The Experts below are selected from a list of 75522 Experts worldwide ranked by ideXlab platform
Feng Dong - One of the best experts on this subject based on the ideXlab platform.
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I2MTC - Analysis of Sensitivity Matrix for Electrical Resistance Tomography
2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2019Co-Authors: Ziqi Liu, Feng DongAbstract:Electrical Resistance Tomography (ERT) is a detection technique for reconstructing conductivity distribution from boundary measurements. However, the image reconstruction of ERT is an ill-posed and nonlinear inverse problem. There are many algorithms for solving inverse problems based on the Sensitivity theory, the basic idea of which is to use the Sensitivity Matrix of reference field to represent the Sensitivity Matrix of the reconstruction process. However, this approximation of reconstruction process is based on the premise that the change of electrical conductivity distribution can be ignored and the distribution of sensitive field is approximately unchanged. The error introduced by using the Sensitivity Matrix of reference field for solving process cannot be ignored when the change of electrical conductivity cannot be ignored in the field. In order to optimize the solution process, this paper analyzes the factors that cause the solution error by the adopted Sensitivity Matrix and further explores the relationship between the Sensitivity Matrix and electrical conductivity distribution. The preliminary optimization direction of Sensitivity Matrix is also suggested.
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Difference Sensitivity Matrix constructed for ultrasound modulated electrical resistance tomography
Measurement Science and Technology, 2018Co-Authors: Zhang Shengnan, Feng DongAbstract:Based on acousto-electric modulation, ultrasound modulated electrical resistance tomography (UMERT) is expected to provide high spatial resolution by extracting more information about the conductivity distribution from data enriched by coupling impedance measurements to localized mechanical vibrations. A difference Sensitivity Matrix constructed from reference and the measured field is proposed for UMERT. Firstly, the difference Sensitivity Matrix, related to conductivity information of the measured field, can suppress the adverse influences of soft-field effects on image reconstruction, which usually causes relatively large errors in traditional electrical resistance tomography (ERT) image reconstruction. Secondly, the differential form adopted by the proposed Sensitivity Matrix reduces the effect of the feature of the nonlinearity of the electric field on the distribution of the Sensitivity Matrix, which is reflected in Sensitivity with a relatively low value in the central area whilst with a high value in the boundary area. Finally, the differential form can also reduce the influences of systematic errors on measurement data and thus, further improve the spatial resolution of reconstructed images. In addition, three current excitation patterns are discussed in order to obtain the best Sensitivity of boundary voltage variations to conductivity changes. The proposed Sensitivity Matrix and the corresponding reconstructed image results are compared with that based on Geselowitz's Sensitivity theorem in ERT and one constructed from the measured field in UMERT. Both theory and simulation results verify the feasibility of the proposed difference Sensitivity Matrix. The reconstructed images demonstrate higher spatial resolution, especially for the detection of small objects. It also has a stronger ability in identifying the size of the objects and noise immunity.
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brain tissue based Sensitivity Matrix in hemorrhage imaging by magnetic induction tomography
Instrumentation and Measurement Technology Conference, 2017Co-Authors: Zhili Xiao, Chao Tan, Feng DongAbstract:Cerebral hemorrhage can be detected and reconstructed by frequency-difference imaging method in magnetic induction tomography(MIT). The traditional Sensitivity Matrix used in solving the inverse problem of MIT was usually based on reciprocity theorem which cannot reconstruct the other brain tissues. Based on the frequency-dependent change of biological tissues conductivity, exact normal distribution of brain tissue was implemented as prior information to produce a new Sensitivity Matrix. A 2D normal realistic head model with 6 kinds of tissues was simulated by finite element analysis software to calculate the brain tissue based Sensitivity Matrix. Brain tissue based Sensitivity maps were investigated and compared with the traditional Sensitivity maps. Frequency-difference images of hemorrhage were reconstructed by Tikhonov regularization method using the brain tissue based Sensitivity Matrix. The results show that the proposed Sensitivity Matrix can reconstruct the hemorrhage more accurate relative to the other brain tissues than traditional Sensitivity Matrix by reconstructing the other brain tissues.
