• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.5
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• pp.673-679
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• 2001

The objective of this work is to estimate the temperature dependent thermal properties of the APC-2 composite using a inverse parameter estimation technique. The present inverse method features the estimation of the thermal conductivity and the volumetric heat capacity, which are dependent on the temperature inside the composite. Furthermore, the thermal conductivity is directionally dependent because of the aniosotropy of the composite. An on-line temperature measurement system with a suitable method of heating is built. A composite slab is fabricated using thermoplastic prepreg for the investigation. The corresponding computer code for evaluating the thermal properties inversely using the temperature reading transmitted from the measurement system is developed. The parameterized form is used for the rapid and stable estimation. The modified Newtons method is adopted for the solution technique of the inverse analysis. The estimated results are compared with the measured data from a previous study for the verification.

• Bulletin of the Korean Mathematical Society
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• v.33 no.4
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• pp.603-611
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• 1996

Let consider the following problem: find an element u(t) in a Banach space U from the equation $$ x'(t) = Ax(t) + f(t,x(t)) + \Phi_0 u(t), 0 \leq t \leq T $$ with initial and terminal conditions $$ x(0) = 0, x(T) = \phi $$ in a Banach space X where $\phi \in D(A)$. This problem is a kind of control engineering inverse problem and contains nonlinear term, so that it is difficult and interesting. Thee proof main result in this paper is based on the Fredholm property of [1] in section 3. Similar considerations of linear system have been dealt with in many references. Among these literatures, Suzuki[5] introduced this problem for heat equation with unknown spatially-varing conductivity. Nakagiri and Yamamoto[2] considered the identifiability problem, which A is a unknown operator to be identified, where the system is described by a linear retarded functional differential equation. We can also apply to determining the magnitude of the control set for approximate controllability if X is a reflexive space, i.e., we can consider whether a dense subset of X is covered by reachable set in section 4.

• Communications of the Korean Mathematical Society
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• v.16 no.3
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• pp.459-485
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• 2001

The aim of EIT (electrical impedance tomography) system is to image cross-section conductivity distribution of a human body by means of both generating and sensing electrodes attached on to the surface of the body, where currents are injected and voltages are measured. 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 tissue conductivity values. With a limited set of current-to-voltage data, figuring out full structure of the conductivity distribution could be extremely difficult at present time, so it could be worthwhile to extract some necessary partial information of the internal conductivity. We try to extract some key patterns of current-to-voltage data that furnish some core information on the conductivity distribution such s location and size. This overview provides our recent observation on the location search and the size estimation.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.1-6
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• 2007

Two impedance imaging systems of multi-frequency electrical impedance tomography (MFEIT) and magnetic resonance electrical impedance tomography (MREIT) are described. MFEIT utilizes boundary measurements of current-voltage data at multiple frequencies to reconstruct cross-sectional images of a complex conductivity distribution (${\sigma}+i{\omega}{\varepsilon}$) inside the human body. The inverse problem in MFEIT is ill-posed due to the nonlinearity and low sensitivity between the boundary measurement and the complex conductivity. In MFEIT, we therefore focus on time- and frequency-difference imaging with a low spatial resolution and high temporal resolution. Multi-frequency time- and frequency-difference images in the frequency range of 10 Hz to 500 kHz are presented. In MREIT, we use an MRI scanner to measure an internal distribution of induced magnetic flux density subject to an injection current. This internal information enables us to reconstruct cross-sectional images of an internal conductivity distribution with a high spatial resolution. Conductivity image of a postmortem canine brain is presented and it shows a clear contrast between gray and white matters. Clinical applications for imaging the brain, breast, thorax, abdomen, and others are briefly discussed.

• Journal of the Korean Geotechnical Society
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• v.20 no.3
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• pp.47-51
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• 2004

An unsaturated hydraulic conductivity function and a soil-water characteristic curve are the essential constitutive factors in studying unsaturated soils. In order to obtain the unsaturated hydraulic conductivity function, prediction functions, which are based on the soil-water characteristic curve, have been used because it is difficult to measure the unsaturated hydraulic conductivity function directly. In this study, a parameter estimation method using the flow pump technique is introduced to determine the unsaturated hydraulic conductivity function. This method provides more accurate and independent solution than previous methods for the determination of the unsaturated hydraulic conductivity function which is not subordinate to the soil-water characteristic curve or prediction models.

