• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1289-1297
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• 2006

Before other statistical data analysis the preprocessing steps should be performed adequately to have meaningful results. These steps include processes such as baseline correction, normalization, denoising, and multiple alignment. In this paper an algorithm for baseline correction is proposed with using the piecewise cubic Hermite interpolation with block-selected points and local minima after denoising for SELDI or MALDI mass spectrometry data.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.14 no.2
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• pp.69-76
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• 2018

The paper presents preliminary investigation results for the effect of the baseline correction in the acceleration excitation method on finite element seismic analysis results (such as accumulated equivalent plastic strain, equivalent plastic strain considering cyclic plasticity, von Mises effective stress, etc) of nuclear safety Class I components. For investigation, finite element elastic-plastic time-history seismic analysis is performed for a surge line including a pressurizer lower head, a pressurizer surge nozzle, a surge piping, and a hot leg surge nozzle using the Chaboche hardening model. Analysis is performed for various seismic loading methods such as acceleration excitation methods with and without the baseline correction, and a displacement excitation method. Comparing finite element analysis results, the effect of the baseline correction is investigated. As a result of the investigation, it is identified that finite element analysis results using the three methods do not show significant difference.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.124-131
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• 2016

Baseline correction is very important due to influence on performance of spectral analysis in application of spectroscopy. Baseline is often estimated by parameter selection using visual inspection on analyte spectrum. It is a highly subjective procedure and can be tedious work especially with a large number of data. For these reasons, it is an objective and automatic procedure is necessary to select optimal parameter value for baseline correction. Asymmetrically reweighted penalized least squares (arPLS) based on penalized least squares was proposed for baseline correction in our previous study. The method uses a new weighting scheme based on the generalized logistic function. In this study, we present an automatic selection of optimal parameter for baseline correction using arPLS. The method computes fitness and smoothness values of fitted baseline within available range of parameters and then selects optimal parameter when the sum of normalized fitness and smoothness gets minimum. According to the experimental results using simulated data with varying baselines, sloping, curved and doubly curved baseline, and real Raman spectra, we confirmed that the proposed method can be effectively applied to optimal parameter selection for baseline correction using arPLS.

• Journal of radiological science and technology
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• v.45 no.5
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• pp.397-405
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• 2022

Computed radiography (CR) systems, which convert an analog signal recorded on a cassette into a digital image, combine the characteristics of analog and digital imaging systems. Compared to digital radiography (DR) systems, CR systems have presented difficulties in evaluating system performance because of their lower detective quantum efficiency, their lower signal-to-noise ratio (SNR), and lower modulation transfer function (MTF). During the step of energy-storing and reading out, a baseline offset occurs in the edge area and makes low-frequency overestimation. The low-frequency offset component in the line spread function (LSF) critically affects the MTF and other image-analysis or qualification processes. In this study, we developed the method of baseline correction using mathematical morphology to determine the LSF and MTF of CR systems accurately. We presented a baseline correction that used a morphological filter to effectively remove the low-frequency offset from the LSF. We also tried an MTF evaluation of the CR system to demonstrate the effectiveness of the baseline correction. The MTF with a 3-pixel structuring element (SE) fluctuated since it overestimated the low-frequency component. This overestimation led the algorithm to over-compensate in the low-frequency region so that high-frequency components appeared relatively strong. The MTFs with between 11- and 15-pixel SEs showed little variation. Compared to spatial or frequency filtering that eliminated baseline effects in the edge spread function, our algorithm performed better at precisely locating the edge position and the averaged LSF was narrower.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.500-506
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• 2019

In this study, we present a decision function of optimal smoothing parameter for baseline correction using Asymmetrically reweighted penalized least squares (arPLS). Baseline correction is very important due to influence on performance of spectral analysis in application of spectroscopy. Baseline is often estimated by parameter selection using visual inspection on analyte spectrum. It is a highly subjective procedure and can be tedious work especially with a large number of data. For these reasons, an objective procedure is necessary to determine optimal parameter value for baseline correction. The proposed function is defined by modeling the median value of possible parameter range as the length and order of the background signal. The median value increases as the length of the signal increases and decreases as the degree of the signal increases. The simulated data produced a total of 112 signals combined for the 7 lengths of the signal, adding analytic signals and linear and quadratic, cubic and 4th order curve baseline respectively. According to the experimental results using simulated data with linear, quadratic, cubic and 4th order curved baseline, and real Raman spectra, we confirmed that the proposed function can be effectively applied to optimal parameter selection for baseline correction using arPLS.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.4
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• pp.201-207
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• 2001

