• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.197-208
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• 2022

The existence of shallow bedrock and the desire to use underground space necessitate the use of blasting methods. The standard blasting method under water after drilling is associated with certain technical difficulties, including reduced detonation power, the use of a fixed charge per delay, and decoupling. However, there is no blasting method to replace the existing blasting method. In this paper, a dry hole charged with ANFO blasting is assessed while employing a dry hole pumping system to remove water from the drill borehole. Additional standard blasting is also utilized to compare the blasting performances of the two methods. The least-squares linear regression method is adopted to analyze the blasting vibration velocity quantitatively using the measured vibration velocity for each blasting method and the vibration velocity model as a function of the scaled distance. The results show that the dry hole charged with ANFO blasting will lead to greater damping of the blasting vibration, more energy dissipation to crush the surrounding rock, and closer distances for the allowable velocity of the blasting vibration. Also, standard blasting shows much longer influencing distances and a wider range of the blasting pattern. The pilot test confirms the blasting efficiency of dry hole charged with ANFO blasting.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.12
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• pp.1387-1394
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• 2014

In a photoelastic experiment, it is necessary to know the material stress fringe constant of the photoelastic specimen to determine the stresses from the measured isochromatic fringe orders. The material stress fringe constant can be obtained using a simple tension specimen and/or a circular disk under diametric compression. In these methods, there is generally a need to apply numerous loads to the specimen in response to the relationship of the fringe order. Then, the least squares method is used to obtain the material constant. In this paper, the fringe orders that appear on a four-point bending specimen are used to determine the fringe constant. This method requires four photoelastic fringes obtained from a circular polariscope by rotating the analyzer to 0, ${\pi}/4$, ${\pi}/2$, and $3{\pi}/4$ radians. Using the four-point bending specimen to determine the material stress fringe constant has an advantage because measurements can be made at different locations by applying a constant load. The stress fringe constant measured with this method is within the range suggested by the manufacturer of the photoelastic material.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.111-121
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• 2006

Recently, many studies have been performed to apply airborne LIDAR data to extracting urban models. In order to model efficiently the man-made objects which are the main components of these urban models, it is important to extract automatically planar patches from the set of the measured three-dimensional points. Although some research has been carried out for their automatic extraction, no method published yet is sufficiently satisfied in terms of the accuracy and completeness of the segmentation results and their computational efficiency. This study thus aimed to developing an efficient approach to automatic segmentation of planar patches from the three-dimensional points acquired by an airborne LIDAR system. The proposed method consists of establishing adjacency between three-dimensional points, grouping small number of points into seed patches, and growing the seed patches into surface patches. The core features of this method are to improve the segmentation results by employing the variable threshold value repeatedly updated through a statistical analysis during the patch growing process, and to achieve high computational efficiency using priority heaps and sequential least squares adjustment. The proposed method was applied to real LIDAR data to evaluate the performance. Using the proposed method, LIDAR data composed of huge number of three dimensional points can be converted into a set of surface patches which are more explicit and robust descriptions. This intermediate converting process can be effectively used to solve object recognition problems such as building extraction.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.117-126
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• 2000

Conventional methods for collecting origin-destination trips have been mainly relied on the surveys of home or roadside interview. However, the methods tend to be costly, labor intensive and time disruptive to the trip makers, thus the methods are not considered suitable for Planning applications such as routing guidance, arterial management and information Provision, as the parts of deployments in Intelligent Transport Systems Motivated by the problems, more economic ways to estimate origin-destination trip tables have been studied since the late 1970s. Some of them, which have been estimating O-D table from link traffic counts are generally Entropy maximizing, Maximum likelihood, Generalized least squares(GLS), and Bayesian inference estimation etc. In the Paper, with user equilibrium constraint we formulate GLS problem for estimating O-D trips and develop a solution a1gorithm by using Genetic Algorithm, which has been known as a g1oba1 searching technique. For the purpose of evaluating the method, we apply it to Seoul inner ringroad and compare it with gradient method proposed by Spiess(1990). From the resu1ts we fond that the method developed in the Paper is superior to other.

• Korean journal of applied entomology
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• v.35 no.2
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• pp.119-125
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• 1996

Temperature-dependent development study for overwintered eggs of Pseudococcus comstocki (Kuwana) wasconducted to develop a forecasting model for egg hatch date. Hatch times of overwintered eggs were comparedat five constant temperatures (10, 15, 20, 25, 27$^{\circ}$C) and different collection dates. A nonlinear, four-parameterdevelopmental model with high temperature inhibition accurately described (R2=0.9948) mean developmentalrates of all temperatures. Variation in developmental times was modeled(~~=0.972w9)it h a cumulative Weibullfunction. Least-squares linear regression (rate=O.O06358[Temp.]-0.07566)d escribed development in the linearregion (15-25$^{\circ}$C) of the development curve. The low development threshold temperature was estimated 11.9"Cand 154.14 degree-days were required for complete development. The linear degree-day model (thermal summation)and rate summation model (Wagner et al. 1985) were validated using field phenology data. In degreedaymodels, mean-minus-base method, sine wave method, and rectangle method were used in estimation of dailythermal units. Mean-minus-base method was 18 to 28d late, sine wave method was 11 to 14d late, rectanglemethod was 3 to 5d late, and rate summation model was 2 to 3d late in predicting 50% hatch of overwinteredeggs. hatch of overwintered eggs.

• 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.

• 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.

• 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.

• 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.

• 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.