• Journal of Ginseng Research
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• v.41 no.4
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• pp.566-571
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• 2017

Background: A number of reports have described the protective effects of ginsenoside Rg1 (Rg1) in Alzheimer's disease (AD). However, the protective mechanisms of Rg1 in AD remain elusive. Methods: To investigate the potential mechanisms of Rg1 in ${\beta}$-amyloid peptide-treated SH-SY5Y cells, a comparative proteomic analysis was performed using stable isotope labeling with amino acids in cell culture combined with nano-LC-MS/MS. Results: We identified a total of 1,149 proteins in three independent experiments. Forty-nine proteins were significantly altered by Rg1 after exposure of the cells to ${\beta}$-amyloid peptides. The protein interaction network analysis showed that these altered proteins were clustered in ribosomal proteins, mitochondria, the actin cytoskeleton, and splicing proteins. Among these proteins, mitochondrial proteins containing HSD17B10, AARS2, TOMM40, VDAC1, COX5A, and NDUFA4 were associated with mitochondrial dysfunction in the pathogenesis of AD. Conclusion: Our results suggest that mitochondrial proteins may be related to the protective mechanisms of Rg1 in AD.

• Journal of Electrochemical Science and Technology
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• v.1 no.1
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• pp.10-18
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• 2010

Controlled electrodeposition of dendritic nano-structured gold-platinum (AuPt) alloy onto an electrochemically pretreated carbon paper substrate was conducted in an attempt to improve catalyst utilization and to secure an electronic percolation network toward formic acid (FA) fuel cell application. The AuPt catalysts were obtained by potentiostatic deposition. AuPt catalysts synthesized as bimetallic alloys with 60% Au content exhibited the highest catalytic activity towards formic acid electro-oxidation. The origin of this high activity and the role of Au were evaluated, in particular, by XPS analysis. Polarization and stability measurements with 1 mg $cm^{-2}$ AuPt catalyst (only 0.4 mg $cm^{-2}$ Pt) showed 52 mW $cm^{-2}$ and sustainable performance using 3M formic acid and dry air at $40^{\circ}C$.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.6
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• pp.169-174
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• 2012

Electromagnetic interference(EMI) shielding effectiveness(SE) was investigated in of woven fabric made of epoxy/copper-nickel fabrics and nickel coated carbon fiber reinforced acrylonitrile-butadiene-styrene(ABS) composites. The coaxial transmission line method was used to measure the EMI shielding effectiveness of the composites. We designed and constructed a measuring system, consisting of a network analyzer and a device that plays the serves as a sample holder and at the same time as a transmission medium of the incident electromagnetic wave. The measurement of SE were carried out frequency range from 100MHz to 2GHz. It is observed that the SE of the composits is the frequency dependent increase with the increase in nickel coated carbon fibre volume fraction. The nickel coating with 20wt% ABS composite was shown to exhibit up to 60dB of SE. The result that nickel coated carbon fibre ABS composite can be used for the purpose of EMI shielding as well as for some microwave applications.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.2
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• pp.144-150
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• 2008

Ships and offshore structures are exposed to wave and engine excitation loadings during navigation and cargo/ballasting operations. These excessive loads may cause damages to hull and may result loss of life the ship. Therefore, it is important to develop a system that allow accurate measurements of global hull loads. The objective of the study is developing a fiber optic monitoring system that is capable of monitoring, recording and warning of the vessel performance. A method for measurement of global loads on a vessel, using strain measurements from a network of fiber optic strain sensors and extensive finite-element analyses(FEA) with idealistic load cases, is presented. The method has been successfully validated on the idealized ship structure model with strain sensors.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11a
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• pp.562-564
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• 2001

