• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.61-67
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• 2018

Emulab software was developed by the team of University of Utah and it has been replicated at dozens of other sites in the world. Although KREONET Emulab, which established by the Korea Institute of Science and Technology Information, has only a modest number of compute nodes it has been provided an ideal playground to conduct various research for network protocols, cyber security and convergence research. A testbed is a critical enabler of experimental research and researchers only carry out the experiments that are supported by the testbed. This paper outlines the Utah Emulab's status and use types among the last 10 years of operation results and compares them with the ones with the KREONET Emulab. In addition, Testbed-as-a-Service(TaaS) is discussed to upgrade the testbed for the convergence research community services.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.450-450
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• 2021

기후변화 보고서에 따르면 집중 호우의 강도 및 빈도 증가가 향후 몇 년동안 지속될 것이라 제시하였다. 이러한 집중호우가 빈번히 발생하게 된다면 강우 침식성이 증가하여 표토 침식에 더 취약하게 발생된다. Universal Soil Loss Equation (USLE) 입력 매개 변수 중 하나인 강우침식능인자는 토양 유실을 예측할때 강우 강도의 미치는 영향을 제시하는 인자이다. 선행 연구에서 USLE 방법을 사용하여 강우침식능인자를 산정하였지만, 60분 단위 강우자료를 이용하였기 때문에 정확한 30분 최대 강우강도 산정을 고려하지 못하는 한계점이 있다. 본 연구의 목적은 강우침식능인자를 이전의 진행된 방법보다 더 빠르고 정확하게 예측하는 머신러닝 모델을 개발하며, 총 월별 강우량, 최대 일 강우량 및 최대 시간별 강우량 데이터만 있어도 산정이 가능하도록 하였다. 이를 위해 본 연구에서는 강우침식능인자의 산정 값의 정확도를 높이기 위해 1분 간격 강우 데이터를 사용하며, 최근 강우 패턴을 반영하기 위해서 2013-2019년 자료로 이용했다. 우선, 월별 특성을 파악하기 위해 USLE 계산 방법을 사용하여 월별 강우침식능인자를 산정하였고, 국내 50개 지점을 대상으로 계산된 월별 강우침식능인자를 실측 값으로 정하여, 머신러닝 모델을 통하여 강우침식능인자 예측하도록 학습시켜 분석하였다. 이 연구에 사용된 머신러닝 모델들은 Decision Tree, Random Forest, K-Nearest Neighbors, Gradient Boosting, eXtreme Gradient Boost 및 Deep Neural Network을 이용하였다. 또한, 교차 검증을 통해서 모델 중 Deep Neural Network이 강우침식능인자 예측 정확도가 가장 높게 산정하였다. Deep Neural Network은 Nash-Sutcliffe Efficiency (NSE) 와 Coefficient of determination (R²)의 결과값이 0.87로서 모델의 예측성을 입증하였으며, 검증 모델을 테스트 하기 위해 국내 6개 지점을 무작위로 선별하여 강우침식능인자를 분석하였다. 본 연구 결과에서 나온 Deep Neural Network을 이용하면, 훨씬 적은 노력과 시간으로 원하는 지점에서 월별 강우침식능인자를 예측할 수 있으며, 한국 강우 패턴을 효율적으로 분석 할 수 있을 것이라 판단된다. 이를 통해 향후 토양 침식 위험을 지표화하는 것뿐만 아니라 토양 보전 계획을 수립할 수 있으며, 위험 지역을 우선적으로 선별하고 제시하는데 유용하게 사용 될 것이라 사료된다.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.36-42
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• 2008

In a gas insulated substation (GIS), Very Fast Transient Over-voltages (VFTOs) are generated due to switching operations and ground faults. These fast transients are associated with high frequency components of the order of a few hundreds of MHz. These transients may cause internal faults i.e., layer-to-layer faults or minor faults in a capacitively graded bushing, which is one of the important pieces of terminal equipment for GIS. In the present study, the PSPICE model has been developed to calculate the voltage distribution across the layers of 420kV graded bushing for high frequency pulses of rise time 1 to 50ns, which simulate the VFTO. For this simulation, an equivalent electrical network of bushing with different equivalent layers has been considered. The effect of different equivalent layers modeling circuits on the non-uniform voltage factor has been analysed. The influence of copper strip inductance on voltage distribution across layers has also been analysed for various rise times of high frequency transients. Finally, the leakage current of the bushing is calculated for evaluating the bushing condition under these transients.

• Carbon letters
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• v.5 no.4
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• pp.159-163
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• 2004

The polymer-ceramic hybrid, known as 'ceramer', was synthesized by a sol-gel process by incorporating different amount of alkoxide as source of silicon in resorcinol-formaldehyde in presence of basic catalyst to get different percentage of silicon in ultimate carbonized composites. FTIR of the ceramer confirms that it is a network of Si-O-Si, Si-O-$CH_2$ and Si-OH type groups linked with benzene ring. Different amount of silicon in the ceramer exhibits varying temperature of thermal stability and lower coefficient of thermal expansion as compared to pure resorcinol-formaldehyde resin. The lower value of CTE in ceramer is due to existence of silica and resorcinol -formaldehyde in co-continuous phase. Unidirectional composites prepared with ceramer matrix and high-strength carbon fibers show lower value of flexural strength at polymer stage as compared to those prepared with resorcinol-formaldehyde resin. However, after heat treatment to $1450^{\circ}C$, the ceramer matrix composites show large improvement in the mechanical properties, i.e. with 7% silicon in the ceramer, the flexural strength is enhanced by 100% and flexural modulus value by 40% as compared to that of pure resorcinol-formaldehyde resin matrix composites.

