• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4350-4356
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• 2023

Radiation detection systems working at high count rates suffer from the overlapping of their output electric pulses, known as pulse pile-up phenomenon, resulting in spectrum distortion and degradation of the energy resolution. Pulse tail extrapolation is a pile-up correction method which tries to restore the shifted baseline of a piled-up pulse by extrapolating the overlapped part of its preceding pulse. This needs a mathematical model which is almost always nonlinear, fitted usually by a nonlinear least squares (NLS) technique. NLS is an iterative, potentially time-consuming method. The main idea of the present study is to replace the NLS technique by an integration-based non-iterative method (NIM) for pulse tail extrapolation by an exponential model. The idea of linear extrapolation, as another non-iterative method, is also investigated. Analysis of experimental data of a NaI(Tl) radiation detector shows that the proposed non-iterative method is able to provide a corrected spectrum quite similar with the NLS method, with a dramatically reduced computation time and complexity of the algorithm. The linear extrapolation approach suffers from a poor energy resolution and throughput rate in comparison with NIM and NLS techniques, but provides the shortest computation time.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.257-266
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• 2016

Random Coefficient Autoregressive models (RCA) have attracted increased interest due to the wide range of applications in biology, economics, meteorology and finance. We consider an RCA as an appropriate model for non-linear properties and better than an AR model for linear properties. We study the methods of RCA parameter estimation. Especially we proposed the special case that an random coefficient ${\phi}(t)$ has the initial value ${\phi}(0)$ in the RCA model. In practical study, we estimated the parameters and compared Prediction Error Sum of Squares (PRESS) criterion between AR and RCA using Korean Mumps data.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.931-954
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• 2003

It is widely recognized that economic time series involved not only the linearities but also the non-linearities. In this paper, when the economic time series data have the nonlinear characteristics we propose the forecasts method using combinations of both forecasts from linear and nonlinear models. In empirical study, we compare the forecasting performance of 4 exchange rates models(AR, GARCH, AR+GARCH, Bilinear model) and combination of these forecasts for dairly Won/Dollar exchange rates returns. The combination method is selected by the estimated individual forecast errors using Monte Carlo simulations. And this study shows that the combined forecasts using unrestricted least squares method is performed substantially better than any other combined forecasts or individual forecasts.

• International Journal of CAD/CAM
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• v.6 no.1
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• pp.89-97
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• 2006

This paper presents a novel approach for non-iterative surface smoothing with feature preservation on arbitrary meshes. Laplacian operator is performed in a global way over the mesh. The surface smoothing is formulated as a quadratic optimization problem, which is easily solved by a sparse linear system. The cost function to be optimized penalizes deviations from the global Laplacian operator while maintaining the overall shape of the original mesh. The features of the original mesh can be preserved by adding feature constraints and barycenter constraints in the system. Our approach is simple and fast, and does not cause surface shrinkage and distortion. Many experimental results are presented to show the applicability and flexibility of the approach.

• Genomics & Informatics
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• v.1 no.2
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• pp.94-100
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• 2003

Motivation: Many have observed a nonlinear relationship between the signal intensity and the transcript abundance in microarray data. The first step in analyzing the data is to normalize it properly, and this should include a correction for the nonlinearity. The commonly used linear normalization schemes do not address this problem. Results: Nonlinearity is present in both cDNA and oligonucleotide arrays, but we concentrate on the latter in this paper. Across a set of chips, we identify those genes whose within-chip ranks are relatively constant compared to other genes of similar intensity. For each gene, we compute the sum of the squares of the differences in its within-chip ranks between every pair of chips as our statistic and we select a small fraction of the genes with the minimal changes in ranks at each intensity level. These genes are most likely to be non-differentially expressed and are subsequently used in the normalization procedure. This method is a generalization of the rank-invariant normalization (Li and Wong, 2001), using all available chips rather than two at a time to gather more information, while using the chip that is least likely to be affected by nonlinear effects as the reference chip. The assumption in our method is that there are at least a small number of non­differentially expressed genes across the intensity range. The normalized expression values can be substantially different from the unnormalized values and may result in altered down-stream analysis.

• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.4
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• pp.97-105
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• 2001

Rubber bearings may exhibit a significant cold temperature effect and some velocity dependency(strain rate effect). Both of these attributes which affect non-linear behavior must be accounted for when accurately modeling the bearings behavior, therefore, an analytical models is proposed to consider the effects of the cold temperature and strain rate on both rubber and lead. From the results of an experimental investigation where the frozen bearings were tested under lateral cyclic loading with constant axial load, a non-linear system identification with least squares procedure was applied to determine the material properties of rubber and lead. It is demonstrated that the proposed analytical model is able to simulate the reversed cyclic loading behavior of elastometric and lead-rubber bearings.

• Analytical Science and Technology
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• v.12 no.5
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• pp.375-381
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• 1999

Near infrared spectrophotometry(NIR) was developed as a non-invasive determination of blood alcohol. The first pure alcohol/water samples were prepared with ethanol concentration from 0.01 to 0.1%(w/w). Analysis of the second-derivative data was accomplished with multilinear regression(MLR). The standard error of calibration(SEC) of ethanol in ethanol/water solutions was approximately 0.0039%. The calibration models were established from the blood alcohol spectra by MLR and PLSR analysis. The best calibration was built with the second-derivative spectra of 2266 and 2326 nm by MLR. Second-derivative spectra in the spectral ranges of 1100~1340, 1500~1796 and 2064~2300 nm with four PLSR factors provided the standard error of prediction(SEP) of 0.030%(w/w). These results indicate that NIR may be applied for a fast non-invasive determination of alcohol in the blood.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.265-272
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• 2007

This proposed gamma (${\gamma}$) correction system is developed to reduce the difference between non-linear gamma curve produced by a typical formula and result produced by the proposed algorithm. In order to reduce the difference, the proposed system is using the Least Squares Polynomial which is calculating the best fitting polynomial through a set of points which is sampled. Each system is consisting of continuous several kinds of equations and having their own overlap sections to get more precise. Based on the algorithm verified by MATLAB, the proposed systems are implemented by using Verilog-HDL. This paper will compare the previous algorithm of gamma system such as Existing system with Seed Table with the latest that such as Proposed system. The former and the latter system have 1, 2 clock latency; each 1 result per clock. Because each of the error range (LSB) is $1{\sim}+1,\;0{\sim}+36$, we can how that Proposed system is improved. Under the condition of SAMSUNG STD90 0.35 worst case, each gate count is 2,063, 2,564 gates and each maximum data arrival time is 29.05[ns], 17.52[ns], respectively.

• Journal of Biosystems Engineering
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• v.33 no.1
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• pp.63-68
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• 2008

This study was carried out to find a useful method to predict hardness of tomato using optical spectrum data. Optical spectrum of reflectance and transmittance data were collected processed by 9 kind of preprocessing methods-normalizations of mean, maximum and range, SNV (standard normal variate), MSC (multiplicative scatter correction), the first derivative and second derivative of Savitzky-Golay and Norris-Gap. With the preprocessed and non-processed original spectrum data, prediction models of hardness of tomato were developed using analytical tools of PLS (partial least squares) and MLR (multiple linear regression) and tested for their validation. The test of validation resulted that the analytical tools of PLS and MLR output similar performances while the transmittance spectra showed much better result than the reflectance spectra.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.8
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• pp.1273-1280
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• 2003

In this paper, transparent dynamic photoelastic experimental hybrid method for propagating cracks in orthotropic material was developed. Using transparent dynamic photoelastic experimental hybrid method, we can obtain stress intensity factor and separate the stress components from only isochromatic fringe patterns without using isoclinics. When crack is propagated with constant velocity, the contours of stress components in the vicinity of crack tip in orthotropic material are similar to those of isotropic material or orthotropic material with stationary crack under the static load. Dynamic stress intensity factors are decreased as crack growths. It was certified that the dynamic photoelastic experimental hybrid method was very useful for the analysis of the dynamic fracture mechanics.