• Journal of Platform Technology
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• v.8 no.2
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• pp.22-31
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• 2020

The construction and operation of industrial automation has been actively taking place from manufacturing plan to production for improving operational efficiency of production line and flexibility of equipment. ISO/TC184 is standardizing on operating methods that can share information of programmable device controllers such as PLC and IoT that are geographically distributed in the production line. In this study, the design of the cell controller consists of PLC group and IoT group that perform signals such as temperature sensors, gas sensors, and pressure sensors for thermal processes and corresponding motors or valves. The operation and analysis of the cell controller were performed using SDN(Software Defined Network) and the three types of process services performed in thermal processes are real-time transmission service, loss-sensitive large-capacity transmission service, and normal transmission service. The simulation result showed that the average loss rate improved by about 17% when the traffic increased before and after the application of the SDN route technique, and the delay in the real-time service was as low as 1 ms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.50-55
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• 2021

Multi-path system is a system in which utilizes various networks simultaneously. It is expected that multi-path system can enhance communication speed, reliability, security of network. In this paper, we focus on path selection in multi-path system. To select optimal path, we propose deep reinforcement learning algorithm which is rewarded by the round-trip-time (RTT) of each networks. Unlike multi-armed bandit model, deep Q learning is applied to consider rapidly changing situations. Due to the delay of RTT data, we also suggest compensation algorithm of the delayed reward. Moreover, we implement testbed learning server to evaluate the performance of proposed algorithm. The learning server contains distributed database and tensorflow module to efficiently operate deep learning algorithm. By means of simulation, we showed that the proposed algorithm has better performance than lowest RTT about 20%.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.59-69
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• 2021

Historical records of earthquakes are generally used as a basis to extrapolate the instrumental earthquake catalog in time and space during the probabilistic seismic hazard analysis (PSHA). However, the historical catalogs' input parameters determined through historical descriptions rather than any quantitative measurements are accompanied by considerable uncertainty in PSHA. Therefore, quantitative assessment to verify the historical earthquake parameters is essential for refining the reliability of PSHA. This study presents an approach and its application to constrain reliable ranges of the magnitude and corresponding epicenter of historical earthquakes. First, ranges rather than specific values of ground motion intensities are estimated at multiple locations with distances between each other for selected historical earthquakes by reviewing observed co-seismic natural phenomena, structural damage levels, or felt areas described in their historical records. Based on specific objective criteria, this study selects only one earthquake (July 24, 1643), which is potentially one of the largest historical earthquakes. Then, ground motion simulations are performed for sufficiently broadly distributed epicenters, with a regular grid to prevent one from relying on strong assumptions. Calculated peak ground accelerations and velocities in areas with the historical descriptions on corresponding earthquakes are converted to intensities with an empirical ground motion-intensity conversion equation to compare them with historical descriptions. For the ground motion simulation, ground motion prediction equations and a frequency-wavenumber method are used to consider the effects of possible source mechanisms and stress drop. From these quantitative calculations, reliable ranges of epicenters and magnitudes and the trade-off between them are inferred for the earthquake that can conservatively match the upper and lower boundaries of intensity values from historical descriptions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.114-121
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• 2022

In this paper, we consider a user centric clustering in order to guarantee the performance of the users in cell free multiple-input multiple-output (MIMO) network. In the user centric clustering scheme, by using large scale fading coefficients of the connected access points (APs), each user decides own cluster with the APs having the higher the large scale fading coefficients than threshold value compared to the highest large scale fading coefficient. In the determined user centric clusters, the APs design the beamformers and power allocations in the distributed manner and the APs cooperatively transmit data to users by using beamformers and power allocations. In the simulation results, we verify the performance of user centric clustering in terms of the spectral efficiency and we also find the optimal threshold value in the given configuration.

