• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1452-1459
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• 2016

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in and under Nyquist rate representation. Generally, the measurement prediction usually works well with a small block while the quality of recovery is known to be better with a large block. In order to overcome this dilemma, conventional research works use a structural measurement matrix with which compressed sensing is done in a small block size but recovery is performed in a large block size. In this way, both prediction and recovery are made to be improved at same time. However, the conventional researches did not compare the performances of the structural measurement matrix, affected by the block size. In this paper, by expanding a structural measurement matrix of conventional works, their performances are compared with different block sizes. Experimental results show that a structural measurement matrix with $4{\times}4$ Hadamard transform matrix provides superior performance in block size 4.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.133-136
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• 1987

This paper presents a new algorithm of optimal measurement system by using band matrix characteristic respectively for state estimation. A performance index of measurement system is established to reflect relation among measurement sets, probability of measurement failure and cost of individual meter installation. Selection ranking in the candidates of measurement sets is composed to guarantee the observability for any any single meter outage. Performance index sensitivity is introduced and recursive formula which based on the matrix inversion lemma used for selection. The proposed algorithm is composed of successive addition algorithm, successive elimination algorithm and combinatorial algorithm. The band matrix characteristic could save in memory requirements and calculate the performance index faster than earlier.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.631-634
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• 2014

We propose a measurement state feedback controller for a class of nonlinear systems that have uncertain nonlinearity and sensor noise. The new design method based on the matrix inequality approach solves the measurement feedback control problem of a class of nonlinear systems. As a result, the proposed methods using a matrix inequality approach has the flexibility to apply the controller. In addition, the sensor noise can be attenuated for more generalized systems containing uncertain nonlinearities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3559-3571
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• 2015

This paper addresses the problem of signal acquisition with a sparse representation in a given orthonormal basis using fewer noisy measurements. The authors formulate the problem statement for randomly measuring with strong signal noise. The impact of white Gaussian signals noise on the recovery performance is analyzed to provide a theoretical basis for the reasonable design of the measurement matrix. With the idea that the measurement matrix can be adapted for noise suppression in the adaptive CS system, an adapted selective compressive sensing (ASCS) scheme is proposed whose measurement matrix can be updated according to the noise information fed back by the processing center. In terms of objective recovery quality, failure rate and mean-square error (MSE), a comparison is made with some nonadaptive methods and existing CS measurement approaches. Extensive numerical experiments show that the proposed scheme has better noise suppression performance and improves the support recovery of sparse signal. The proposed scheme should have a great potential and bright prospect of broadband signals such as biological signal measurement and radar signal detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.834-838
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• 1992

Tracking performance depends on the quantity of the measurement data. In the Kalman-Bucy filter and other trackers, this dependence is well understood in terms of the measurement noise covariance matrix, which specifies the uncertainty in the value of measurement inputs. In this paper, we derived approximated error covariance matrix to evaluate the dependence of target detection probability and false alarm probability in the presence of uncertainty of measurement origin.

• Journal of the Korean Ceramic Society
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• v.29 no.8
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• pp.587-592
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• 1992

Sheets of SiC-SiC whisker maxed matrix were prepared from the mixed slurry of SiC whisker and SiC matrix by the rolling method. With the increase of SiC whisker, the pore size, the porosity and the phosphoric acid absorbency of the matrix were increased, while the bubble pressure was decreased. The activation energy for the transfer of H+ ion was decreased with the increase of mixing ratio of SiC whisker to the SiC matrix from the measurement of hydrogen ion conductivity. The activation energy was evaluated as 0.25 eV when the mixing ratio of SiC whisker to the SiC matrix was 1 : 2 and the activation energy was 0.16 eV for the 2 : 1 matrix. It means that SiC whisker matrix contributes to attain a better microstructure for the diffusion of hydrogen ion. From the measurement of single cell performance of matrix with various mixing ratio, it is concluded that if SiC-SiC whisker maxed matrix has a sufficient bubble pressure to prevent the crossover of H2 gas, the current density of a fuel cell is increased with the increase of acid absorbency of the matrix. Current density was improved from 140 mA/$\textrm{cm}^2$ for 0.25 mm thickness of matrix to 170 mA/$\textrm{cm}^2$ for the 0.20 mm one at 700 mV.

• Carbon letters
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• v.5 no.1
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• pp.18-22
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• 2004

The failure behaviours of unidirectional pultruded carbon fiber reinforced polymer (CFRP) composites were monitored by the electrical resistance measurement during tensile loading, three-point-bending, interlaminar shear loading. The tensile failure behaviour of carbon fiber tows was also investigated by the electrical resistance measurement. Infrared thermography non-destructive evaluation was performed in real time during tensile test of CFRP composites to validate the change of microdamage in the materials. Experiment results demonstrated that the CFRP composites and carbon fiber tows were damaged by different damage mechinsms during tensile loading, for the CFRP composites, mainly being in the forms of matrix damage and the debonding between matrix and fibers, while for the carbon fiber tows, mainly being in the forms of fiber fracture. The correlation between the infrared thermographs and the change in the electrical resistance could be regarded as an evidence of the damage mechanisms of the CFRP composites. During three-point-bending loading, the main damage forms were the simultaneity fracture of matrix and fibers firstly, then matrix cracking and the debonding between matrix and fiber were carried out. This results can be shown in Fig. 9(a) and (b). During interlaminar shear loading, the change in the electrical resistance was related to the damage degree of interlaminar structure. Electrical resistance measurement was more sensitive to the damage behaviour of the CFRP composites than the stress/time curve.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1018-1021
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• 2007

This paper explores the use of BEM based NAH to reconstruct the surface vibration of a plate in a rectangular finite cavity, in which the distances between sensors and the nearest points on the source surface are not equal. In such circumstances, different degree of information on propagating and non-propagating wave components will be detected by sensors at different positions, as well as the influence of measurement noise will vary significantly from the nearest points of measurement to the farthest ones. On the other hand, the condition number of the vibro-acoustic transfer function matrix relating normal surface velocities and field pressures will becomes high, numerically indicating an increase of linear dependency between rows of transfer function matrix. The combination of poor measurement and high condition number will result inaccurate reconstruction. Therefore, one approach to be investigated in this work is to select the measurement positions in such ways that reduce measurement redundancy, as it indicated by the condition number. The improvement is found to be significant in the numerical simulations utilizing two different criterions, spanning from over-determined to under-determined cases, and in the validation experiment.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.491-499
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• 2018

The conventional calibration method for a noise figure analyzer is to use a noise source. This method is accompanied by a significant irregular ripple in the measurement results, because it does not consider the mismatch of the noise source and noise figure analyzer during calibration. A novel calibration method of the noise figure analyzer is proposed that considers the mismatch between the noise power and noise figure analyzer. A novel noise correlation matrix measurement technique using this method is also proposed. The method determines the noise correlation matrix and the gain of the uncorrected noise figure analyzer using uncorrected noise powers. Then, having determined the gain and noise correlation matrix, the effects of noise figure analyzers were corrected in the measurement results of the noise correlation matrix for the device under test (DUT). Through the proposed method, the measured noise parameters of a DUT showed the same degree of irregular ripples as the result of using the relative noise ratio.