• Bulletin of the Korean Chemical Society
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• v.7 no.3
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• pp.190-193
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• 1986

The structure of partially $Ag^+$-exchanged zeolite 4A, $Ag_{7.6}Na_{4.4}-A$, vacuum dehydrated at $370^{\circ}C$, has been determined by single-crystal x-ray diffraction techniques in the cubic space group, Pm3m (a = 12.311(1)${\AA}$) at $24(1)^{\circ}}C$. The structure was refined to the final error indices $R_1$ = $R_2$ (weighted) = 0.064 using 266 independent reflections for which $I_0$>$3{\sigma}(I_0)$. Three $Na^+$ ions occupy the 3 8-ring sites, and the remaining ions, 1.4 $Na^+$ and 6.6 $Ag^+$, fill the 8 6-ring sites; each $Ag^+$ ion is nearly in the [111] plane of its 3 O(3) ligands, and each $Na^+$ ion is 0.9${\AA}$ from its corresponding plane, on the large-cavity side. One reduced silver atom per unit cell was found inside the sodalite unit. It was presumably formed from the reduction of a $Ag^+$ ion by an oxide ion of a residual water molecule or of the zeolite framework. It may be present as a hexasilver cluster in 1/6 of the sodalite units, or, most attractively among several alternatives, as an isolated Ag atom coordinated to 4 Ag ions in each sodalite unit to give $(Ag_5)^{4+}$, symmetry 4mm.

• The Journal of Korean Institute of Information Technology
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• v.17 no.10
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• pp.57-63
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• 2019

Motion errors which are caused by several reasons, non-ideal path, errors of navigation systems, and radar system errors, have to be corrected. Motion compensation methods can compensate the motion error, but not exactly. To correct these residual errors, several autofocus methods are invented. A popular method is phase gradient autofocus (PGA). PGA does not assume specific circumstances, such as isolated point targets and shapes of errors. PGA is an iterative and adaptive method, so that the processing time is the main problem for the real time processing. In this paper, the improved method to select targets for PGA is proposed to reduce processing time. The variances of image pixels are used to select targets with high SNR. The processing of PGA with these targets diminishes the processing time and iterations effectively. The processed results with real radar data, obtained by flight tests, show that the proposed method compensates errors well, and reduce working time.

• Composites Research
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• v.34 no.6
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• pp.337-344
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• 2021

In this study, the progressive failure behavior of the composite fan blade dovetail element under tensile loading is numerically investigated through finite element(FE) simulation. The accuracy of prediction by FE simulation is verified through tensile testing. The dovetail element is one of the joints for coupling the fan blade with the disk in a turbofan engine. The dovetail element is usually made of a metal material such as titanium, but the application of composite material is being studied for weight reduction reasons. However, manufacturing defects such as drop-off ply and resin pocket inevitably occur in realizing complex shapes of the fan blade made by composite materials. To investigate the effect of these manufacturing defects on the composite fan blade dovetail element, we performed numerical simulation with FE model to compare the prediction of the FE model and the tensile test results. At this time, the cohesive zone model is used to simulate the delamination behavior. Finally, we found that FE simulation results agree with test results when considering thermal residual stress and through-thickness compression enhancement effect.

• Tunnel and Underground Space
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• v.32 no.1
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• pp.1-19
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• 2022

In this report, new standard, published by Japanese Geotechnical Society, on in-situ stress measurements by hydraulic fracturing was reviewed. In the standard, modification was made for the calculation of fracture re-opening pressure in consideration of fracture surface roughness and residual aperture. The standard also presents how much the system compliance influences the estimation of the fracture re-opening pressure and subsequent in-situ stresses. It is shown that the stiffer the rock mass is, the system compliance should be sufficiently small enough so as to obtain in-situ stress measurement with higher confidence.

• Advances in aircraft and spacecraft science
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• v.8 no.4
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• pp.345-372
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• 2021

The analysis of nonlinear vibrations, buckling, post-buckling, flutter boundary determination and post-flutter behavior of a homogeneous curved plate assuming cylindrical bending is conducted in this article. Other assumptions include simply-supported boundary conditions, supersonic aerodynamic flow at the top of the plate, constant pressure conditions below the plate, non-viscous flow model (using first- and third-order piston theory), nonlinear structural model with large deformations, and application of mechanical and thermal loads on the curved plate. The analysis is performed with constant environmental indicators (flow density, heat, Reynolds number and Mach number). The material properties (i.e., coefficient of thermal expansion and modulus of elasticity) are temperature-dependent. The equations are derived using the principle of virtual displacement. Furthermore, based on the definitions of virtual work, the potential and kinetic energy of the final relations in the integral form, and the governing nonlinear differential equations are obtained after fractional integration. This problem is solved using two approaches. The frequency analysis and flutter are studied in the first approach by transferring the handle of ordinary differential equations to the state space, calculating the system Jacobin matrix and analyzing the eigenvalue to determine the instability conditions. The second approach discusses the nonlinear frequency analysis and nonlinear flutter using the semi-analytical solution of governing differential equations based on the weighted residual method. The partial differential equations are converted to ordinary differential equations, after which they are solved based on the Runge-Kutta fourth- and fifth-order methods. The comparison between the results of frequency and flutter analysis of curved plate is linearly and nonlinearly performed for the first time. The results show that the plate curvature has a profound impact on the instability boundary of the plate under supersonic aerodynamic loading. The flutter boundary decreases with growing thermal load and increases with growing curvature.