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I2MTC - Brain tissue based Sensitivity Matrix in hemorrhage imaging by magnetic induction tomography
2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2017Co-Authors: Zhili Xiao, Chao Tan, Feng DongAbstract:Cerebral hemorrhage can be detected and reconstructed by frequency-difference imaging method in magnetic induction tomography(MIT). The traditional Sensitivity Matrix used in solving the inverse problem of MIT was usually based on reciprocity theorem which cannot reconstruct the other brain tissues. Based on the frequency-dependent change of biological tissues conductivity, exact normal distribution of brain tissue was implemented as prior information to produce a new Sensitivity Matrix. A 2D normal realistic head model with 6 kinds of tissues was simulated by finite element analysis software to calculate the brain tissue based Sensitivity Matrix. Brain tissue based Sensitivity maps were investigated and compared with the traditional Sensitivity maps. Frequency-difference images of hemorrhage were reconstructed by Tikhonov regularization method using the brain tissue based Sensitivity Matrix. The results show that the proposed Sensitivity Matrix can reconstruct the hemorrhage more accurate relative to the other brain tissues than traditional Sensitivity Matrix by reconstructing the other brain tissues.
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Construction of Sensitivity Matrix involving location information for ultrasound modulated electrical impedance tomography
2017 IEEE International Conference on Imaging Systems and Techniques (IST), 2017Co-Authors: Zhang Shengnan, Feng DongAbstract:Electrical impedance tomography (EIT) was developed rapidly in industrial and clinical applications in recent years. However, the inverse problem of EIT is ill-posed. Based on acousto-electric effect, ultrasound modulated electrical impedance tomography (UMEIT) can combine electric and acoustic modalities, and this technique can obtain much more effective measurement information, especially in which the important location information is included. The corresponding location information of the focused ultrasound was taken account into the Sensitivity Matrix. Two different patterns of opposite excitation were applied, in addition, data of different patterns were combined and analyzed. Image reconstruction was implemented with the linear back-projection (LBP) algorithm, the results were compared with the ones from EIT that was based on Geselowitz's Sensitivity theorem. Finally, the models with noise were tested. Compared to that of EIT, the reconstructed images demonstrated higher spatial resolution by UMEIT.
W W Dai - One of the best experts on this subject based on the ideXlab platform.
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A direct Sensitivity Matrix approach for fast reconstruction in electrical impedance tomography
Physiological Measurement, 1994Co-Authors: J. P. Morucci, M. Granie, M Lei, P M Marsili, Y Shi, W W DaiAbstract:In electrical impedance imaging, several proposed reconstruction algorithms have employed the concept of a Sensitivity Matrix, which can be used to relate the magnitude of a boundary voltage change of a 2D object to the change in conductivity inside the object that has given rise to it. The search for an appropriate inversion of the Sensitivity Matrix is the key to these algorithms. In this work, a method called the direct Sensitivity Matrix approach for fast image reconstruction is proposed. Both theoretical and experimental results showing the efficiency of this proposed method are also presented.
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Direct Sensitivity Matrix approach for fast 3-D reconstruction in electrical impedance imaging
Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1994Co-Authors: J. P. Morucci, M. Granie, M Lei, Marie Chabert, W W DaiAbstract:A method called the direct Sensitivity Matrix (DSM) method is proposed, which assumes the differentiability of the conductivity distribution with respect to boundary voltage measurement and presents similitude with the method proposed by Tarassenko (1985). The DSM is composed of direct Sensitivity coefficients (DSC) defined as the conductivity change of a pixel divided by a resulting peripheral voltage change. There is no need for inversion of the DSM, and the reconstructed image of conductivity change is obtained directly by the product of the DSM and the measurement vector. The boundary element method (BEM) is used in the construction of DSM. >
Y. Ziya Ider - One of the best experts on this subject based on the ideXlab platform.
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Sensitivity Matrix analysis of the back-projection algorithm in Electrical Impedance Tomography
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1992Co-Authors: Nevzat G. Gencer, Mustafa Kuzuoglu, Y. Ziya IderAbstract:In Electrical Impedance Tomography, conductivity images can be reconstructed using a back-projection algorithm which is very similar to the ones used in other imaging modalities. In this study, the basic assumptions underlying the back-projection algorithm are investigated. Under these assumptions, a Sensitivity Matrix is derived relating the conductivity variation to the variation in gradients. A new image reconstruction method is suggested based on a minimization procedure which is equivalent to the solution of the Sensitivity Matrix equation in a generalized sense.
Etienne Patoor - One of the best experts on this subject based on the ideXlab platform.
