• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3612-3624
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• 2021

At present, in the case of pump fast optimization, there is a problem of rapid, accurate and effective prediction of cavitation performance. In "A Cavitation Performance Prediction Method for Pumps PART1-Proposal and Feasibility" [1], a new cavitation performance prediction method is proposed, and the feasibility of this method is demonstrated in combination with experiments of a mixed flow pump. However, whether this method is applicable to vane pumps with different specific speeds and whether the prediction results of this method are accurate is still worthy of further study. Combined with the experimental results, the research evaluates the sensitivity and accuracy at different flow rates. For a certain operating condition, the method has better sensitivity to different flow rates. This is suitable for multi-parameter multi-objective optimization of pump impeller. For the test mixed flow pump, the method is more accurate when the area ratios are 13.718% and 13.826%. The cavitation vortex flow is obtained through high-speed camera, and the correlation between cavitation flow structure and cavitation performance is established to provide more scientific support for cavitation performance prediction. The method is not only suitable for cavitation performance prediction of the mixed flow pump, but also can be expanded to cavitation performance prediction of blade type hydraulic machinery, which will solve the problem of rapid prediction of hydraulic machinery cavitation performance.

• Journal of Adhesion and Interface
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• v.10 no.1
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• pp.30-34
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• 2009

The effect of pressure on the thermal conductivity in two-phase composite system was studied. Thermally conductive polypropylene (PP)/silicon carbide (SiC) composites were prepared by applying various pressures from 0 to 20 MPa. The thermal conductivity of the composite was 1.86 W/mK at 20 MPa, increased by 40% compared to the value of at 0 MPa. It was 9 times higher than that of unfilled polypropylene. It implies the pressure induces the easy path for phonon transport. Also, the experimental values were compared with Maxwell's prediction and Agari's prediction. Agari's prediction gave a better agreement compared to that of Maxwell's prediction due to the consideration of interactions between filler-filler and filler-polymer.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.163-173
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• 2018

This paper performed the prediction of the acoustic loads applied to the surface of the flight vehicle during flight. Acoustic loads during flight arise from the pressure fluctuations on the surface of body. The conventional method of predicting the acoustic loads in flight uses semi-empirical method derived from theoretical and experimental results. However, there is a limit in obtaining the flow characteristics and the boundary layer parameters of the flight vehicle which are used as the input values of the empirical equation through experiments. Therefore, in this paper, we use the hybrid method which combines the results of CFD (Computational Fluid Dynamics) with semi-empirical methods to predict the acoustic loads acting on flight vehicle during flight. For the flight vehicle with cone-cylinder-flare shape, acoustic loads were estimated for the subsonic, transonic, supersonic, and Max-q (Maximum dynamic pressure) condition flight. For the hybrid method, two kind of boundary layer edge estimation methods based on CFD results are compared and the acoustic loads prediction results were compared according to empirical equations presented by various researchers.

• Wind and Structures
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• v.36 no.6
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• pp.393-404
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• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.365-372
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• 2021

The purpose of this study was to examine the statements characteristics of the pressure ulcer nursing record by natural langage processing and assess the prediction accuracy for each pressure ulcer stage. Nursing records related to pressure ulcer were analyzed using descriptive statistics, and word cloud generators (http://wordcloud.kr) were used to examine the characteristics of words in the pressure ulcer prevention nursing records. The accuracy ratio for the pressure ulcer stage was calculated using deep learning. As a result of the study, the second stage and the deep tissue injury suspected were 23.1% and 23.0%, respectively, and the most frequent key words were erythema, blisters, bark, area, and size. The stages with high prediction accuracy were in the order of stage 0, deep tissue injury suspected, and stage 2. These results suggest that it can be developed as a clinical decision support system available to practice for nurses at the pressure ulcer prevention care.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.3
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• pp.111-121
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• 2002

This study was performed to estimate the degree of consolidation using excess pore water pressure in the very soft ground. The final settlement prediction methods by Hyperbolic, Asaoka and Curve fitting methods from the measured settlement data were used to compare with the degree of consolidation estimated by excess pore water pressure. The dissipated excess pore water pressure during embankment construction and the peak excess pore water pressure on the completed embankment were used for the estimation of the degree of consolidation. After completion of embankment, it was concluded that the degree of consolidation estimated from dissipated excess pore water pressure was more reliable than that from the peak excess pore water pressure. And, the degree of consolidation estimated from the surface settlement was nearly the same as settlement of each layer. The degree of consolidation estimated from dissipated excess pore water pressure was a little larger than that from settlement.

• Proceedings of the KIEE Conference
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• 1986.07a
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• pp.429-432
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• 1986

• 유체기계공업학회:학술대회논문집
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• 2005.12a
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• pp.17-40
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• 2005

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.971-972
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• 2005

• Proceedings of the Korean Nuclear Society Conference
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• 1999.05a
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• pp.131-131
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• 1999