• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1262-1268
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• 2014

Inverse problem is very interest in the sciences and engineering, in particular for modeling and monitoring applications. By applying inverse problem, it can be widely used to exploration of mineral resources, identification of underground cables and buried pipelines, and diagnostic imaging in medical area. In this paper, we firstly consider 2-dimensional EM scattering problem and present the FDTD method to estimate unknown source. In this case, non-linear CGM technique is used to investigate unknown sources corresponding to measured data obtained from forward problem in near field. The proposed technique for solving the inverse source problem presents a reasonable agreement and can be applied to investigate an internal source signal of embedded security module.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.9
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• pp.431-438
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• 2001

This paper present experimental results of source identification for non-minimum phase system. Generally, a causal linear system may be described by matrix form. The inverse problem is considered as a matrix inversion. Direct inverse method can\`t be applied for a non-minimum phase system, the reason is that the system has ill-conditioning. Therefore, in this study to execute an effective inversion, SVD inverse technique is introduced. In a Non-minimum phase system, its system matrix may be singular or near-singular and has one more very small singular values. These very small singular values have information about a phase of the system and ill-conditioning. Using this property we could solve the ill-conditioned problem of the system and then verified it for the practical system(cantilever beam). The experimental results show that SVD inverse technique works well for non-minimum phase system.

• Structural Engineering and Mechanics
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• v.14 no.2
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• pp.151-170
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• 2002

Moving force identification is a very important inverse problem in structural dynamics. Most of the identification methods are eventually converted to a linear algebraic equation set. Different ways to solve the equation set may lead to solutions with completely different levels of accuracy. Based on the measured bending moment responses of the bridge made in laboratory, this paper presented the time domain method (TDM) and frequency-time domain method (FTDM) for identifying the two moving wheel loads of a vehicle moving across a bridge. Directly calculating pseudo-inverse (PI) matrix and using the singular value decomposition (SVD) technique are adopted as means for solving the over-determined system equation in the TDM and FTDM. The effects of bridge and vehicle parameters on the TDM and FTDM are also investigated. Assessment results show that the SVD technique can effectively improve identification accuracy when using the TDM and FTDM, particularly in the case of the FTDM. This improved accuracy makes the TDM and FTDM more feasible and acceptable as methods for moving force identification.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2642-2649
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• 2023

A real-time unmeasured dynamic response prediction process for the nuclear power plant pressure pipeline is proposed and its performance is tested in the test-loop system (KAERI). The aim of the process is to predict unmeasurable or unreachable dynamic responses such as acceleration, velocity, and displacement by using a limited amount of directly measured physical responses. It is achieved by combining a well-constructed finite element model and robust inverse force identification algorithm. The pressure pipeline system is described by using the displacement-pressure vibro-acoustic formulation to consider fully filled liquid effect inside the pipeline structure. A robust multiphysics modal projection technique is employed for the real-time sensor synchronized prediction. The inverse force identification method is also derived and employed by using Bathe's time integration method to identify the full-field responses of the target system from the modal domain computation. To validate the performance of the proposed process, an experimental test is extensively performed on the nuclear power plant pressure pipeline test-loop under operation conditions. The results show that the proposed identification process could well estimate the unmeasured acceleration in both frequency and time domain faster than 32,768 samples per sec.

• Journal of Mechanical Science and Technology
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• v.16 no.4
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• pp.454-467
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• 2002

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. Toidentifythelocation and depth of a crack in a structure, a method is presented in this paper which uses neuro-fuzzy-evolutionary technique, that is, Adaptive-Network-based Fuzzy Inference System (ANFIS) solved via hybrid learning algorithm (the back-propagation gradient descent and the least-squares method) and Continuous Evolutionary Algorithms (CEAs) solving sir ale objective optimization problems with a continuous function and continuous search space efficiently are unified. With this ANFIS and CEAs, it is possible to formulate the inverse problem. ANFIS is used to obtain the input(the location and depth of a crack) - output(the structural Eigenfrequencies) relation of the structural system. CEAs are used to identify the crack location and depth by minimizing the difference from the measured frequencies. We have tried this new idea on beam structures and the results are promising.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1704-1709
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• 2000

This paper proposes a spherical probe allowing acoustic intensity measurements in three dimensions to be made, which creates a diffracted field that is well-defined, thanks to analytic solution of diffraction phenomena. Six microphones are distributed on the surface of the sphere along three rectangular axes. Its measurement technique is not based on finite difference approximation, as is the case for the ID probe but on the analytic solution of diffraction phenomena. In fact, the success of sound source identification depends on the inverse models used to estimate inverse diffraction phenomena, which has non-linear properties. In this paper, we introduce the concept of nonlinear inverse diffraction modeling using a neural network and the idea of 3 dimensional sound source identification with several tests.

• Structural Engineering and Mechanics
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• v.39 no.6
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• pp.751-765
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• 2011

The present paper deals with the identification of a concentrated damage in an elastic parabolic arch through the minimization of an objective function which measures the differences between numerical and experimental values of static displacements. The damage consists in a notch that reduces the height of the cross section at a given abscissa and therefore causes a variation in the flexural stiffness of the structure. The analytical values of static displacements due to applied loads are calculated by means of the principle of virtual work for both the undamaged and damaged arch. First, pseudo-experimental data are used to study the inverse problem and investigate whether a unique solution can occur or not. Various damage intensities are considered to assess the reliability of the identification procedure. Then, the identification procedure is applied to an experimental case, where displacements are measured on a prototype arch. The identified values of damage parameters, i.e., location and intensity, are compared to those obtained by means of a dynamic identification technique performed on the same structure.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1175-1182
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• 2000

Diffraction systematically causes error in acoustic measurements. Most probes are designed to reduce this phenomenon. On the contrary, this paper proposes a spherical probe a] lowing acoustic inten sity measurements in three dimensions to be made, which creates a diffracted field that is well-defined, thanks to analytic solution of diffraction phenomena. Six microphones are distributed on the surface of the sphere along three rectangular axes. Its measurement technique is not based on finite difference approximation, as is the case for the ID probe but on the analytic solution of diffraction phenomena. In fact, the success of sound source identification depends on the inverse models used to estimate inverse diffraction phenomena, which has nonlinear properties. In this paper, we propose the concept of nonlinear inverse diffraction modeling using a neural network and the idea of 3 dimensional sound source identification with better performances. A number of computer simulations are carried out in order to demonstrate the diffraction phenomena under various angles. Simulations for the inverse modeling of diffraction phenomena have been successfully conducted in showing the superiority of the neural network.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.9
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• pp.1806-1813
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• 2002

It is well known that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a classical optimization technique was adopted by previous researchers. That technique overcame the difficulty of finding the intersection point of the superposed contours that correspond to the eigenfrequency caused by the crack presence. However, it is hard to select a trial solution initially for optimization because the defined objective function is heavily multimodal. A method is presented in this paper, which uses continuous evolutionary algorithms(CEAs). CEAs are effective for solving inverse problems and implemented on PC clusters to shorten calculation time. With finite element model of the structure to calculate eigenfrequencies, it is possible to formulate the inverse problem in optimization format. CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising with high parallel efficiency over about 94%.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.2062-2069
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.