.155-169
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• 2018

Purpose: The purpose of this study is to review the quantitative research literature on measuring caring in order to identify overall trends in measuring caring. Methods: Fifty three papers were selected from four databases including RISS4U, DBpia, KISS, and Korea Med. Results: The number of measuring caring papers has increased since 2000. Approximately 60 % of the total papers were descriptive and correlative design researches with convenience sampling. Jean Waston's theory was the most popular conceptual framework, but much of the research tended to be conducted without any conceptual framework. In that kind of research, 'caring' terms were used without definition. The most frequently used term for the concept of caring was nurses' caring behaviors. Also, 'nurses' was one of the most popular subjects. Thirty six measuring caring instruments were used. Twenty were developed in foreign countries and translated into Korean. The others were developed originally in Korean. Interpersonal Caring Technique - Communication Skills Scale, based on the interpersonal process model, was the most frequently used tool. Among the translated instruments, Coates' Caring Efficacy Scale was the most popular. Some instruments were used without validation. Conclusion: These results provide basic data on measuring caring and indicate directions for further research. In particular, validation studies of measuring caring instruments are needed.

• Journal of The Geomorphological Association of Korea
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• v.23 no.2
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• pp.109-125
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• 2016

This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.

• Steel and Composite Structures
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• v.39 no.4
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• pp.471-491
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• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.