• Water for future
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• v.29 no.5
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• pp.215-222
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• 1996

An expression for the cross covariance of the logconductivity and the head in nonstationary porous formation is obtained. This cross covariance plays a key role in the inverse problem, i.e., in inferring the statistical characteristics of the conductivity field from head data. The nonstationary logconductivity is modeled as superposition of definite linear trend and stationary fluctuation and the hydraulic head in saturated aquifers is found through stochastic analysis of a steady, two-dimensional flow. The cross covariance with a Gaussian correlation function is investigated for two particular cases where the trend is either parallel or normal to the head gradient. The results show that cross covariances are stationary except along separation distances parallel to the mean flow direction for the case where the trend is parallel to head gradient. Also, unlike the stationary model, the cross covariance along distances normal to flow direction is non-zero. From these observations we conclude that when a trend in the conductivity field is suspected, this information must be incorporated in the analysis of groundwater flow and solute transjport.

• Journal of IKEEE
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• v.19 no.1
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• pp.33-40
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• 2015

Inverse problem in electrical impedance tomography (EIT) is highly ill-posed therefore prior information is used to mitigate the ill-posedness. Regularization methods are often adopted in solving EIT inverse problem to have satisfactory reconstruction performance. In solving the EIT inverse problem, iterative Gauss-Newton method is generally used due to its accuracy and fast convergence. However, its performance is still suboptimal and mainly depends on the selection of regularization parameter. Although, there are few methods available to determine the regularization parameter such as L-curve method they are sometimes not applicable for all cases. Moreover, regularization parameter is a scalar and it is fixed during iteration process. Therefore, in this paper, a novel method is used to determine the regularization parameter to improve reconstruction performance. Conductivity norm is calculated at each iteration step and it used to obtain the regularization parameter which is a diagonal matrix in this case. The proposed method is applied to human thorax imaging and the reconstruction performance is compared with traditional methods. From numerical results, improved performance of proposed method is seen as compared to conventional methods.

• The KSFM Journal of Fluid Machinery
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• v.14 no.1
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• pp.34-41
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• 2011

Fire characteristics can be analyzed more realistically by using more accurate properties related to the fire dynamics and one way to acquire these fire properties is to use one of the inverse property estimation techniques. In this study two optimization algorithms which are frequently applied for the inverse heat transfer problems are selected to demonstrate the procedure of obtaining pyrolysis properties of charring material with relatively simple thermal decomposition. Thermal decomposition is occurred at the surface of the charring material heated by receiving the radiative energy from external heat sources and in this process the heat transfer through the charring material is simplified by an unsteady 1-dimensional problem. The basic genetic algorithm(GA) and repulsive particle swarm optimization(RPSO) algorithm are used to find the eight properties of a charring material; thermal conductivity(virgin, char), specific heat(virgin, char), char density, heat of pyrolysis, pre-exponential factor and activation energy by using the surface temperature and mass loss rate history data which are obtained from the calculated experiments. Results show that the RPSO algorithm has better performance in estimating the eight pyrolysis properties than the basic GA for problems considered in this study.

• 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.

• Steel and Composite Structures
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• v.42 no.4
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• pp.489-499
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• 2022

This paper proposes a numerical optimization method to design the mesoscale architecture of textile composite for simultaneously enhancing mechanical and thermal properties, which compete with each other making it difficult to design intuitively. The base cell of the periodic warp and fill yarn system is served as the design space, and optimal fibre yarn geometries are found by solving the optimization problem through the proposed method. With the help of homogenization method, analytical formulae for the effective material properties as functions of the geometry parameters of plain-woven textile composites were derived, and they are used to form the inverse homogenization method to establish the design problem. These modules are then put together to form a multiobjective optimization problem, which is formulated in such a way that the optimal design depends on the weight factors predetermined by the user based on the stiffness and thermal terms in the objective function. Numerical examples illustrate that the developed method can achieve reasonable designs in terms of fibre yarn paths and geometries.