• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3808-3815
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• 2021

In this paper, the EPICS2017 photoatomic database was used to evaluate the photon mass attenuation coefficients and buildup factors of soils collected at different depths in the Philippine islands. The extraction and interpolation of the library was accomplished at the recommended linear-linear scales to obtain the incoherent and total cross section and mass attenuation coefficient. The buildup factors were evaluated using the G-P fitting method in ANSI/ANS-6.4.3. An agreement was achieved between XCOM, MCNP5, and EPICS2017 for the calculated mass attenuation coefficient values. The buildup factors were reported at several penetration depths within the standard energy grid. The highest values of both buildup factor classifications were found in the energy range between 100 and 400 keV where incoherent scattering interaction probabilities are predominant, and least at the region of predominant photoionization events. The buildup factors were examined as a function of different soil silica contents. The soil samples with larger silica concentrations were found to have higher buildup factor values and hence lower shielding characteristics, while conversely, those with the least silica contents have increased shielding characteristics brought by the increased proportions of the abundant heavier oxides.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.305-316
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• 2022

Numerous research revealed a strong association between the ionospheric perturbations and various natural hazards. The ionospheric measurements from Global Navigation Satellite System (GNSS) observations provide the state of electron contents in the ionosphere that contributes to investigate the source events. In this study, two geophysical events occurred on 23 January 2018, the 7.9 Mw earthquake in Alaska and Kusatsu-Shiranesan volcanic eruption in Japan, are examined to characterize the fingerprint of each event in the ionosphere. Firstly, we extracted the Total Electron Content (TEC) from GNSS measurements, then isolated disturbed wave signatures from the TEC measurements that is referred to as a traveling ionospheric disturbance (TID). As TIDs are short-term ionospheric variations, the major trend of GNSS TEC measurements should be properly removed. We applied a natural neighbor interpolation method together with a leave-one-out cross validation technique for detrending. After detrending the TEC, the remaining signals are further enhanced by applying a band-pass filter and TIDs are detected from them. Finally, the detected TIDs are verified as the response of the ionosphere to Kusatsu-Shiranesan volcanic eruption and Gulf of Alaska earthquake which propagated through the ionosphere with an average velocity of 530 m/s and 724 m/s, respectively. In addition, a coherence analysis is conducted to discriminate between the signatures from a volcanic explosion and an earthquake. The analysis reveals the TID waveforms from each single event are highly correlated, while a low correlation is found between the TIDs from the earthquake and explosion. This study supports the claim that different geophysical events induce the distinctive characteristics of TIDs that are detectable by the ionospheric measurements of GNSS.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.407-409
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• 2021

Under the influence of the 4th industrial revolution, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. Digital images may generate noise due to various reasons, and may affect various systems such as image recognition and classification and object tracking. To compensate for these shortcomings, we propose an image restoration algorithm based on pattern information of non-noise pixels. According to the distribution of non-noise pixels inside the filtering mask, the proposed algorithm switched the filtering process by dividing the interpolation method into a pattern that can be applied, a pattern based on region division, and a randomly arranged pixel pattern. preserves and restores the image. The proposed algorithm showed superior performance compared to the existing impulse noise removal algorithm.

• Steel and Composite Structures
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• v.50 no.5
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• pp.563-581
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• 2024

This paper presents a novel finite element model for the free vibration analysis of variable-thickness functionally graded porous (FGP) microplates resting on Pasternak's medium in the hygro-thermal environment. The governing equations are established according to refined higher-order shear deformation plate theory (RPT) in construction with the modified couple stress theory. For the first time, three-node triangular elements with twelve degrees of freedom for each node are developed based on Hermitian interpolation functions to describe the in-plane displacements and transverse displacements of microplates. Two laws of variable thickness of FGP microplates, including the linear law and the nonlinear law in the x-direction are investigated. Effects of thermal and moisture changes on microplates are assumed to vary continuously from the bottom surface to the top surface and only cause tension loads in the plane, which does not change the material's mechanical properties. The numerical results of this work are compared with those of published data to verify the accuracy and reliability of the proposed method. In addition, the parameter study is conducted to explore the effects of geometrical and material properties such as the changing law of the thickness, length-scale parameter, and the parameters of the porosity, temperature, and humidity on the free vibration response of variable thickness FGP microplates. These results can be applied to design of microelectromechanical structures in practice.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.4D
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• pp.407-419
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• 2012

