• Earthquakes and Structures
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• v.17 no.1
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• pp.17-29
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• 2019

The present study aims to present a comprehensive understanding of the performance of neighboring multi-story buildings with different dynamic characteristics under blast loads. Two different scenarios are simulated in terms of explosion locations with respect to both buildings. To investigate the effect of interaction between the neighboring buildings in terms of the induced responses, the separation gap is set to be sufficiently small to ensure collisions between stories. An adequately large separation gap is set between the buildings to explore responses without collisions under the applied blast loads. Several blast loads with different peak pressure intensities are employed to perform the dynamic analysis. The finite-element toolbox Computer Aided Learning of the Finite-Element Method (CALFEM) is used to develop a MATLAB code to perform the simulation analysis. The dynamic responses obtained in the scenarios considered herein are presented comparatively. It is found that the obtained stories' responses are governed mainly by the location and intensity of the applied blast loads, separation distances, and flexibility of the attacked structures. Moreover, explosions near a light and flexible building may lead to a significant decrease in blast resistance because explosions severely influence the dynamic responses of the building's stories.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

• Journal of the Korean Magnetic Resonance Society
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• v.23 no.3
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• pp.67-72
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• 2019

Caveolae are small plasma membrane invaginations that play many roles in signal transduction, endocytosis, mechanoprotection, lipid metabolism. The most important protein in caveolae is the integral membrane protein, caveolin, which is divided into three families such as caveolin 1, caveolin 2, and caveolin 3. Caveolin 1 and 3 are known to incorporate palmitate through linkage to three cysteine residues. Regulation of the protein palmitoylation cycle is important for the cellular processes such as intracellular localization of the target protein, membrane association, conformation, protein-protein interaction, and activity. However, the detailed aspect of individual palmitoylation has not been studied. In the present work, the role of each lipid modification at three cysteines was studied by NMR. Our results suggest that each lipid modification at the natively palmitoylation site has its own roles. For example, lipidations to C106 and C129 are play a role in structural stabilization, however, interestingly, lipid modification to C116 interrupts the structural stabilization.

• Proceedings of the KIPE Conference
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• 2018.11a
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• pp.101-103
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• 2018

This paper describes -40kV high voltage solid state pulse power modulator (SSPPM) for driving a magnetron, which is used as a RF power source of LINAC for cancer treatment systems. In case of the medical LINAC, small size and light weight are required. The SSPPM is 92 liters in size and weighs 50 kg. In this paper, S-band 2.6 MW magnetron load experiment is conducted and impedance matching was applied to obtain a smooth output current. Finally, the experimental results is discussed and the reliability of SSPPM is verified.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.130-136
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• 2021

In recent years, much study has been conducted in robotic grasping. The grasping algorithms based on deep learning have shown better grasping performance than the traditional ones. However, deep learning-based algorithms require a lot of data and time for training. In this study, a grasping algorithm using an artificial neural network-based graspability estimator is proposed. This graspability estimator can be trained with a small number of data by using a neural network based on the residual blocks and point clouds containing the shapes of objects, not RGB images containing various features. The trained graspability estimator can measures graspability of objects and choose the best one to grasp. It was experimentally shown that the proposed algorithm has a success rate of 90% and a cycle time of 12 sec for one grasp, which indicates that it is an efficient grasping algorithm.

• Computers and Concrete
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• v.30 no.4
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• pp.269-276
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• 2022

In this paper, frequency vibrations of double-walled carbon nanotubes (CNTs) has been investigated based upon nonlocal elastic theory. The inference of small scale is being perceived by establishing nonlocal Love shell model. The wave propagation approach has been operated to frame the governing equations as eigen value system. An innovational nonlocal model to examine the scale effect on vibrational behavior of armchair, zigzag and chiral of double-walled CNTs. An appropriate selection of material properties and nonlocal parameter has been considered. The influence of dimensionless nonlocal parameter has been studied in detail. The dominance of end condition via nonlocal parameter is explained graphically. The results generated furnish the evidence regarding applicability of nonlocal shell model and also verified by earlier published literature.

• Advances in nano research
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• v.13 no.1
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• pp.47-61
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• 2022

The present research investigates the dynamic behavior of a rotating functionally graded (FG) nonlocal cylindrical beam. The cylindrical beam is mathematically modeled via third-order beam theory linked with nonlocal strain gradient theory. The tube structure is made of functionally graded materials composed of Aluminum oxide coated on the Nickel, which the mechanical properties vary in the tube radius direction according to the power law. The bending harmonic force is applied in the tube length middle. The nonlocal spinning equations of the tube are derived via the energy method of the Hamilton principle, and they are solved via a robust numerical procedure for different boundary conditions. The main application of the rotating nanostructures is for the production of small-scale motors and devices and the drug-delivery application, the presented results can help the researcher have a better view regarding the different conditions.

• Steel and Composite Structures
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• v.42 no.3
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• pp.353-361
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• 2022

In this study, an estimation regarding nonlocal shell model based on wave propagation approach has been considered for vibrational behavior of the double walled carbon nanotubes with distinct nonlocal parameters. Vibrations of double walled carbon nanotubes for chiral indices (8, 3) have been analyzed. The significance of small scale is being perceived by developing nonlocal Love shell model. The influence of changing mechanical parameter Poisson's ratio has been investigated in detail. The dominance of boundary conditions via nonlocal parameter is shown graphically. It is found that on increasing the Poisson's ratio, the frequencies increases. It is noted that the frequencies of clamped-clamped frequencies are higher than that of simply-supported and clamped-free edge conditions. The outcomes of frequencies are tested with earlier computations.

• Computers and Concrete
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• v.30 no.2
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• pp.85-97
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• 2022

Using a combination of nonlocal Eringen as well as classical beam theories, this research explores the thermal buckling of a bidirectional functionally graded nanobeam. The formulations of the presented problem are acquired by means on conserved energy as well as nonlocal theory. The results are obtained via generalized differential quadrature method (GDQM). The mechanical properties of the generated material vary in both axial and lateral directions, two-dimensional functionally graded material (2D-FGM). In nanostructures, porosity gaps are seen as a flaw. Finally, the information gained is used to the creation of small-scale sensors, providing an outstanding overview of nanostructure production history.

• Structural Engineering and Mechanics
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• v.88 no.4
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• pp.355-368
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• 2023

Employing the non-local strain gradient theory (NSGT), this paper investigates the nonlinear resonance characteristics of functionally graded material (FGM) nanoshells with initial geometric imperfection for the first time. The effective material properties of the porous FGM nanoshells with even distribution of porosities are estimated by a modified power-law model. With the guidance of Love's thin shell theory and considering initial geometric imperfection, the strain equations of the shells are obtained. In order to characterize the small-scale effect of the nanoshells, the nonlocal parameter and strain gradient parameter are introduced. Subsequently, the Euler-Lagrange principle was used to derive the motion equations. Considering three boundary conditions, the Galerkin principle combined with the modified Lindstedt Poincare (MLP) method are employed to discretize and solve the motion equations. Finally, the effects of initial geometric imperfection, functional gradient index, strain gradient parameters, non-local parameters and porosity volume fraction on the nonlinear resonance of the porous FGM nanoshells are examined.