• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.spc
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• pp.108-114
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• 2008

Near infrared spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. The applicability of near infrared reflectance spectroscopic method was tested to determine the color value (L, a, b) of green tea. A total of 162 green tea calibration samples and 82 validation samples were used for NIRS equation development and validation, respectively. In the developed NIRS equation for analysis of the color value (L, a, b), the most accurate equation for L value was obtained at 2, 8, 6, 1 (2nd derivative, 8 nm gap, 6 points smoothing, and 1pointsecond smoothing), and for a, and b value were obtained at 1, 4, 4, 1 (1st derivative, 4 nm gap, 4points smoothing, and 1 point second smoothing) math treatment condition with SNVD (Standard Normal Variate and Detrend) scatter correction method and entire spectrum ($400{\sim}2,500\;nm$) by using MPLS (Modified Partial Least Squares) regression. Validation results of these NIRS equations showed very low bias (L: 0.005%, a: 0.003%, b: -0.013%) and standard error of prediction (SEP, L: 0.361%, a: 0.141%, b: 0.306%) as well as high coefficient of determination ($R^2$, L: 0.905, a: 0.986, b: 0.931). Therefore, these NIRS equations can be applicable and reliable for determination of color value (L, a, b) of green tea, and NIRS method could be used as a mass screening technique for breeding programs and quality control in the green tea industry.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• Sleep Medicine and Psychophysiology
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• v.6 no.1
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• pp.52-60
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• 1999

Objectives: With the object of finding the appropriate conditions and algorithms for dimensional analysis of human EEG, we calculated correlation dimensions in the various condition of sampling rate and data aquisition time and improved the computation algorithm by taking advantage of bit operation instead of log operation. Methods: EEG signals from 13 scalp lead of a man were digitized with A-D converter under the condition of 12 bit resolution and 1000 Hertz of sampling rate during 32 seconds. From the original data, we made 15 time series data which have different sampling rate of 62.5, 125, 250, 500, 1000 hertz and data acqusition time of 10, 20, 30 second, respectively. New algorithm to shorten the calculation time using bit operation and the Least Trimmed Squares(LTS) estimator to get the optimal slope was applied to these data. Results: The values of the correlation dimension showed the increasing pattern as the data acquisition time becomes longer. The data with sampling rate of 62.5 Hz showed the highest value of correlation dimension regardless of sampling time but the correlation dimension at other sampling rates revealed similar values. The computation with bit operation instead of log operation had a statistically significant effect of shortening of calculation time and LTS method estimated more stably the slope of correlation dimension than the Least Squares estimator. Conclusion: The bit operation and LTS methods were successfully utilized to time-saving and efficient calculation of correlation dimension. In addition, time series of 20-sec length with sampling rate of 125 Hz was adequate to estimate the dimensional complexity of human EEG.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.968-974
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• 2010

We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.

• Journal of Dental Rehabilitation and Applied Science
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• v.35 no.1
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• pp.1-10
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• 2019

Purpose: The aim of this study was to evaluate the influence of polishing methods on the color stability of composite resins. Materials and Methods: Two bulk-fill and four conventional resin composites were filled in cylindrical molds (6 mm diameter, 4 mm height) and light-cured. The specimens were stored in $34^{\circ}C$ distilled water for 24 h. Spectrophotometer was used to determine the color value according to the CIE $L^*a^*b^*$ color space. Each group was divided into three groups according to polishing methods (n = 5). Group 1 was control group (Mylar strip group), group 2 was polished with PoGo, and group 3 was polished with Sof-Lex Spiral wheels. Color evaluation was performed weekly for 4 weeks after immersion in $34^{\circ}C$ distilled water. The results were analyzed by generalized least squares method (P < 0.05). Results: Generalized least squares analysis revealed that Sof-Lex Spiral wheels group showed the significantly lower ${\Delta}E$ values compared to PoGo and control group (P < 0.05). The ${\Delta}E$ values of polished group showed the significantly lower than the ${\Delta}E$ values of unpolished group (P < 0.05). Regarding color changes of composite resins, there was no significant difference between the ${\Delta}E$ values of Filtek Z250 and Filtek Z350 XT Universal restorative in all time intervals (P < 0.05). Tetric N-Ceram Bulk Fill showed the significantly lower ${\Delta}E$ values compared to other composite resins in 1, 2, 3 weeks (P < 0.05). Conclusion: Within the limitations of this study, polishing methods influence the color stabilities of composite resins. The group polished with Sof-Lex Spiral Wheels showed more resistance to discoloration than group polished with PoGo.

• Journal of the Korean Chemical Society
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• v.20 no.1
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• pp.19-34
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• 1976

The crystal structure of sodium sulfisoxazole hexahydrate, $C_{11}H_{12}N_3O_3SNa{\cdot}6H_2O$,has been determined by X-ray diffraction method. The compound crystallizes in the monoclinic space group $$P2_1}c$$ with a = 15.68(3), b = 7.70(2), c = 17.94(4)${\AA}$, ${\beta}$ = $118(2)^{\circ}$ and Z = 4. A total of 1717 observed reflections were collected by the Weissenberg method with $CuK{\alpha}$ radiation. Structure was solved by heavy atom method and refined by block-diagonal least-squares methods to the R value of 0.14. The conformational angle formed by the S-C(l) bond with that of N(2)-C(7), when the projection in taken along the S-N(2), is $73^{\circ}.$ The benzene ring is planar and makes an angle of $60^{\circ}$ with the plane of the isoxazole ring, which is also planar. The sodium atom has a distorted octahedral coordination of N(l) and five oxygen atoms from hydrate molecules. Sodium sulfisoxazole hexahydrate shows fourteen different hydrogen bondings in the crystal. These are six $O-H{\cdots}O-H bonds, three $O-H{\cdots}O$ bonds, two $O-N{\cdots}N,$ one $N-H{\cdots}O,O-H{\cdots}N,N-H{\cdots}O-H$ bond, with the distances in the range of 2.71 to $3.04{\AA}.$.

