• Journal of the Society of Naval Architects of Korea
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• v.49 no.6
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• pp.469-477
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• 2012

This paper describes the characteristic analysis of ice impact force for the Arctic structure shape. In the present study an energy method has been used to predict the impact force during the ice-structure collision. This study also employs two concepts for reference contact area and normalized stress in analysis procedure. The influences of factors, such as impact velocity, full penetration depth, structure shape and ice floe size, are investigated. Full penetration occurs, particularly at lower impact velocity when ice thickness increase. But "typical size" ice floe does not expected ever to achieve full penetration during the impact procedure. The structure shape is the dominant factor in ice impact force characteristic. The results for various ice-structure collision scenarios are analyzed.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.264-264
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• 2010

물리증착이나 화학증착으로 제조되는 박막은 공정 조건에 따라 다양한 성장 양태를 보인다. 박막의 성장은 초기에 Seed가 형성되어 그 Seed를 바탕으로 성장하는 것으로 알려져 있으며 기판온도, 이온충돌, 박막의 두께 등에 따라 성장양태나 성장방위 등이 달라진다. 최근 나노에 대한 관심이 높아지면서 진공증착으로 제조한 박막에서도 조직의 나노화에 대한 관심이 높아지고 있으며 특히, Pore-free, Defect-free 박막의 형성을 통해 특성을 향상시키고자 하는 연구도 증가하고 있다. 본 연구에서는 Al과 Cu 같은 금속의 박막을 제조함에 있어서 공정변수가 박막의 조직이나 배향성 등에 미치는 영향을 조사하였다. 특히, 이러한 조직변화와 박막의 특성과의 상관성을 도출하고자 하였다. Al 박막에서는 이온빔의 효과와 함께 공정중에 산소 가스를 주입하거나 플라즈마 처리를 통해 성장조직의 변화를 유도하였고, Cu 박막에서는 고속 증착 조건이 피막의 조직에 미치는 영향을 조사하였다. 한편, TiN 박막의 형성에 미치는 이온빔의 효과를 조사하여 이온빔 조건과 TiN 박막의 형성과의 관계를 규명하였고 이로부터 Normalized Energy가 TiN 박막의 색상에 미치는 영향을 도출하여 Normalized Energy가 Fundamental Parameter가 될 수 있음을 확인하였다.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• International Journal of Highway Engineering
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• v.7 no.2 s.24
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• pp.69-76
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• 2005

While the repeated traffic loading accumulates the damage of asphalt pavement, the damage has being healed during rest periods. And then, this healing enhances the fatigue life of asphalt pavement. A method was developed to determine the healing rate of asphalt mixture in terms of recovered dissipated creep strain energy (DCSE) per unit time, and the healing properties of four different asphalt mixtures were evaluated. The test procedure consists of repeated loading test and periodical resilient modulus tests. A normalized healing rate in terms of $DCSE/DCSE_{applied}$ was defined to evaluate the healing properties independently of the amount of damage incurred in the mixture. From the test results, it was concluded that the healing rates of asphalt mixtures were increased exponentially as the temperature was increased and more affected by the structural characteristics of mixture such as asphalt content than the binder characteristics such as the polymer modification.

• Geophysics and Geophysical Exploration
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• v.15 no.2
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• pp.66-74
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• 2012

This paper presents development of a three-dimensional marine controlled-source electromagnetic (mCSEM) modeling algorithm and its application to a salt and reservoir model to examine detectability of mCSEM for a reservoir under complex subsurface structures. The algorithm is based on the finite difference method, and employs the secondary field formulation for an accurate and fast calculation of modeling responses. The algorithm is verified for a two-layer model by comparing solutions not only with analytic solutions but also with those from other 3D modeling algorithm. We calculate and analyze electric and magnetic fields and their normalized responses for a salt and reservoir model due to three sources located at boundaries between a salt, a reservoir, and background. Numbers and positions of resistive anomalies are informed by normalized responses for three sources, and types of resistive anomalies can be informed when there is a priori information about a salt by seismic exploration.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.155-162
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• 2003

The evolved energy concept has been demonstrated to fir the seismic evaluation of various steel connection details with the objective basis. For this, the strain energy stored in the structural system obtained from the experimental database has been normalized by that of a benchmark system. In this notion, the ideal elastic-perfectly plastic structural system has been assumed as a benchmark. In addition, the attempt shows that those previously peformed experimental database can be recycled to have further significance.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.825-833
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• 2020

Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3928-3942
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• 2022

Thermal striping is a complex thermal-hydraulic phenomenon caused by fluid temperature fluctuations that can also cause high-cycle thermal fatigue to the structural wall of sodium-cooled fast reactors (SFRs). Numerical simulations using large-eddy simulation (LES) were performed to predict and evaluate the characteristics of the temperature fluctuations related to thermal striping in the upper internal structure (UIS) of the prototype generation-IV sodium-cooled fast reactor (PGSFR). Specific monitoring points were established for the fluid region near the control rod driving mechanism (CRDM) guide tubes, CRDM guide tube walls, and UIS support plates, and the normalized mean and fluctuating temperatures were investigated at these points. It was found that the location of the maximum amplitude of the temperature fluctuations in the UIS was the lowest end of the inner wall of the CRDM guide tube, and the maximum value of the normalized fluctuating temperatures was 17.2%. The frequency of the maximum temperature fluctuation on the CRDM guide tube walls, which is an important factor in thermal striping, was also analyzed using the fast Fourier transform analysis. These results can be used for the structural integrity evaluation of the UIS in SFR.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.11
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• pp.954-962
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• 1998

Energy-trapped thickness vibrations of piezoelectric substrates are utilized in fabricating resonators and filters which have their operating frequencies in HF band. Normalized particle displacement distributions of the fundamental thickness shear vibration mode and overtone modes into the thickness direction in energy-trapped resonators and double-coupled filters were obtained by solving the wave equation and calculating the solved equations. These results show that as the number order of the harmonic mode in a energy-trapped resonator becomes larger, the degree of energy-trapping in the resonator increase, and if the conditions for energy-trapping become sufficiently weak, the energy-trapping effect of the harmonic mode which has the lower order disappears the earlier. Above simulation results were proved by the experiments.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.58-60
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• 2000

In this paper we present an estimation method of electric energy[kWh] for load management of pole-transformer. For the electric energy estimation, we use the nonlinear load research based estimation(NLRE) algorithm. The NLRE curve is the normalized annual cumulative energy consumption for a particular day in a year. And, it is used for the coefficient estimation. Estimation method of suggested electric energy of pole-transformer used billing cycle electric energy estimation equation is verified as comparison billing cycle electric energy and estimated electric energy. We can reduce the error of peak load estimation by suggested method than the conventional method in domestic.