• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.20-25
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• 1980

A singular value decomposition (SVD) pseudo-inversion method has been applied to the image reconstruction from projections. This approach is relatively unknown and differs from conventionally used reconstructioll methods such as the Foxier convolution and iterative techniques. In this paper, two SVD pseudo-inversion methods have been discussed for the search of optimum reconstruction and restoration, one using truncated inverse filtering, the other scalar Wiener filtering. These methods partly overcome the ill-conditioned nature of restoration problems by trading off between noise and signal quality. To test the SVD pseudo-inversion method, simulations were performed from projection data obtained from a phantom using truncated inversefiltering. The results are presented together with some limitations particular to the applications of the method to the general class of 3-D image reconstruction and restoration.

• Journal of the Korean Magnetic Resonance Society
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• v.4 no.2
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• pp.116-124
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• 2000

Singular value decomposition (SVD) has been used during past few decades in the advanced NMR data processing and in many applicable areas. A new modified SVD, piecewise polynomial truncated SVD (PPTSVD) was developed far the large solvent peak suppression and noise elimination in U signal processing. PPTSVD consists of two algorithms of truncated SVD (TSVD) and L$_1$ problems. In TSVD, some unwanted large solvent peaks and noises are suppressed with a certain son threshold value while signal and noise in raw data are resolved and eliminated out in L$_1$ problem routine. The advantage of the current PPTSVD method compared to many SVD methods is to give the better S/N ratio in spectrum, and less time consuming job that can be applicable to multidimensional NMR data processing.

• Bulletin of the Korean Chemical Society
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• v.24 no.7
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• pp.967-970
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• 2003

A new modified singular value decomposition method, piecewise polynomial truncated SVD (PPTSVD), which was originally developed to identify discontinuity of the earth's radial density function, has been used for large solvent peak suppression and noise elimination in nuclear magnetic resonance (NMR) signal processing. PPTSVD consists of two algorithms of truncated SVD (TSVD) and L₁ problems. In TSVD, some unwanted large solvent peaks and noise are suppressed with a certain soft threshold value, whereas signal and noise in raw data are resolved and eliminated in L₁ problems. These two algorithms were systematically programmed to produce high quality of NMR spectra, including a better solvent peak suppression with good spectral line shapes and better noise suppression with a higher signal to noise ratio value up to 27% spectral enhancement, which is applicable to multidimensional NMR data processing.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.91-98
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• 2017

Electrical impedance tomography is a nondestructive imaging technique to reconstruct unknown conductivity distribution based on applied current data and measured voltage data through an array of electrodes attached on the periphery of a domain. In this paper, an inverse method based on truncated singular value decomposition is proposed to solve the inverse problem with the generalized Tikhonov regularization and to reconstruct the conductivity distribution. In order to reduce the inverse computational time, truncated singular value decomposition is applied to the inverse term after the generalized regularization matrix is taken out from the inverse matrix term. Numerical experiments and phantom experiments have been performed to verify the performance of the proposed method.

• Journal of Biomedical Engineering Research
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• v.24 no.5
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• pp.459-464
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• 2003

The source current distribution in a heart was reconstructed from the magnetocardiogram (MCG) and its clinical usefulness was demonstrated. MCG was measured using 40-channel superconducting quantum interference device (SQUID) gradiometers for a patient of Wolff-Parkinson-White (WPW) syndrome, which has an accessory pathway between the atria and the ventricles. Reconstruction of source current distribution in a plane below the chest surface was performed using minimum norm estimation (MNE) algorithm and truncated singular value decomposition (SVD), In the simulation, we confirmed that the current distributions. which were computed for the test dipoles, represented well the essential feature of the test current configurations, In the current map of WPW syndrome, we observed abnormal currents that would bypass the atrioventricular junction at a delta wave. However, we could not observe such currents any more after the surgery. These results showed that the obtained current distribution using MCG signals is consistent with the electrical activity in a heart and has clinical usefulness.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.743-750
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• 2017

Modal parameter identification plays a key role in the structural health monitoring (SHM) for civil engineering. Eigensystem realization algorithm (ERA) is one of the most popular identification methods. However, the complex environment around civil structures can introduce the noises into the measurement from SHM system. The spurious modes would be generated due to the noises during ERA process, which are usually ignored and be recognized as physical modes. This paper proposes an improved stabilization diagram method in ERA to distinguish the spurious modes. First, it is proved that the ERA can be performed by any two Hankel matrices with one time step shift. The effect of noises on the eigenvalues of structure is illustrated when the choice of two Hankel matrices with one time step shift is different. Then, a moving data diagram is proposed to combine the traditional stabilization diagram to form the improved stabilization diagram method. The moving data diagram shows the mode variation along the different choice of Hankel matrices, which indicates whether the mode is spurious or not. The traditional stabilization diagram helps to determine the concerned truncated order before moving data diagram is implemented. Finally, the proposed method is proved through a numerical example. The results show that the proposed method can distinguish the spurious modes.

• Structural Engineering and Mechanics
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• v.27 no.5
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• pp.625-638
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• 2007

The damage detection process may appear difficult to be implemented for truss structures because not all degrees of freedom in the numerical model can be experimentally measured. In this context, the damage locating vector (DLV) method, introduced by Bernal (2002), is a useful approach because it is effective when operating with an arbitrary number of sensors, a truncated modal basis and multiple damage scenarios, while keeping the calculation in a low level. In addition, the present paper also evaluates the noise influence on the accuracy of the DLV method. In order to verify the DLV behavior under different damages intensities and, mainly, in presence of measurement noise, a parametric study had been carried out. Different excitations as well as damage scenarios are numerically tested in a continuous Warren truss structure subjected to five noise levels with a set of limited measurement sensors. Besides this, it is proposed another way to determine the damage locating vectors in the DLV procedure. The idea is to contribute with an alternative option to solve the problem with a more widespread algebraic method. The original formulation via singular value decomposition (SVD) is replaced by a common solution of an eigenvector-eigenvalue problem. The final results show that the DLV method, enhanced with the alternative solution proposed in this paper, was able to correctly locate the damaged bars, using an output-only system identification procedure, even considering small intensities of damage and moderate noise levels.