• Geophysics and Geophysical Exploration
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• v.17 no.1
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• pp.11-20
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• 2014

To reveal shear-wave velocities ($v_s$) and site characterization of Wonju, Korea, Rayleigh waves were recorded at 78 sites of lower altitude using 12 to 24 4.5-Hz vertical geophones for 20 days during the period of February to September 2013. Dispersion curves of the Rayleigh waves obtained by the extended spatial autocorrelation method were inverted using the damped least-squares method to derive $v_s$ models. From these 1-D models, the average $v_s$ to a depth of 30 m ($v_s30$), $v_s$ of weathered rocks, depths to these basement rocks, and average $v_s$ of the overburden layer were derived to be $16.3{\pm}0.7m$, $576{\pm}8m/s$, $290{\pm}7m/s$, and $418{\pm}13m/s$, respectively, in the 95% confidence range. To determine adequate proxies for $v_s30$, we computed correlation coefficients of $v_s30$ with topographic slope (r = 0.46) and elevation (r = 0.43). An empirical linear relationship is presented as a combination of individually estimated $v_s30$ with weighting factors of 0.45, 0.45, and 0.1 for topographic slope, elevation, and mapped lithology, respectively. Due to a weak correlation between $v_s30$ obtained from inversion of dispersion curves and the proxy-based estimation (r = 0.50), however, the relatively large error range should be considered for applications of this relationship.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.335-345
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• 2020

This paper deals with the problem of estimating the log-transformed linear regression model to fit actual battle data from the Ardennes Campaign of World War II into the Lanchester model. The problem of determining a global solution for parameters and multicollinearity problems are identified and modified by examining the results of previous studies on data. The least squares method requires attention because a local solution can be found rather than a global solution if considering a specific constraint or a limited candidate group. The method of exploring this multicollinearity problem can be confirmed by a statistic known as a variance inflation factor. Therefore, the Lanchester model is simplified to avoid these problems, and the combat power attrition rate model was proposed which is statistically significant and easy to explain. When fitting the model, the dependence problem between the data has occurred due to autocorrelation. Matters that might be underestimated or overestimated were resolved by the Cochrane-Orcutt method as well as guaranteeing independence and normality.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.2
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• pp.143-151
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• 2003

A series of fishing experiments was carried out in the waters off the east coast of Korea from January, 2002 to March, 2003, using gill nets of different mesh sizes (m = 180, 210, 240, 270, and 300 ㎜) to determine the size selectivity of gill net for male snow crab Chionoecetes opilio. The maximum carapace length (RL) of each male snow crab caught in the fishing experiment was measured. The master curve of mesh selectivity was estimated by applying the extended Kitahara's method. Two kinds of functional models, quadratic function and cubic function were used to express logarithmic selectivity curve and were fitted to the data using the method of least squares. The obtained results were summarized as follows; 1. The cubic function of asymmetry was chosen to determine the selectivity curve of gill net for male snow crab from the model deviance comparison. 2. The result of size selectivity showed that the catch number of small male crab was getting decreased according to the increase of mesh size. 3. The optimum value (RL/m) was 0.549 and the RL/m was estimated to be 0.281, 0.296, and 0.356 when the retention probability were 0.2, 0.25 and 0.5, respectively.

• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.4
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• pp.350-357
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• 2017

Near infrared spectroscopy (NIRS) is a rapid and accurate method for analyzing the quality of cereals, and dried animal forage. However, one limitation of this method is its inability to measure fermentation parameters in dried and ground samples because they are volatile, and therefore, respectively lost during the drying process. In order to overcome this limitation, in this study, fresh coarse haylage was used to test the potential of NIRS to accurately determine chemical composition and fermentation parameters. Fresh coarse Italian ryegrass haylage samples were scanned at 1 nm intervals over a wavelength range of 680 to 2500 nm, and optical data were recorded as log 1/reflectance. Spectral data, together with first- and second-order derivatives, were analyzed using partial least squares (PLS) multivariate regressions; scatter correction procedures (standard normal variate and detrend) were used in order to reduce the effect of extraneous noise. Optimum calibrations were selected based on their low standard error of cross validation (SECV) values. Further, ratio of performance deviation, obtained by dividing the standard deviation of reference values by SECV values, was used to evaluate the reliability of predictive models. Our results showed that the NIRS method can predict chemical constituents accurately (correlation coefficient of cross validation, $R_{cv}^2$, ranged from 0.76 to 0.97); the exception to this result was crude ash ($R_{cv}^2=0.49$ and RPD = 2.09). Comparison of mathematical treatments for raw spectra showed that second-order derivatives yielded better predictions than first-order derivatives. The best mathematical treatment for DM, ADF, and NDF, respectively was 2, 16, 16, whereas the best mathematical treatment for CP and crude ash, respectively was 2, 8, 8. The calibration models for fermentation parameters had low predictive accuracy for acetic, propionic, and butyric acids (RPD < 2.5). However, pH, and lactic and total acids were predicted with considerable accuracy ($R_{cv}^2$ 0.73 to 0.78; RPD values exceeded 2.5), and the best mathematical treatment for them was 1, 8, 8. Our findings show that, when fresh haylage is used, NIRS-based calibrations are reliable for the prediction of haylage characteristics, and therefore useful for the assessment of the forage quality.