Transformation of molecular film occurs only usually in air-water interface, 2 dimensions domain's growth and crash are achieved. Organic matter thin film that accumulate molecular film in archaism board only that consist of growth of domain can understand correct special quality of accumulation film supplying information about fine structure and properties of matter of device observing information and so on that is surface forward player and optic enemy using AFM one of SPM application by nano electronics. The stable images are probably due to a strong interaction between the monolayer film and glass substrate. We are unable to obtain molecule resolution in images of the films but did see a marked contrast between images of the bare substrate and those with the network structure film deposited onto it. Formation that prevent when gas phase state and liquid phase state measure but Could know organic matter that molecules form equal and stable film when molecules were not distributed evenly, and accumulated in solid state only.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.59-66
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• 2019

As the monetary value of cryptocurrency increases, the security measures for cryptocurrency becomes more important. A limitation of the existing cryptocurrency exchanges is their vulnerability to threats of hacking due to their centralized manner of management. In order to overcome such limitation, blockchain technology is increasingly adopted. The blockchain technology enables decentralization and Peer-to-Peer(P2P) transactions, in which blocks of information are linked in chain topology, and each node participating in the blockchain shares a distributed ledger. In this paper, we propose and implement a mobile electronic wallet that can safely store, send and receive cryptocurrencies. The proposed mobile cryptocurrency wallet connects to the network only when the wallet actively is used. Wallet owner manages his or her private key offline, which is advantageous in terms of security. JavaScript based wallet apps were implemented to respectively run on Android and iOS mobile phones. I demonstrate the process of transferring Ethereum cryptocurrency from an account to another account through Ropsten, a test net for Ethereum. Hardware wallets, such as Ledger Nano S, provide a slightly higher level of security, yet have the disadvantages of added burden of carrying additional physical devices and high costs (about 80$).

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.39-51
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• 2022

Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

• Advances in nano research
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• v.12 no.2
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• pp.223-239
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• 2022

Object detection has always been to pursue objects with particular properties or representations and to predict details on objects including the positions, sizes and angle of rotation in the current picture. This was a very important subject of computer vision science. While vision-based object tracking strategies for the analysis of competitive videos have been developed, it is still difficult to accurately identify and position a speedy small ball. In this study, deep learning (DP) network was developed to face these obstacles in the study of tennis motion tracking from a complex perspective to understand the performance of athletes. This research has used CNN-VGG 16 to tracking the tennis ball from broadcasting videos while their images are distorted, thin and often invisible not only to identify the image of the ball from a single frame, but also to learn patterns from consecutive frames, then VGG 16 takes images with 640 to 360 sizes to locate the ball and obtain high accuracy in public videos. VGG 16 tests 99.6%, 96.63%, and 99.5%, respectively, of accuracy. In order to avoid overfitting, 9 additional videos and a subset of the previous dataset are partly labelled for the 10-fold cross-validation. The results show that CNN-VGG 16 outperforms the standard approach by a wide margin and provides excellent ball tracking performance.

• Advances in nano research
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• v.14 no.2
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.51-58
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• 2023

This paper proposes a deep learning method for estimating the heart rate from facial videos. Our proposed method estimates remote photoplethysmography (rPPG) signals to predict the heart rate. Although there have been proposed several methods for estimating rPPG signals, most previous methods can not be utilized in low-power single board computers due to their computational complexity. To address this problem, we construct a lightweight student model and employ a knowledge distillation technique to reduce the performance degradation of a deeper network model. The teacher model consists of 795k parameters, whereas the student model only contains 24k parameters, and therefore, the inference time was reduced with the factor of 10. By distilling the knowledge of the intermediate feature maps of the teacher model, we improved the accuracy of the student model for estimating the heart rate. Experiments were conducted on the UBFC-rPPG dataset to demonstrate the effectiveness of the proposed method. Moreover, we collected our own dataset to verify the accuracy and processing time of the proposed method on a real-world dataset. Experimental results on a NVIDIA Jetson Nano board demonstrate that our proposed method can infer the heart rate in real time with the mean absolute error of 2.5183 bpm.