• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.12
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• pp.1058-1063
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• 2015

In this article, we propose a near field microwave scanning probe structure in which two short conducting rods are attached to the center of the ridged(H-type) aperture, thereby reducing significantly the beam spot area while maintaining the high transmission efficiency through the output coupling H-type(ridged) aperture. Here the two short parallel conducting rods seem to play an important role of concentrating the transmitted electromagnetic energy through the H-type aperture and so reducing the beam area for high resolution. For validation of the proposed theory, the near field waveguide probe is fabricated according to the simulated results and its return loss characteristics versus frequencies are measured. The comparison between theory and experiment is seen to be in good agreements.

• Corrosion Science and Technology
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• v.8 no.6
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• pp.217-222
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• 2009

The production of advanced high strength steels requires the improvement of cooling technology. The use of high cooling rates allows relatively low levels of expensive alloying additions to ensure sufficient hardenability. In classical annealing and hot-dip galvanizing lines a mixing station is used to provide atmosphere gas containing 3-5% hydrogen and 97-95% nitrogen in the various sections of the furnace, including the rapid cooling section. Heat exchange enhancement in this cooling section can be insured by the increased hydrogen concentration. Drever International developed a patented improvement of cooling technology based on the following features: pure hydrogen gas is injected only in the rapid cooling section whereas the different sections of the furnace are supplied with pure nitrogen gas; the control of flows through atmosphere gas management allows to get high hydrogen concentration in cooling section and low hydrogen content in the other furnace zones. This cooling technology development insures higher cooling rates without additional expensive hydrogen gas consumption and without the use of complex sealing equipments between zones. In addition reduction in electrical energy consumption is obtained. This atmosphere control development can be combined with geometrical design improvements in order to get optimised cooling technology providing high cooling rates as well as reduced strip vibration amplitudes. Extensive validation of theoretical research has been conducted on industrial lines. New lines as well as existing lines, with limited modifications, can be equipped with this new development. Up to now this technology has successfully been implemented on 6 existing and 7 new lines in Europe and Asia.

• Journal of Power Electronics
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• v.17 no.4
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• pp.892-904
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• 2017

A design of a Class-E inverter with only one inductor and one capacitor is presented. It is operated at the optimal operation mode for domestic cooker. The design principle is based on the zero-voltage derivative switching (ZVDS) of the Class-E inverter with a parallel load network, which is a parallel resonant equivalent circuit. An induction load characterization is obtained from a large-signal excitation test bench, which is the key to an accurate design of the induction cooker system. Consequently, the proposed scheme provides a more systematic, simple, accurate, and feasible solution than the conventional quasi-resonant inverter analysis based on series load network methodology. The derivative of the switch voltage is zero at the turn-on transition, and its absolute value is relatively small at the turn-off transition. Switching losses and noise are reduced. The parameters of the ZVDS Class-E inverter for the domestic induction cooker must be selected properly, and details of the design of the components of this Class-E inverter need to be addressed. A 1,200 W prototype is designed and evaluated to verify the validation of the proposed topology.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.53-58
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• 2006

MicroRNA (miRNA)는 약 22 nt의 작은 RNA 조각으로 이루어져 있으며 stem-loop 구조의 precursor 형태에서 최종적으로 만들어 진다. miRNA는 mRNA의 3‘UTR에 상보적으로 결합하여 유전자의 발현을 억제하거나 mRNA의 분해를 촉진한다. miRNA를 동정하기 위한 실험적인 방법은 조직 특이적인 발현, 적은 발현양 때문에 방법상 한계를 가지고 있다. 이러한 한계는 컴퓨터를 이용한 방법으로 어느 정도 해결될 수 있다. 하지만 miRNA의 서열상의 낮은 보존성은 homology를 기반으로 한 예측을 어렵게 한다. 또한 기계학습 방법인 support vector machine (SVM) 이나 naive bayes가 적용되었지만, 생물학적인 의미를 해석할 수 있는 generative model을 제시해 주지 못했다. 본 연구에서는 우수한 miRNA 예측을 보일 뿐만 아니라 학습된 모델로부터 생물학적인 지식을 얻을 수 있는 Bayesian network을 적용한다. 이를 위해서는 생물학적으로 의미 있는 특질들의 선택이 중요하다. 여기서는 position weighted matrix (PWM)과 Markov chain probability (MCP), Loop 크기, Bulge 수, spectrum, free energy profile 등을 특질로서 선택한 후 Information gain의 특질 선택법을 통해 예측에 기여도가 높은 특질 25개 와 27개를 최종적으로 선택하였다. 이로부터 Bayesian network을 학습한 후 miRNA의 예측 성능을 10 fold cross-validation으로 확인하였다. 그 결과 pre-/mature miRNA 각 각에 대한 예측 accuracy가 99.99% 100.00%를 보여, SVM이나 naive bayes 방법보다 높은 결과를 보였으며, 학습된 Bayesian network으로부터 이전 연구 결과와 일치하는 pre-miRNA 상의 의존관계를 분석할 수 있었다.

