• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05a
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• pp.87-91
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• 2002

본 연구는 탐지 대상물체의 형상인식 이 가능한 비접촉, 원격 탐지장치의 개발에 관한 것이다. 형체인식용 비접촉 시스템은 주로 Magneto-Impedance법과 Magneto-Inductance법이 있 으며, Magneto- Inductance법은 왜곡이 적고, 인체나 탐지대상물체에 손상을 가져오지 않는다. 본 연구에서는 2극으로 디자인 된 Magnetic Tomography System을 이용하여 물체의 위치에 따른 투자율 변화량을 계산 및 실측하였다.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.35-41
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• 2018

Electrical impedance tomography (EIT) is a kind of nondestructive testing technique that obtains the internal resistivity distribution from the voltages measured at the electrodes located outside the area of interest. However, an image reconstruction problem in EIT has innate non-linearity and ill-posedness, so that it is difficult to obtain satisfactory reconstructed results. In general, a neural network can efficiently model the input and output relationships of a non-linear system. This paper proposes a method for estimating the position and size of a circular target using a multi-layer neural network. To verify the performance of the proposed method, neural network was trained and various computer simulations were performed and satisfactory performance was verified.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.576-579
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• 2008

Electrical impedance(EIT) for the multi-phase flow visualization is an imaging modality in which the resistivity distribution of the unknown object is estimated based on the known sets of injected currents and measured voltages on the surface of the object. In this paper, an EIT reconstruction algorithm based on the extended Kalman filter(EKF) is proposed. The EIT reconstruction problem is formulated as a dynamic model which is composed of the state equation and the observation equation, and the unknown resistivity distribution is estimated recursively with the aid of the EKF. To verify the reconstruction performance of the proposed algorithm, experiments with simulated multi-phase flow are performed.

• Smart Structures and Systems
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• v.8 no.1
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• pp.139-155
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• 2011

Polymeric thin-film assemblies whose bulk electrical conductivity and mechanical performance have been enhanced by single-walled carbon nanotubes are proposed for measuring strain and corrosion activity in metallic structural systems. Similar to the dermatological system found in animals, the proposed self-sensing thin-film assembly supports spatial strain and pH sensing via localized changes in electrical conductivity. Specifically, electrical impedance tomography (EIT) is used to create detailed mappings of film conductivity over its complete surface area using electrical measurements taken at the film boundary. While EIT is a powerful means of mapping the sensing skin's spatial response, it requires a data acquisition system capable of taking electrical impedance measurements on a large number of electrodes. A low-cost wireless impedance analyzer is proposed to fully automate EIT data acquisition. The key attribute of the device is a flexible sinusoidal waveform generator capable of generating regulated current signals with frequencies from near-DC to 20 MHz. Furthermore, a multiplexed sensing interface offers 32 addressable channels from which voltage measurements can be made. A wireless interface is included to eliminate the cumbersome wiring often required for data acquisition in a structure. The functionality of the wireless impedance analyzer is illustrated on an experimental setup with the system used for automated acquisition of electrical impedance measurements taken on the boundary of a bio-inspired sensing skin recently proposed for structural health monitoring.

• 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 the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.83-90
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• 2017

Electrical impedance tomography is a relatively new nondestructive imaging modality in which the internal conductivity distribution is reconstructed based on the injected currents and measured voltages through electrodes placed on the surface of a domain. In this paper, a fast iterative Gauss-Newton method is proposed to increase the spatial resolution as well as reduce the inverse computational time in the inverse problem, which could be applied to online binary mixture flow applications. To evaluate the reconstruction performance of the proposed method, numerical experiments have been carried out and the results are analyzed.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.279-287
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• 2009

Magnetic resonance electrical impedance tomography (MREIT) is a new medical imaging modality providing cross-sectional images of a conductivity distribution inside an electrically conducting object. MREIT has rapidly progressed in its theory, algorithm and experimental technique and now reached the stage of in vivo animal and human experiments. Conductivity image reconstructions in MREIT require various steps of carefully implemented numerical computations. To facilitate MREIT research, there is a pressing need for an MREIT software package with an efficient user interface. In this paper, we present an example of such a software, called CoReHA which stands for conductivity reconstructor using harmonic algorithms. It offers various computational tools including preprocessing of MREIT data, identification of boundary geometry, electrode modeling, meshing and implementation of the finite element method. Conductivity image reconstruction methods based on the harmonic $B_z$ algorithm are used to produce cross-sectional conductivity images. After summarizing basics of MREIT theory and experimental method, we describe technical details of each data processing task for conductivity image reconstructions. We pay attention to pitfalls and cautions in their numerical implementations. The presented software will be useful to researchers in the field of MREIT for simulation as well as experimental studies.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.134-137
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• 2003

Newton-Raphson is most used algorithm in EIT(electrical impedance tomography), cross-sectional distribution of resistivity is reconstructed by mean of both generating and sensing electrodes attached onto the surface of the object. EIT has been suffered from the severe ill-posedness which is caused by the inherent low sensitivity of boundary measurements to any changes of internal resistivity values. In this paper, we propose modified cost function and weighting factor that compensate for low sensitivity between boundary measurements and internal resistivity and improve performance of Newton-Raphson for EIT in region of interest.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.5-7
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• 1991

We have developed an efficient and robust image reconstruction algorithm for static impedance imaging. This improved Newton-Raphson method produced more accurate images by reducing the undesirable effects of the ill-conditioned Hessian matrix. We found that our electrical impedance tomography (EIT) system could produce two-dimensional static images from a physical phantom with 7% spatial resolution at the center and 5% at the periphery. Static EIT image reconstruction requires a large amount of computation. In order to overcome the limitations on reducing the computation time by algorithmic approaches, we implemented the improved Newton-Raphson algorithm on a parallel computer system and showed that the parallel computation could reduce the computation time from hours to minutes.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.513-516
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• 2002

In electrical impedance tomography(EIT), various image reconstruction algorithms have been used in order to compute the internal resistivity distribution of the unknown object with its electric potential data at the boundary. Mathematically the EIT image reconstruction algorithm is a nonlinear ill-posed inverse problem. This paper presents two intelligent optimization algorithm techniques such as genetic algorithm and simulated annealing for the solution of the static EIT inverse problem. We summarize the simulation results for the three algorithm forms: modified Newton-Raphson, genetic algorithm, and simulated annealing.