This paper describes an automated detection algorithm of ST-T episodes using global curvature which can connect the isoelectric level in ECG and can eliminate not only the slope of ST segment, but also difference of the baseline and global curve. This above method of baseline correction is very faster than the classical baseline correction methods. The optimal values of parameters for baseline correction were found as the value having the highest detection rate of ST episode. The features as input of backpropagation Neural Network were extracted from the whole ST segment. The European ST-T database was used as training and test data. Finally, ST elevation, ST depression and normal ST were classified. The average ST episode sensitivity and predictivity were 85.42%, 80.29%, respectively. This result shows the high speed and reliability in ST episode detection. In conclusion, the proposed method showed the possibility in various applications for the Holter system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.16-22
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• 2012

In this paper, we proposed a method of baseline correction for analysis of Raman spectra of platelets from Alzheimer's disease (AD) transgenic mice. Measured Raman spectra include the meaningful information and unnecessary noise which is composed of baseline and additive noise. The Raman spectrum is divided into the local region including several peaks and the spectrum of the region is modeled by curve fitting using Gaussian model. The additive noise is clearly removed from the process of replacing the original spectrum with the fitted model. The baseline correction after interpolating the local minima of the fitted model with linear, piecewise cubic Hermite and cubic spline algorithm. The baseline corrected models extract the feature with principal component analysis (PCA). The classification result of support vector machine (SVM) and maximum $a$ posteriori probability (MAP) using linear interpolation method showed the good performance about overall number of principal components, especially SVM gave the best performance which is about 97.3% true classification average rate in case of piecewise cubic Hermite algorithm and 5 principal components. In addition, it confirmed that the proposed baseline correction method compared with the previous research result could be effectively applied in the analysis of the Raman spectra of platelet.

• Progress in Superconductivity
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• v.9 no.1
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• pp.29-34
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• 2007

The baseline selection is the first and important step to analyze magnetocardiography (MCG) parameters. There are no difficulties to select the baseline between P- and Q-wave peak (P-Q interval) of MCG wave recorded from healthy subjects because the P-Q intervals of the healthy subjects do not much vary. However, patients with ischemic heart disease often show an unstable P-Q interval which does not seem to be appropriate for the baseline. In this case, T-P interval is alternatively recommended for the baseline. However, there has been no study on the difference made by the baseline selection. In this study, we studied the effect of the different baseline selection. MCG data were analyzed from twenty healthy subjects and twenty one patients whose baselines were alternatively selected in the T-P interval for their inappropriate P-Q interval. Paired T-test was used to compare two set of data. Fifteen parameters derived from the R-wave peak, the T-wave peak, and the period, $T_{max/3}{\sim}T_{max}$ were compared for the different baseline selection. As a result, most parameters did not show significant differences (p>0.05) except few parameters. Therefore, there will be no significant differences if anyone of two intervals were selected for the MCG baseline. However, for the consistent analysis, P-Q interval is strongly recommended for the baseline correction.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.3
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• pp.44-50
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• 2014

This paper describes reasonable methods by considering change of outdoor temperature into Customer Baseline Load(CBL) of Demand Resources in Smart Demand Resource Market, which controls peak power demand and maintains reliability of power system. The Smart Demand Resouce Market, which KPX(Korea Power Exchange) implement, is explained and then effects for CBL calculated by considering temperature correction factor are established. Finally, four methods for calculation of CBL are proposed and those results are compared and analyzed.

• Journal of Positioning, Navigation, and Timing
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• v.2 no.1
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• pp.9-17
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• 2013

In this paper, a pseudorange correction regeneration algorithm was developed to improve the positioning accuracy of DGPS using multi-reference stations, and the optimal minimum number of reference sites was determined by trying out different numbers of reference. This research was conducted using from two to five sites, and positioning errors of less than 1 m were obtained when pseudorange corrections are collected from at least four reference stations and interpolated as the pseudorange correction at the rover. After determining the optimal minimum number of reference stations, the pseudorange correction regeneration algorithm developed was tested by comparison with the performance of other algorithms. Our approach was developed based on an exponential model. If pseudorange corrections are regenerated using an exponential model, the effect of a small difference in the baseline distance can be enlarged. Therefore, weights can be applied sensitively even when the baseline distance differs by a small amount. Also weights on the baseline distance were applied differently by assigning weights depending on the difference of the longest and shortest baselines. Through this method, the positioning accuracy improved by 19% compared to the result of previous studies.