• ETRI Journal
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• v.41 no.1
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• pp.42-51
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• 2019

The rainbow hologram provides views of reconstruction with rainbow color within a large viewing zone. In our recent paper, a Fourier rainbow holographic display using diffraction grating and a white-light LED source was introduced. In this technique, the rainbow effect is realized by the dispersion of white-light source on diffraction grating, while the slit is implemented numerically by reducing the demands of the space-bandwidth product of the display. This paper presents a novel analysis on the visual perception of the Fourier rainbow holographic display using Wigner distribution. The view-dependent appearance of the image, including multispectral field of view and viewing zone, is investigated considering the observer and the display parameters. The resolution of the holographic view is also investigated. For this, a new quantitative assessment for image blur is introduced using Wigner distribution analysis. The analysis is supported with numerical simulations and experimentally captured optical reconstructions for the holograms of the computer model and real object generated with different slit size, reconstruction distance, and different observation conditions.

• Current Optics and Photonics
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• v.6 no.1
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• pp.1-9
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• 2022

This paper proposes a robust, simple zonal wavefront-estimation method in a grid sampling model. More slopes are added to the integral equation of the algorithm to improve the accuracy and convergence rate of this approach, especially for higher-order optical aberrations. The Taylor theorem is applied to clarify the mathematical description of the remaining error in the proposed method. Several numerical simulations are conducted to ensure the performance and improvement in comparison to the Southwell and previous algorithm. An experiment is also conducted according to deflectometry output and the results are verified using a reference measured with a stylus system.

• Journal of IKEEE
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• v.20 no.2
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• pp.152-162
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• 2016

Electrical resistivity tomography (ERT) is a technique to reconstruct the internal resistivity distribution using the measured voltages on the surface electrodes. ERT inverse problem suffers from ill-posedness nature, so regularization methods are used to mitigate ill-posedness. The reconstruction performance varies depending on the type of regularization method. In this paper, an interacting dual-mode regularization method is proposed with two different regularization methods, L1-norm regularization and total variation (TV) regularization, to achieve robust reconstruction performance. The interacting dual-mode regularization method selects the suitable regularization method and combines the regularization methods based on computed mode probabilities depending on the actual conditions. The proposed method is tested with numerical simulations and the results demonstrate an improved reconstruction performance.

• Wind and Structures
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• v.18 no.6
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• pp.693-715
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• 2014

Stochastic processes are used to represent phenomena in many diverse fields. Numerical simulation method is widely applied for the solution to stochastic problems of complex structures when alternative analytical methods are not applicable. In some practical applications the stochastic processes show non-Gaussian properties. When the stochastic processes deviate significantly from Gaussian, techniques for their accurate simulation must be available. The various existing simulation methods of non-Gaussian stochastic processes generally can only simulate super-Gaussian stochastic processes with the high-peak characteristics. And these methodologies are usually complicated and time consuming, not sufficiently intuitive. By revealing the inherent coupling effect of the phase and amplitude part of discrete Fourier representation of random time series on the non-Gaussian features (such as skewness and kurtosis) through theoretical analysis and simulation experiments, this paper presents a novel approach for the simulation of non-Gaussian stochastic processes with the prescribed amplitude probability density function (PDF) and power spectral density (PSD) by amplitude modulation and phase reconstruction. As compared to previous spectral representation method using phase modulation to obtain a non-Gaussian amplitude distribution, this non-Gaussian phase reconstruction strategy is more straightforward and efficient, capable of simulating both super-Gaussian and sub-Gaussian stochastic processes. Another attractive feature of the method is that the whole process can be implemented efficiently using the Fast Fourier Transform. Cases studies demonstrate the efficiency and accuracy of the proposed algorithm.

• 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 Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.419-425
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• 2004

This paper presents a new method which is calculated to use only three vanishing points in order to compute the dimensions of object and its pose from a single image of perspective projection taken by a camera. Our approach is to only compute three vanishing points without informations such as the focal length and rotation matrix from images in the case of perspective projection. We assume that the object can be modeled as a linear function of a dimension vector v. The input of reconstruction is a set of correspondences between features in the model and features in the image. To minimize each the dimensions of the parameterized models, this reconstruction of optimization can be solved by standard nonlinear optimization techniques with a multi-start method which generates multiple starting points for the optimizer by sampling the parameter space uniformly.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.973-988
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• 2009

The objective of this study is to develop an accurate and robust two-dimensional finite element method to simulate drying and wetting in complex natural rivers. The Dry/Wet algorithm developed in this study is Grid Reconstruction Method which requires the definition of new meshes and renumbering of the nodes. Any drop of water level in the two-dimensional domain results in eliminating nodes and elements. Several numerical simulations were carried out to examine the performance of the Grid Reconstruction Method for the purpose of validation and verification of the model. The accuracy and application of the developed model are verified by comparing simulated results with observed values.

• Journal of IKEEE
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• v.19 no.2
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• pp.219-224
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• 2015

Electrical impedance tomography is an imaging technique to reconstruct the internal conductivity distribution based on applied currents and measured voltages in a domain of interest. In this paper, a modified Gauss-Newton method is proposed for conductivity image reconstruction. In the proposed method, the dimension of the inverse term is reduced by replacing the number of elements with the number of measurement data in the conductivity updating equation of the conventional Gauss-Newton method. Therefore, the computation time is greatly reduced as compared to the conventional Gauss-Newton method. Moreover, the regularization parameter is selected by computing the minimum-maximum from the diagonal components of the Jacobian matrix at every iteration. The numerical experiments with several scenarios were carried out to evaluate the reconstruction performance of the proposed method.

• Medical Lasers
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• v.9 no.1
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• pp.39-43
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• 2020

Background and Objectives Breast reconstruction has the advantage of reducing the loss of the body image of patients after mastectomy surgery, and also improving the quality of the social and sexual life of breast cancer patients. However, in surprising and unfortunate number of patients, acute postoperative pain persists beyond the normal course of postsurgical recovery. We set out to investigate the effect of local dynamical micro-massage (LDM) treatment for achieving pain relief and reducing other postoperative complications. Materials and Methods We performed a retrospective analysis on 58 patients who underwent LDM treatment for postoperative pain management at Soonchunhyang University Bucheon Hospital between February 2017 and June 2019. Those patients who complained of persistent postoperative pain, which was uncontrollable with medication, were treated with LDM. The degree of pain and discomfort with contracture were recorded using numerical rating scale (NRS) scoring system with numbers from 0 to 10 ('none' to 'worst'). Results The median NRS score of pain was reduced by 62.3% from the start to the end of LDM treatment (p<0.001). Further, the NRS score of discomfort with contracture was reduced 66.0% (p<0.001). There was no complication related to the LDM treatment. Conclusion Dual-frequency ultrasound LDM can be an effective therapeutic option for persisting pain after breast reconstruction surgery. It was also effective in improvement of discomfort with contracture and erythema of the surgical wound.

• Smart Structures and Systems
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• v.30 no.6
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.