• Steel and Composite Structures
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• v.42 no.6
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• pp.779-789
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• 2022

This work presents a non-linear cylindrical bending analysis of functionally graded plate reinforced by single-walled carbon nanotubes (SWCNTs) in thermal environment using a simple integral higher-order shear deformation theory (HSDT). This theory does not require shear correction factors and the transverse shear stresses vary parabolically through the thickness. The material properties of SWCNTs are assumed to be temperature-dependent and are obtained from molecular dynamics simulations. The material properties of functionally graded carbon nanotube-reinforced composites (FG-CNTCRs) are considered to be graded in the thickness direction, and are estimated through a micromechanical model. The non-linear strain-displacement relations in the Von Karman sense are used to study the effect of geometric non-linearity and the solution is obtained by minimization of the total potential energy. The numerical illustrations concern the nonlinear bending response of FG-CNTRC plates under different sets of thermal environmental conditions, from which results for uniformly distributed CNTRC plates are obtained as benchmarks.

• Computers and Concrete
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• v.29 no.6
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• pp.393-405
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• 2022

Lightweight concrete is a superior material due to its light weight and high strength. There however remain significant lacunae in engineering knowledge with regards to shear failure of lightweight fiber reinforced concrete beams. The main aim of the present study is to investigate the optimum usage of steel fibers in lightweight fiber reinforced concrete (LWFRC). Multi-scale finite element model calibrated with experimental results is developed to study the effect of steel fibers on the mechanical properties of LWFRC beams. To decrease the amount of steel fibers, it is preferred to reinforce only the middle section of the LWFRC beams, where the flexural stresses are higher. For numerical simulation, a multi-scale finite element model was developed. The cement matrix was modeled as homogeneous and uniform material and both steel fibers and lightweight coarse aggregates were randomly distributed within the matrix. Considering more realistic assumptions, the bonding between fibers and cement matrix was considered with the Cohesive Zone Model (CZM) and its parameters were determined using the model update method. Furthermore, conformity of Load-Crack Mouth Opening Displacement (CMOD) curves obtained from numerical modeling and experimental test results of notched beams under center-point loading tests were investigated. Validating the finite element model results with experimental tests, the effects of fibers' volume fraction, and the length of the reinforced middle section, on flexural and residual strengths of LWFRC, were studied. Results indicate that using steel fibers in a specified length of the concrete beam with high flexural stresses, and considerable savings can be achieved in using steel fibers. Reducing the length of the reinforced middle section from 50 to 30 cm in specimens containing 10 kg/m3 of steel fibers, resulting in a considerable decrease of the used steel fibers by four times, whereas only a 7% reduction in bearing capacity was observed. Therefore, determining an appropriate length of the reinforced middle section is an essential parameter in reducing fibers, usage leading to more affordable construction costs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.9-14
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• 2022

In the IoT(internet of things) where various devices can be connected, failure of essential devices may lead to a lot of economic and life losses. For reducing the losses, fault diagnosis techniques have been considered an essential part of IoT. In this paper, the method based on a graph neural network is proposed for determining fault and classifying types by extracting features from vibration data of systems. For training of the deep learning model, fault dataset are used as input data obtained from the CWRU(case western reserve university). To validate the classification performance of the proposed model, a conventional CNN(convolutional neural networks)-based fault classification model is compared with the proposed model. From the simulation results, it was confirmed that the classification performance of the proposed model outweighed the conventional model by up to 5% in the unevenly distributed data. The classification runtime can be improved by lightweight the proposed model in future works.

• Journal of Ocean Engineering and Technology
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• v.36 no.6
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• pp.380-389
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• 2022

In the Haeundae area of Busan, South Korea, damage has continued to occur recently from building wind from caused by dense skyscrapers. Five wind observation stations were installed near LCT residential towers in Haeundae to analyze the effect of building winds during typhoon Omais. The impact of building wind was analyzed through relative and absolute evaluations. At an intersection located southeast of LCT (L-2), the strongest wind speed was measured during the monitoring. The maximum average wind speed for one minute was observed to be 38.93 m/s, which is about three times stronger than at an ocean observation buoy (12.7 m/s) at the same time. It is expected that 3 to 4 times stronger wind can be induced under certain conditions compared to the surrounding areas due to the building wind effect. In a Beaufort wind scale analysis, the wind speed at an ocean observatory was mostly distributed at Beaufort number 4, and the maximum was 8. At L-2, more than 50% of the wind speed exceeded Beaufort number 4, and numbers up to 12 were observed. However, since actual measurement has a limitation in analyzing the entire range, cross-validation with computational fluid dynamics simulation data is required to understand the characteristics of building winds.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.295-303
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• 2023

