• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.43-57
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• 2022

Advances in measurement techniques have reduced measurement costs and enhanced safety resulting in less uncertainty. For example, an acoustic doppler current profiler (ADCP) based suspended sediment concentration (SSC) measurement technique is being accepted as an alternative to the conventional data collection method. In Korean rivers, horizontal ADCPs (H-ADCPs) are mounted on the automatic discharge monitoring stations, where SSC can be measured using the backscatter of ADCPs. However, automatic discharge monitoring stations and sediment monitoring stations do not always coincide which hinders the application of the new techniques that are not feasible to some stations. This work presents and analyzes H-ADCP-SSC models for 9 discharge monitoring stations in Korean rivers. In application of the Gaussian mixture model (GMM) to sediment-related variables (catchment area, particle size distributions of suspended sediment and bed material, water discharge-sediment discharge curves) from 44 sediment monitoring stations, it is revealed that those characteristics can distinguish sediment monitoring stations regionally. Linking the two results, we propose a protocol determining the H-ADCP-SSC model where no H-ADCP-SSC model is available.

• The Korean Journal of Nuclear Medicine
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• v.38 no.6
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• pp.486-491
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• 2004

Purpose: factor analysis and independent component analysis (ICA) has been used for handling dynamic image sequences. Theoretical advantages of a newly suggested ICA method, ensemble ICA, leaded us to consider applying this method to the analysis of dynamic myocardial $H_2^{15}O$ PET data. In this study, we quantified patients' blood flow using the ensemble ICA method. Materials and Methods: Twenty subjects underwent $H_2^{15}O$ PET scans using ECAT EXACT 47 scanner and myocardial perfusion SPECT using Vertex scanner. After transmission scanning, dynamic emission scans were initiated simultaneously with the injection of $555{\sim}740$ MBq $H_2^{15}O$. Hidden independent components can be extracted from the observed mixed data (PET image) by means of ICA algorithms. Ensemble learning is a variational Bayesian method that provides an analytical approximation to the parameter posterior using a tractable distribution. Variational approximation forms a lower bound on the ensemble likelihood and the maximization of the lower bound is achieved through minimizing the Kullback-Leibler divergence between the true posterior and the variational posterior. In this study, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Blood flow was measured in 9 regions - apex, four areas in mid wall, and four areas in base wall. Myocardial perfusion SPECT score and angiography results were compared with the regional blood flow. Results: Major cardiac components were separated successfully by the ensemble ICA method and blood flow could be estimated in 15 among 20 patients. Mean myocardial blood flow was $1.2{\pm}0.40$ ml/min/g in rest, $1.85{\pm}1.12$ ml/min/g in stress state. Blood flow values obtained by an operator in two different occasion were highly correlated (r=0.99). In myocardium component image, the image contrast between left ventricle and myocardium was 1:2.7 in average. Perfusion reserve was significantly different between the regions with and without stenosis detected by the coronary angiography (P<0.01). In 66 segment with stenosis confirmed by angiography, the segments with reversible perfusion decrease in perfusion SPECT showed lower perfusion reserve values in $H_2^{15}O$ PET. Conclusions: Myocardial blood flow could be estimated using an ICA method with ensemble learning. We suggest that the ensemble ICA incorporating non-negative constraint is a feasible method to handle dynamic image sequence obtained by the nuclear medicine techniques.

• Investigative Magnetic Resonance Imaging
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• v.18 no.4
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• pp.303-313
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• 2014

Purpose : In-vivo conductivity reconstruction using transmit field ($B_1{^+}$) information of MRI was proposed. We assessed the accuracy of conductivity reconstruction in the presence of statistical noise in complex $B_1{^+}$ map and provided a parametric model of the conductivity-to-noise ratio value. Materials and Methods: The $B_1{^+}$ distribution was simulated for a cylindrical phantom model. By adding complex Gaussian noise to the simulated $B_1{^+}$ map, quantitative conductivity estimation error was evaluated. The quantitative evaluation process was repeated over several different parameters such as Larmor frequency, object radius and SNR of $B_1{^+}$ map. A parametric model for the conductivity-to-noise ratio was developed according to these various parameters. Results: According to the simulation results, conductivity estimation is more sensitive to statistical noise in $B_1{^+}$ phase than to noise in $B_1{^+}$ magnitude. The conductivity estimate of the object of interest does not depend on the external object surrounding it. The conductivity-to-noise ratio is proportional to the signal-to-noise ratio of the $B_1{^+}$ map, Larmor frequency, the conductivity value itself and the number of averaged pixels. To estimate accurate conductivity value of the targeted tissue, SNR of $B_1{^+}$ map and adequate filtering size have to be taken into account for conductivity reconstruction process. In addition, the simulation result was verified at 3T conventional MRI scanner. Conclusion: Through all these relationships, quantitative conductivity estimation error due to statistical noise in $B_1{^+}$ map is modeled. By using this model, further issues regarding filtering and reconstruction algorithms can be investigated for MREPT.

• Journal of Soil and Groundwater Environment
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• v.11 no.6
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• pp.28-34
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• 2006

Laboratory column tests using $Cl^-$ tracer were conducted to study the correlation of soil particle distribution and hydrodynamic dispersion mechanism with three kinds of ununiformed soil samples, in which the ratio of gravel and sand versus silt and clay is 24.5 for S-1 soil, 4.48 for S-2 soil, and 0.4 for S-3 soil. Chloride breakthrough curves with time were fitted with gaussian functions. The relative concentrations of chloride were converged to 1.0 after 0.7 hours for S-1, 6.3 hours for S-2, and 389 hours for S-3. Average linear velocity, longitudinal dispersion coefficient, and longitudinal dispersivity were calculated by chloride breakthrough curves. Longitudinal dispersion coefficients were $1.20{\times}10^{-4}\;m^2/sec$ for S-1, $8.87{\times}10^{-7}\;m^2/sec$ for S-2, and $1.94{\times}10^{-9}\;m^2/sec$ for S-3. Peclet numbers calculated by the molecular diffusion coefficient of chloride and the mean grain diameters of soils were $2.59{\times}10^2$ for S-1, $6.27{\times}10^0$ for S-2, and $1.35{\times}10^{-4}$ for S-3. Mechanical dispersion was dominant for the hydrodynamic dispersion mechanism of S-1. Both mechanical dispersion and molecular diffusion were dominant for the hydrodynamic dispersion mechanism of S-2, but mechanical dispersion was ascendant over molecular diffusion. Hydrodynamic dispersion in S-3 was occurred mainly by molecular diffusion. When plotting three soils on the graph of $D_L/D_m$ versus Peclet number produced by Bijeljic and Blunt (2006), the values of $D_L/D_m$ for S-1 and S-2 were more than 2.0 order compared to their graph. S-3 was not plotted on their graph because the Peclet number was as small as $1.35{\times}10^{-4}$.