• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.169-177
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• 2006

In this paper, we present an efficient image reconstruction method which is suited to remove various noise generated from measurement using X-ray attenuation. To be specific, we present a wavelet method to efficiently remove ring artifacts, which are caused by inevitable mechanical error in X-ray emitters and detectors. and streak artifacts, which are caused by general observation errors and Fourier transform-based reconstruction process. To remove ring artifacts related noise from projections, we suggest to estimate the noise intensity by using the fact that the noise related to ring artifacts has a strong correlation in the angle direction, and remove them by using wavelet shrinkage. We also suggest to use wavelet-vaguelette decomposition for general-purpose noise removal and image reconstruction. Through simulation studies. we show that the proposed method provides a better result in ring artifact removal and image reconstruction over the traditional Fourier transform-based methods.

• 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 ILASS-Korea
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• v.7 no.4
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• pp.9-15
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• 2002

Convolution Fourier transformation tomographically reconstructs the spatially resolved spray injection rate from direct measuring cells. Asymmetric sprays generated from a twin-hole air shroud injector are tested with 12 equiangular projections of measurements. For each projection angle, line of sight integrated injection rate was measured at 35 positions with equal spacing measuring cells of 3 mm in width, 100 mm in length, 55 mm in depth and 0.5 mm thickness of separating wall. Interpolated data between the projection angles effectively increase the number of projections, which significantly enhances the signal-to-noise level in the reconstructed data. This modified convolution Fourier transformation scheme predicts well the structure of asymmetric sprays. Comparative study has been made between sprays with and without air shrouding. Tomograhpic reconstruction of injection rate from direct measuring cells obtained can be used to estimate the accuracy of volume fraction of spray from the LDPA tomographic reconstruction.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.5
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• pp.318-326
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• 1991

A Quantizer reconstruction level control method for block artifact reduction in DCT image coding is described. In our scheme, quantizer reconstruction level control is obtained by adding quantization level step size to the optimum quantization level in the direction of reducing the block artifact by minimizing the mean square error(MSE) and error difference(EDF) distribution in boundary without the other additional bits. In simulation results, although the performance in terms of signal to noise ratio is degraded by a little amount, mean square of error difference at block boundary and mean square error having relation block artifact is greatly reduced. Subjective image qualities are improved compared with other block artifact reduction method such as postprocessing by filtering and trasform coding by block overlapping. But the addition calculations of 1-dimensional DCT become to be more necessary to coding process for determining the reconstruction level.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.2
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• pp.59-64
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• 2001

In this paper, we propose an alternate method for determining the uniqueness of the reconstruction of a complex sequence from its phase. Uniqueness constraints could be derived in terms of the zeros of a complex polynomial defined by the DFT of the sequence. However, rooting of complex polynomials of high order is a very difficult problem. Instead of finding zeros of a complex polynomial, the proposed uniqueness criteria show that non-singularity of a matrix can guarantee the uniqueness of the reconstruction of a complex sequence from its phase-only data. It has clear advantage over the rooting method in numerical stability and computational time.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.410-421
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• 2019

Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1533-1540
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• 2023

Self-Powered Neutron Detectors(SPNDs) are used to calculate core power distributions, an essential factor in the safe operation of nuclear power plants. Some detectors may fail during normal operation, and signals from failed detectors are isolated from intact signals. The calculated detailed power distribution accuracy depends on the number of available detector signals. Failed detectors decrease the operating margin by enlarging the power distribution measurement error. Therefore, a thorough reconstruction of the failed detector signals is critical. This note suggests a compressive sensing based methodology that rationally reconstructs the readings of failed detectors. The methodology significantly improves reconstruction accuracy by sorting signals and removing high-frequency components from conventional compressive sensing methodology.

• Computers and Concrete
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• v.20 no.5
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.14-25
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• 2021

This paper presents a RLS based adaptive sliding mode observer (A-SMO) for unknown fault reconstruction in longitudinal autonomous driving. Securing the functional safety of autonomous vehicles from unexpected faults of sensors is essential for avoidance of fatal accidents. Because the magnitude and type of the faults cannot be known exactly, the RLS based A-SMO for unknown acceleration fault reconstruction has been designed with relationship function in this study. It is assumed that longitudinal acceleration of preceding vehicle can be obtained by using the V2V (Vehicle to Vehicle) communication. The kinematic model that represents relative relation between subject and preceding vehicles has been used for fault reconstruction. In order to reconstruct fault signal in acceleration, the magnitude of the injection term has been adjusted by adaptation rule designed based on MIT rule. The proposed A-SMO in this study was developed in Matlab/Simulink environment. Performance evaluation has been conducted using the commercial software (CarMaker) with car-following scenario and evaluation results show that maximum reconstruction error ratios exist within range of ±10%.

• Journal of radiological science and technology
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• v.40 no.3
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• pp.385-392
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• 2017

The purpose of this study is to investigate the image quality and exposure dose according to kVp and mAs in CT and to confirm improvement in image quality according to None IR and IR(Iterative Reconstruction) levels. Measurement results of image quality using Image J, HU(Hounsfield units) and BN(Background Noise) are decreased, while SNR(Signal to Noise Ratio) and $CTDI_{vol}$(CT dose index volume) are increased as the kVp increases and there was no change of BHU(Background Hounsfield units). BN was reduced due to increased kVp, while SNR and $CTDI_{vol}$ were increased. Also, the higher IR stage, the lower BN, SI(Signal Intensity) and HU while SNR was improved by about 10~60%. Based on this, when applying IR for clinical applications, it is necessary to finely adjust kVp and mA with a phased approach.