• Journal of the Korean Magnetic Resonance Society
• /
• v.9 no.2
• /
• pp.93-102
• /
• 2005

Purpose: To develop an advanced non-linear curve fitting (NLCF) algorithm for dynamic susceptibility contrast study of brain. Materials and Methods: The first pass effects give rise to spuriously high estimates of $K^{trans}$ in voxels with large vascular components. An explicit threshold value has been used to reject voxels. Results: By using this non-linear curve fitting algorithm, the blood perfusion and the volume estimation were accurately evaluated in T2*-weighted dynamic contrast enhanced (DCE)-MR images. From the recalculated each parameters, perfusion weighted image were outlined by using modified non-linear curve fitting algorithm. This results were improved estimation of T2*-weighted dynamic series. Conclusion: The present study demonstrated an improvement of an estimation of kinetic parameters from dynamic contrast-enhanced (DCE) T2*-weighted magnetic resonance imaging data, using contrast agents. The advanced kinetic models include the relation of volume transfer constant $K^{trans}\;(min^{-1})$ and the volume of extravascular extracellular space (EES) per unit volume of tissue $\nu_e$.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.3
• /
• pp.163-168
• /
• 2011

In this paper, we proposed non-linear gamma curve algorithm for gamma correction. The previous non-linear gamma curve algorithm is generated by the least square polynomial using the Gauss-Jordan inverse matrix. However, the previous algorithm has some weak points. When calculating coefficients using inverse matrix of higher degree, occurred truncation errors. Also, only if input sample points are existed regular interval on 10-bit scale, the least square polynomial is accurately works. To compensate weak-points, we calculated accurate coefficients of polynomial using eigenvalue and orthogonal value of mat11x from singular value decomposition (SVD) and QR decomposition of vandemond matrix. Also, we used input data part segmentation, then we performed polynomial curve fitting and merged curve fitting results. When compared the previous method and proposed method using the mean square error (MSE) and the standard deviation (STD), the proposed segmented polynomial curve fitting is highly accuracy that MSE under the least significant bit (LSB) error range is approximately $10^{-9}$ and STD is about $10^{-5}$.

• Investigative Magnetic Resonance Imaging
• /
• v.11 no.2
• /
• pp.103-109
• /
• 2007

To develop an advanced non-linear curve fitting (NLCF) algorithm for performing dynamic susceptibility contrast study of the brain. The first pass effects give rise to spuriously high estimates of $K^{trans}$ for the voxels that represent the large vascular components. An explicit threshold value was used to reject voxels. The blood perfusion and volume estimation were accurately evaluated in the $T2^*$-weighted dynamic contrast enhanced (DCE)-MR images. From each of the recalculated parameters, a perfusion weighted image was outlined by using the modified non-linear curve fitting algorithm. The present study demonstrated an improvement of an estimation of the kinetic parameters from the DCE $T2^*$-weighted magnetic resonance imaging data with using contrast agents.

• Journal of the Korean Society for information Management
• /
• v.29 no.2
• /
• pp.193-204
• /
• 2012

The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.10 no.4 s.50
• /
• pp.25-33
• /
• 2006

The purpose of this study is to develop the curve fitted equations for practicality and actual calculation during seismic performance evaluation of buried pipelines. Curve fitting for strain curve according to the wavelength of the seismic wave was produced using the non-linear least square method and the equations with the best results was suggested. In addition, a degree and coefficient of polynomial fitting equation needed to use curve fitted equation were identified. Interpreting process during the test of resistance of earthquake of buried pipelines with various end boundary conditions were provided through example questions. The results of this study were used to conduct a dynamic response analysis and a seismic performance evaluation of concrete, steel, and FRP pipes with various end boundary conditions.

• Journal of the Korean Geotechnical Society
• /
• v.20 no.8
• /
• pp.49-58
• /
• 2004

The cone pressuremeter test (CPM) is a new in-situ test which combines a standard cone penetration test with a pressuremeter. The cone pressuremeter tests in clay are presented and analyzed. An analytical solution of CPM incorporated non-linear soil behavior with no volume change is presented, and curve fitting technique is proposed to make use of both the loading and unloading portions of the pressuremeter test. The proposed method is accomplished by putting greater emphasis on the unloading portion. Twenty CPM tests are analyzed using the proposed method, and the derived undrained shear strength of soil is compared with other tests such as field vane tests and laboratory tests. The interpreted soil parameters had resonable values when compared to other in-situ and laboratory test results. The cone pressuremeter has provided reliable measures of undrained shear strength using curve fitting method.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.6
• /
• pp.1645-1651
• /
• 2016

A lot of data, particularly in the medical field, contain variables that have a measurement error such as blood pressure and body mass index. On the other hand, recently smoothing methods are often used to solve a complex scientific problem. In this paper, we study a Bayesian curve-fitting under functional measurement error model. Especially, we extend our previous model by incorporating covariates free of measurement error. In this paper, we consider penalized splines for non-linear pattern. We employ a hierarchical Bayesian framework based on Markov Chain Monte Carlo methodology for fitting the model and estimating parameters. For application we use the data from the fifth wave (2012) of the Korea National Health and Nutrition Examination Survey data, a national population-based data. To examine the convergence of MCMC sampling, potential scale reduction factors are used and we also confirm a model selection criteria to check the performance.

• Proceedings of the KSMRM Conference
• /
• 2006.11a
• /
• pp.82-82
• /
• 2006

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.7
• /
• pp.913-917
• /
• 2013

During the restoration process of primary restorative transmission system, some over voltages may happen due to nonlinear interaction between unloaded transformers and transmission systems. These over voltages caused by harmonic resonance can be suppressed by inserting damping loads before energizing transformers. But it is very difficult to predict the occurrence possibility of harmonic resonance and complex simulation must be repeated to estimate the sufficient damping loads. This paper presents a damping loads prediction system to prevent harmonic resonance. Detailed analysis of the relationship between harmonic resonance and the amount of damping loads is discussed. The prediction system is developed using a curve fitting and a neural network based on this relationship. A curve fitting used a Gaussian function based on non-linear least square method and multi-layer back-propagation neural network is applied. The system is applied to primary restorative transmission lines in korean power system and the result showed satisfactory performance.

• Progress in Medical Physics
• /
• v.6 no.2
• /
• pp.83-92
• /
• 1995

The T1 mapping of an human anatomy may give a characteristic contrast among the various tissues and the normal/abnormal tissues. Here, the methodology of constructing T1 map out of several images with different TRs, will be described using non-linear curve fitting. The general curve fitting algorithm requires the initial trial values T1t and Mot for the variables to be fitted. Three different methods of suppling the trial T1t and Mot are suggested and compared for the efficiency and the accuracy. The curve-fitting routine was written in ANSI C and excuted on a SUN workstation. Several distilled-water phantoms with various concentrations of Gd-DTPA were prepared to examine the accuracy of the curve-fitting program. An MR image was used as the true proton density image without any random noise, and several images with different TRs were generated with the theoretical T1 relaxation times 250, 500, and 1000msec. The random noise of 1, 5, and 10％ were embedded into the simulated images. These images were used to generate the T1 map, and the resultant T1 maps for each T1 were analyzed to study the effect of the random noise on the T1 map.