• Nuclear Engineering and Technology
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• v.42 no.4
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• pp.339-364
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• 2010

Following a joint OECD/NEA-IAEA-sponsored meeting to define the current role and future perspectives of the application of Computational Fluid Dynamics (CFD) to nuclear reactor safety problems, three Writing Groups were created, under the auspices of the NEA working group WGAMA, to produce state-of-the-art reports on different aspects of the subject. The work of the second group, WG2, was to document the existing assessment databases for CFD simulation in the context of Nuclear Reactor Safety (NRS) analysis, to gain a measure of the degree of quality and trust in CFD as a numerical analysis tool, and to take initiatives to extend the existing databases. The group worked over the period of 2003-2007 and produced a final state-of-the-art report. The present paper summarises the material gathered during the study, illustrating the points with a few highlights. A total of 22 safety issues were identified for which the application of CFD was considered to potentially bring real benefits in terms of better understanding and increased safety. A list of the existing databases was drawn up and synthesised, both from the nuclear area and from other parallel, non-nuclear, industrial activities. The gaps in the technology base were also identified and discussed. In order to initiate new ways of bringing experimentalists and numerical analysts together, an international workshop -- CFD4NRS (the first in a series) -- was organised, a new blind benchmark activity was set up based on turbulent mixing in T-junctions, and a Wiki-type web portal was created to offer online access to the material put together by the group giving the reader the opportunity to update and extend the contents to keep the information source topical and dynamic.

• Steel and Composite Structures
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• v.25 no.2
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• pp.157-175
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• 2017

The novelty of this work is the use of a new displacement field that includes undetermined integral terms for analyzing thermal buckling response of functionally graded (FG) sandwich plates. The proposed kinematic uses only four variables, which is even less than the first shear deformation theory (FSDT) and the conventional higher shear deformation theories (HSDTs). The theory considers a trigonometric variation of transverse shear stress and verifies the traction free boundary conditions without employing the shear correction factors. Material properties of the sandwich plate faces are considered to be graded in the thickness direction according to a simple power-law variation in terms of the volume fractions of the constituents. The core layer is still homogeneous and made of an isotropic material. The thermal loads are assumed as uniform, linear and non-linear temperature rises within the thickness direction. An energy based variational principle is employed to derive the governing equations as an eigenvalue problem. The validation of the present work is checked by comparing the obtained results the available ones in the literature. The influences of aspect and thickness ratios, material index, loading type, and sandwich plate type on the critical buckling are all discussed.

• Structural Engineering and Mechanics
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• v.77 no.1
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• pp.47-56
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• 2021

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.973-991
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• 2022

Globally, several countries with sea are using eco-friendly energy resources through offshore wind power development by overcoming the weak point of the existing power generation method. The sea has the advantage of being able to develop large scale wind farms in wide waters, but the installation of marine structures threatens the safe operation of vessels. Accordingly, a standard guideline for safe navigation by analyzing the mutual effects between ship routes and offshore wind site was presented by the PIANC. Nonetheless, the standard guideline calculated the same safe distance in all situations. Therefore, this study developed a calculation model for an optimal safe distance between ship routes and offshore wind sites by reflecting the ship's maneuvering, encounter situations, environmental force, traffic density, offshore wind power generators, and channel types. As a result of the validation simulation, the developed model showed that the optimal safe distance was secured.

• Smart Structures and Systems
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• v.29 no.3
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• pp.445-455
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• 2022

Steel anchor bolts are installed in concrete using a variety of methods. One of the most common methods of anchor bolt installation is using epoxy resin as an infill material injected into the drilled hole to act as a bonding material between the steel bolt and the surrounding concrete. Typical design standards assume uniform stress distribution along the length of the anchor bolt accompanied with single crack leading to pull-out failure. Experimental evidence has shown that the steel anchor bolts fail owing to the multiple failure patterns, hence these design assumptions are not realistic. In this regard, the presented research work details the analytical model that takes into consideration multiple micro cracks in the infill material induced via impact loading. The impact loading from the Schmidt hammer is used to evaluate the bond condition bond condition of anchor bolt and the epoxy material. The added advantage of the presented analytical model is that it is able to take into account the various type of end conditions of the anchor bolts such as bent or U-shaped anchors. Through sensitivity analysis the optimum stiffness and shear strength properties of the epoxy infill material is achieved, which have shown to achieve lower displacement coupled with reduced damage to the surrounding concrete. The accuracy of the presented model is confirmed by comparing the simulated deformational responses with the experimental evidence. From the comparison it was found that the model was successful in simulating the experimental results. The proposed model can be adopted by professionals interested in predicting and controlling the deformational response of anchor bolts.