• Smart Structures and Systems
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• v.25 no.6
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• pp.765-778
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• 2020

The present study intends to find a proper solution for the estimation of the physical behaviors of enlarged piles through a combination of small-scale laboratory tests and a hybrid computational predictive intelligence process. In the first step, experimental program is completed considering various critical influential factors. The results of the best multilayer perceptron (MLP)-based predictive network was implemented through three mathematical-based solutions of dragonfly algorithm (DA), whale optimization algorithm (WOA), and ant lion optimization (ALO). Three proposed models, after convergence analysis, suggested excellent performance. These analyses varied based on neurons number (e.g., in the basis MLP hidden layer) and of course, the level of its complexity. The training R² results of the best hybrid structure of DA-MLP, WOA-MLP, and ALO-MLP were 0.996, 0.996, and 0.998 where the testing R² was 0.995, 0.985, and 0.998, respectively. Similarly, the training RMSE of 0.046, 0.051, and 0.034 were obtained for the training and testing datasets of DA-MLP, WOA-MLP, and ALO-MLP techniques, while the testing RMSE of 0.088, 0.053, and 0.053, respectively. This obtained result demonstrates the excellent prediction from the optimized structure of the proposed models if only population sensitivity analysis performs. Indeed, the ALO-MLP was slightly better than WOA-MLP and DA-MLP methods.

• IMMUNE NETWORK
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• v.23 no.4
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• pp.32.1-32.15
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• 2023

Most influenza vaccines currently in use target the highly variable hemagglutinin protein to induce neutralizing antibodies and therefore require yearly reformulation. T cell-based universal influenza vaccines focus on eliciting broadly cross-reactive T-cell responses, especially the tissue-resident memory T cell (T_RM) population in the respiratory tract, providing superior protection to circulating memory T cells. This study demonstrated that intramuscular (i.m.) administration of the adenovirus-based vaccine expressing influenza virus nucleoprotein (rAd/NP) elicited weak CD8 T_RM responses in the lungs and airways, and yielded poor protection against lethal influenza virus challenge. However, a novel "prime-and-deploy" strategy that combines i.m. vaccination of rAd/NP with subsequent intranasal administration of an empty adenovector induced strong NP-specific CD8⁺ T_RM cells and provided complete protection against influenza virus challenge. Overall, our results demonstrate that this "prime-and-deploy" vaccination strategy is potentially applicable to the development of universal influenza vaccines.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.160-168
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• 2013

In tactical environment, tactical wireless networks are generally comprised of Tactical MANETs(T-MANETs) or Tactical WMNs(T-WMNs). The most important services in tactical network are voice and low rate data such as command control and situation awareness. These data should be forwarded via multi-hop in tactical wireless networks. Urgent and mission-critical data should be protected in this environment, so QoS(Quality of Service) must be guaranteed for specific type of traffic for satisfying the requirement of a user. In IEEE 802.11s, TDMA-based MAC protocol, MCCA(MCF Controlled Channel Access), has a function of resource reservation. But 802.11s protocol can not guarantee the end-to-end QoS, because it only supports reservation with neighbors. In this paper, we propose the routing protocol, R-HWMP(Reservation-based HWMP) which has the resource reservation to support the end-to-end QoS. The proposed protocol can reserve the channel slots and find optimal path in T-WMNs. We analyzed the performance of the proposed protocol and showed that end-to-end QoS is guaranteed using NS-2 simulation.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• ETRI Journal
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• v.27 no.6
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• pp.691-696
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• 2005

In asynchronous transfer mode (ATM) networks, fixed length cells of 53 bytes are transmitted. A cell may be discarded during transmission due to buffer overflow or a detection of errors. Cell discarding seriously degrades transmission quality. The quality degradation can be reduced by employing efficient forward error control (FEC) to recover discarded cells. In this paper, we present the design and implementation of decoding equipment for FEC in ATM networks based on a single parity check (SPC) product code using very-large-scale integration (VLSI) technology. FEC allows the destination to reconstruct missing data cells by using redundant parity cells that the source adds to each block of data cells. The functionality of the design has been tested using the Model Sim 5.7cXE Simulation Package. The design has been implemented for a $5{\times}5$ matrix of data cells in a Virtex-E XCV 3200E FG1156 device. The simulation and synthesis results show that the decoding function can be completed in 81 clock cycles with an optimum clock of 56.8 MHz. A test bench was written to study the performance of the decoder, and the results are presented.

• Structural Engineering and Mechanics
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• v.71 no.6
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• pp.739-749
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• 2019

This article deals with the application of reliability analysis for determining the safety of simply supported beam under the uniformly distributed load. The uncertainties of the existing methods were taken into account and hence reliability analysis has been adopted. To accomplish this aim, Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR) models are developed. Reliability analysis is the probabilistic style to determine the possibility of failure free operation of a structure. The application of probabilistic mathematics into the quantitative aspects of a structure and improve the qualitative aspects of a structure. In order to construct the GRNN, ELM and GPR models, the dataset contains Modulus of Elasticity (E), Load intensity (w) and performance function (${\delta}$) in which E and w are inputs and ${\delta}$ is the output. The achievement of the developed models was weighed by various statistical parameters; one among the most primitive parameter is Coefficient of Determination ($R^2$) which has 0.998 for training and 0.989 for testing. The GRNN outperforms the other ELM and GPR models. Other different statistical computations have been carried out, which speaks out the errors and prediction performance in order to justify the capability of the developed models.