• Advances in nano research
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• v.12 no.5
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• pp.529-547
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• 2022

Graphene is one of the strongest, stiffest, and lightest nanoscale materials known to date, making it a potentially viable and attractive candidate for developing lightweight structural composites to prevent high-velocity ballistic impact, as commonly encountered in defense and space sectors. In-plane twist in bilayer graphene has recently revealed unprecedented electronic properties like superconductivity, which has now started attracting the attention for other multi-physical properties of such twisted structures. For example, the latest studies show that twisting can enhance the strength and stiffness of graphene by many folds, which in turn creates a strong rationale for their prospective exploitation in high-velocity impact. The present article investigates the ballistic performance of twisted bilayer graphene (tBLG) nanostructures. We have employed molecular dynamics (MD) simulations, augmented further by coupling gaussian process-based machine learning, for the nanoscale characterization of various tBLG structures with varying relative rotation angle (RRA). Spherical diamond impactors (with a diameter of 25Å) are enforced with high initial velocity (V_i) in the range of 1 km/s to 6.5 km/s to observe the ballistic performance of tBLG nanostructures. The specific penetration energy (E_p^*) of the impacted nanostructures and residual velocity (V_r) of the impactor are considered as the quantities of interest, wherein the effect of stochastic system parameters is computationally captured based on an efficient Gaussian process regression (GPR) based Monte Carlo simulation approach. A data-driven sensitivity analysis is carried out to quantify the relative importance of different critical system parameters. As an integral part of this study, we have deterministically investigated the resonant behaviour of graphene nanostructures, wherein the high-velocity impact is used as the initial actuation mechanism. The comprehensive dynamic investigation of bilayer graphene under the ballistic impact, as presented in this paper including the effect of twisting and random disorder for their prospective exploitation, would lead to the development of improved impact-resistant lightweight materials.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1477-1485
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• 2021

Recently, super-resolution has been intensively studied only on upscaling models with integer magnification. However, the need to expand arbitrary magnification is emerging in representative application fields of actual super-resolution, such as object recognition and display image quality improvement. In this paper, we propose a model that can support arbitrary magnification by using the weights of the existing integer magnification model. This model converts super-resolution results into the DCT spectral domain to expand the space for arbitrary magnification. To reduce the loss of high-frequency information in the image caused by the expansion by the DCT spectral domain, we propose a high-frequency attention network for arbitrary magnification so that this model can properly restore high-frequency spectral information. To recover high-frequency information properly, the proposed network utilizes channel attention layers. This layer can learn correlations between RGB channels, and it can deepen the model through residual structures.

• Korean Journal of Radiology
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• v.22 no.9
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• pp.1481-1489
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• 2021

Objective: To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer. Materials and Methods: This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman's correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation. Results: A total of 157 patients (median age, 56 years; range, 27-79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62-0.82). Conclusion: Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.4
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• pp.401-417
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• 2020

As an increasing demand for rapid railway transportation, the construction of sidetrack is inevitable to operate local and express trains simultaneously. However, the current technologies for the sidetrack construction method require a long construction period by interrupting the operation of the existing subway line, as well as cause a huge economic loss. Thus, it is necessary to study the sidetrack construction method under the special situation that the subway is in operation and to analyze the risk of the existing tunnel enlargement process for the sidetrack construction. Therefore, in this paper, the Government Complex Gwacheon station on Subway Line 4 was considered as a target station for the virtual sidetrack construction and the optimal sidetrack construction plan was derived. Subsequently, the application of risk management process was carried out in the order of identifying risk, risk response planning, performing a risk analysis, risk monitoring and control for potential risk events during the construction of sidetrack under the subway operation. A total of eight potential risk events and risk mitigation methods were selected, and a risk assessment matrix was established using the five-step risk probability and impact level criteria to perform the risk assessment including residual risks. Based on the results of the risk assessment, the risk grade and the reduction effect of each risk mitigation method were confirmed.

• Tunnel and Underground Space
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• v.30 no.5
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• pp.462-472
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• 2020

Rock classification is fundamental discipline of exploring geological and geotechnical features in a site, which, however, may not be easy works because of high diversity of rock shape and color according to its origin, geological history and so on. With the great success of convolutional neural networks (CNN) in many different image-based classification tasks, there has been increasing interest in taking advantage of CNN to classify geological material. In this study, a feasibility of the deep CNN is investigated for automatically and accurately identifying rock types, focusing on the condition of various shapes and colors even in the same rock type. It can be further developed to a mobile application for assisting geologist in classifying rocks in fieldwork. The structure of CNN model used in this study is based on a deep residual neural network (ResNet), which is an ultra-deep CNN using in object detection and classification. The proposed CNN was trained on 10 typical rock types with an overall accuracy of 84% on the test set. The result demonstrates that the proposed approach is not only able to classify rock type using images, but also represents an improvement as taking highly diverse rock image dataset as input.