Whole-body counters are widely used to assess internal contamination after a nuclear accident. However, it is difficult to determine radioiodine activity due to limitations in conventional calibration phantoms. Inhaled or ingested radioiodine is heterogeneously distributed in the human body, necessitating time-dependent biodistribution for the assessment of the internal contamination caused by the radioiodine intake. This study aims at calculating counting efficiencies considering the biodistribution of ¹³¹I in whole-body counting measurement. Monte Carlo simulations with computational human phantoms were performed to calculate the whole-body counting efficiency for a realistic radioiodine distribution after its intake. The biodistributions of ¹³¹I for different age groups were computed based on biokinetic models and applied to age- and gender-specific computational phantoms to estimate counting efficiency. After calculating the whole-body counting efficiencies, the efficiency correction factors were derived as the ratio of the counting efficiencies obtained by considering a heterogeneous biodistribution of ¹³¹I over time to those obtained using the BOMAB phantom assuming a homogeneous distribution. Based on the correction factors, the internal contamination caused by ¹³¹I can be assessed using whole-body counters. These correction factors can minimize the influence of the biodistribution of ¹³¹I in whole-body counting measurement and improve the accuracy of internal dose assessment.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.314-314
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• 2020

SWAT (Soil and Water Assessment Tool)은 미국 농무성 농업연구소에서 개발된 준분포형(semi-distributed) 수문 모형으로 복합토지이용유역에서 장기간에 걸친 다양한 종류의 토양, 토지이용 및 토지관리 상태의 변화에 따른 유역의 유출량, 유사량 및 영양물질의 영향을 예측하기 위해 개발되었다. SWAT은 기본적으로 다양한 매개변수에 대한 수동 보정 기능을 제공하고 있지만 매개변수 보정에 따른 모의결과의 불확실성을 수반하게 된다. 이러한 문제를 해결하기 위해 자동보정 기능을 제공하는 SWAT-CUP (Calibration and Uncertainty Program)이 개발되었다. SWAT-CUP에서 제공하는 매개변수의 최적화 과정에서 유사한 모의 결과를 산출하는 수천 개의 매개변수조합이 존재하기 때문에 보정기법의 선택에 따라 최종 매개변수의 값이 달라질 수 있다. 불확실성을 발생시키는 요인으로 (1) 매개변수의 선택, (2) 보정 기법, (3) 목적함수, (4) 매개변수의 초기 범위, (5) 모의(simulation)의 실행(run) 및 반복(iteration) 횟수, (6) 위치, 개수 등 보정 자료의 선택 등이 주로 지목된다. 이러한 요인으로 발생하는 불확실성은 SWAT 모형의 구조 및 입력 자료에서 기인하는 것으로, 사용자의 설정에 따라 크게 좌우된다. 본 연구에서는 SWAT 매개변수 보정 과정에서 발생할 수 있는 불확실성을 평가하고, 효율적인 보정 방안을 제시하기 위해 수행되었다. 낙동강 권역의 내성천 유역을 대상으로 SWAT 모형을 구축하였으며, 내성천 본류에 위치한 수위(유량) 관측소의 자료를 활용하여 검·보정을 수행하였다. 모의 결과는 유량의 크기 뿐 아니라 유량의 발생 시기, 유역의 반응 및 증가·감소 경향성을 함께 고려하여 평가하였다. 그 결과 모형 구조에 따른 불확실성의 전이과정을 정확하게 파악하는 것은 불가능하지만 SWAT 모형의 비고유성(non-uniqueness)에 의한 불확실성을 정량화하여 나타내었다.