It is difficult to obtain the accurate and homogeneous height information over the whole Korea due to the effect of different vertical datums have been divided into land and sea part. In this study, we tried to unify the different vertical datums using the precise spirit leveling between TBM (tidal bench mark) and BM (bench mark) in order to solve the problems caused by different vertical datums. For this, the vertical datum offsets at observed points which were calculate from leveling results and then transformation model of vertical datum will be modelled using calculated offsets along the coastal line. For suggesting the precise modelling method to vertical datum transformation, we analyzed results from various interpolation methods such as Spline and LSC method. As the results from analysis, the LSC method combined with 4-parameters trend model is more suitable for modelling the offsets between vertical datums. The final transformation model of vertical datum using the combination of LSC and 4-parameter model which provides the transformation accuracies of ${\pm}10.4cm{\sim}14.8cm$ level. And, the software for vertical datum transformation that was also developed using the final model in order to convert the height information included in various spatial data effectively. Therefore, the transformation model between vertical datums of land and sea part, which is developed in this study, is expected to minimize the confusion caused by mismatch of height information in the use of spatial data, and it also can be minimize economic and time losses in various application fields such as coastal development project, coastal disaster prevention, etc.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.1
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• pp.21-30
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• 2004

This study deals with free vibrations of laminated composite structures with a channel section using finite element method. In this paper, the mixed finite element method using Lagrangian and Hermite interpolation functions is adopted and a high-order plate theory is used to analyze laminated composite non-prismatic folded plates with a channel section more accurately for free vibration. The theory accounts for parabolic distribution of the transverse shear stress and requires no shear correction factors supposed in the first-order plate theory. An 32×32 matrix is assembled to transform the system element matrices from the local to global coordinates using a coordinate transformation matrix, in which an eighth drilling degree of freedom (DOF) per node is appended to the existing 7-DOF system. The results in this study are compared with those of available literatures for the conventional and first-order plate theory. Sample studies are carried out for various layup configurations and length-thickness ratio, and geometric shapes of plates. The significance of the high-order plate theory in analyzing complex composite structures with a channel section is enunciated in this paper.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.273-281
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• 2010

In this paper, we propose an automatic segmentation method of the endo- and epicardial boundary by using ray-casting profile based on intensity distribution and gradient information in CT images. First, endo-cardial boundary points are detected by using adaptive thresholding and seeded region growing. To include papillary muscles inside the boundary, the endo-cardial boundary points are refined by using ray-casting based profile. Second, epi-cardial boundary points which have both a myocardial intensity value and a maximum gradient are detected by using ray-casting based adaptive gradient profile. Finally, to preserve an elliptical or circular shape, the endo- and epi-cardial boundary points are refined by using elliptical interpolation and B-spline curve fitting. Then, curvature-based contour fitting is performed to overcome problems associated with heterogeneity of the myocardium intensity and lack of clear delineation between myocardium and adjacent anatomic structures. To evaluate our method, we performed visual inspection, accuracy and processing time. For accuracy evaluation, average distance difference and overalpping region ratio between automatic segmentation and manual segmentation are calculated. Experimental results show that the average distnace difference was $0.56{\pm}0.24mm$. The overlapping region ratio was $82{\pm}4.2%$ on average. In all experimental datasets, the whole process of our method was finished within 1 second.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.16-22
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• 2012

In this paper, we proposed a method of baseline correction for analysis of Raman spectra of platelets from Alzheimer's disease (AD) transgenic mice. Measured Raman spectra include the meaningful information and unnecessary noise which is composed of baseline and additive noise. The Raman spectrum is divided into the local region including several peaks and the spectrum of the region is modeled by curve fitting using Gaussian model. The additive noise is clearly removed from the process of replacing the original spectrum with the fitted model. The baseline correction after interpolating the local minima of the fitted model with linear, piecewise cubic Hermite and cubic spline algorithm. The baseline corrected models extract the feature with principal component analysis (PCA). The classification result of support vector machine (SVM) and maximum $a$ posteriori probability (MAP) using linear interpolation method showed the good performance about overall number of principal components, especially SVM gave the best performance which is about 97.3% true classification average rate in case of piecewise cubic Hermite algorithm and 5 principal components. In addition, it confirmed that the proposed baseline correction method compared with the previous research result could be effectively applied in the analysis of the Raman spectra of platelet.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.18-23
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• 2008

In this paper, we propose the input image selection-method to improve the reconstructed high-resolution (HR) image quality. To obtain ideal super-resolution (SR) reconstruction image, all input images are well-registered. However, the registration is not ideal in practice. Due to this reason, the selection of input images with low registration error (RE) is more important than the number of input images in order to obtain good quality of a HR image. The suitability of a candidate input image can be determined by using statistical and restricted registration properties. Therefore, we propose the proper candidate input Low Resolution(LR) image selection-method as a pre-processing for the SR reconstruction in automatic manner. In video sequences, all input images in specified region are allowed to use SR reconstruction as low-resolution input image and/or the reference image. The candidacy of an input LR image is decided by the threshold value and this threshold is calculated by using the maximum motion compensation error (MMCE) of the reference image. If the motion compensation error (MCE) of LR input image is in the range of 0 < MCE < MMCE then this LR input image is selected for SR reconstruction, else then LR input image are neglected. The optimal reference LR (ORLR) image is decided by comparing the number of the selected LR input (SLRI) images with each reference LR input (RLRI) image. Finally, we generate a HR image by using optimal reference LR image and selected LR images and by using the Hardie's interpolation method. This proposed algorithm is expected to improve the quality of SR without any user intervention.