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parameter identification of a thermodynamic model for superelastic shape memory alloys using analytical calculation of the Sensitivity Matrix
European Journal of Mechanics A-solids, 2014Co-Authors: Fodil Meraghni, Yves Chemisky, Boris Piotrowski, Rachid Echchorfi, Nadine Bourgeois, Etienne PatoorAbstract:Abstract This paper presents an identification procedure for the parameters of a thermodynamically based constitutive model for Shape memory Alloys (SMAs). The proposed approach is a gradient-based method and utilizes an analytical computation of the Sensitivity Matrix. For several loading cases, including superelasticity, that are commonly utilized for the model parameters identification of such a constitutive model, a closed-form of the total infinitesimal strain is derived. The partial derivatives of this state variable are developed to find the components of the Sensitivity Matrix. A Levenberg–Marquardt algorithm is utilized to solve the inverse problem and find the best set of model parameters for specific SMA materials. Moreover, a pre-identification method, based on the second derivative of the total strain components is proposed. This provides a suitable initial set of model parameters, which increases the efficiency of the inverse method. The proposed approach is applied for the simultaneous identification of the non-linear constitutive parameters for two superelastic SMAs. The comparison between experimental and numerical curves obtained for different temperatures shows the capabilities of the developed identification approach. The robustness and the efficiency of the developed approach are then experimentally validated.
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Parameter identification of a thermodynamic model for superelastic shape memory alloys using analytical calculation of the Sensitivity Matrix
European Journal of Mechanics - A Solids, 2014Co-Authors: Fodil Meraghni, Yves Chemisky, Boris Piotrowski, Rachid Echchorfi, Nadine Bourgeois, Etienne PatoorAbstract:This paper presents an identification procedure for the parameters of a thermodynamically based constitutive model for Shape memory Alloys (SMAs). The proposed approach is a gradient-based method and utilizes an analytical computation of the Sensitivity Matrix. For several loading cases, including superelasticity, that are commonly utilized for the model parameters identification of such a constitutive model, a closed-form of the total infinitesimal strain is derived. The partial derivatives of this state variable are developed to find the components of the Sensitivity Matrix. A LevenbergeMarquardt algorithm is utilized to solve the inverse problem and find the best set of model parameters for specific SMA materials. Moreover, a pre-identification method, based on the second derivative of the total strain components is proposed. This provides a suitable initial set of model parameters, which increases the efficiency of the inverse method. The proposed approach is applied for the simultaneous identification of the non-linear constitutive parameters for two superelastic SMAs. The comparison between experimental and numerical curves obtained for different temperatures shows the capabilities of the developed identification approach. The robustness and the efficiency of the developed approach are then experimentally validated
J. P. Morucci - One of the best experts on this subject based on the ideXlab platform.
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3D reconstruction in electrical impedance imaging using a direct Sensitivity Matrix approach
Physiological measurement, 1995Co-Authors: J. P. Morucci, M. Granie, M Lei, Marie Chabert, P M MarsiliAbstract:This paper presents a reconstruction algorithm using a direct Sensitivity Matrix (DSM) approach for fast 3D image reconstruction in electrical impedance imaging. The boundary element method (BEM) is used in the construction of this Matrix. The first images of a conductivity perturbation inside a sphere are reconstructed, using theoretical data.
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A direct Sensitivity Matrix approach for fast reconstruction in electrical impedance tomography
Physiological Measurement, 1994Co-Authors: J. P. Morucci, M. Granie, M Lei, P M Marsili, Y Shi, W W DaiAbstract:In electrical impedance imaging, several proposed reconstruction algorithms have employed the concept of a Sensitivity Matrix, which can be used to relate the magnitude of a boundary voltage change of a 2D object to the change in conductivity inside the object that has given rise to it. The search for an appropriate inversion of the Sensitivity Matrix is the key to these algorithms. In this work, a method called the direct Sensitivity Matrix approach for fast image reconstruction is proposed. Both theoretical and experimental results showing the efficiency of this proposed method are also presented.
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Direct Sensitivity Matrix approach for fast 3-D reconstruction in electrical impedance imaging
Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1994Co-Authors: J. P. Morucci, M. Granie, M Lei, Marie Chabert, W W DaiAbstract:A method called the direct Sensitivity Matrix (DSM) method is proposed, which assumes the differentiability of the conductivity distribution with respect to boundary voltage measurement and presents similitude with the method proposed by Tarassenko (1985). The DSM is composed of direct Sensitivity coefficients (DSC) defined as the conductivity change of a pixel divided by a resulting peripheral voltage change. There is no need for inversion of the DSM, and the reconstructed image of conductivity change is obtained directly by the product of the DSM and the measurement vector. The boundary element method (BEM) is used in the construction of DSM. >
