• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.1
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• pp.1-12
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• 2016

One of the most important factors in sailing yacht design is accurate velocity prediction. Velocity prediction programs (VPP's) are widely used to predict velocity of sailing yachts. VPP's, which are primarily based on experimental data and experience of long years, however suffer limitations when applied in realistic conditions. Thus, in the present study, a high fidelity velocity prediction method using computational fluid dynamics (CFD) was proposed. Using the developed method, velocity and attitude of a 30 feet sloop yacht, which was developed by Korea Research Institute of Ship and Ocean (KRISO) and termed KORDY30, were predicted in upwind sailing condition.

• Journal of computational fluids engineering
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• v.19 no.3
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• pp.1-7
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• 2014

One of the most important factors in sailing yacht design is an accurate velocity prediction. Velocity prediction programs (VPPs) are widely used to predict velocity of sailing yachts. VPPs, which are primarily based on experimental data and experience of long years, suffer limitations applied in realistic conditions. Thus, in the present study, a high fidelity velocity prediction method using the computational fluid dynamics (CFD) is proposed. Using the developed method, velocity and motion of a 30 feet sloop yacht, which was developed by Korea Research Institute of Ship and Ocean (KRISO) and termed KORDY30, were predicted in upwind sailing condition.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.18-25
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• 2023

In these days, high dimensional data prediction technology based on neural network shows compelling results in many different kind of field including engineering. Especially, a lot of variants of convolution neural network are widely utilized to develop pixel level prediction model for high dimensional data such as picture, or physical field value from the sensors. In this study, velocity vector field of ideal flow on ship surface is estimated on pixel level by Unet. First, potential flow analysis was conducted for the set of hull form data which are generated by hull form transformation method. Thereafter, four different neural network with a U-shape structure were conFig.d to train velocity vectors at the node position of pre-processed hull form data. As a result, for the test hull forms, it was confirmed that the network with short skip-connection gives the most accurate prediction results of streamlines and velocity magnitude. And the results also have a good agreement with potential flow analysis results. However, in some cases which don't have nothing in common with training data in terms of speed or shape, the network has relatively high error at the region of large curvature.

• Earthquakes and Structures
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• v.8 no.5
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• pp.957-976
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• 2015

Recently, two energy-based response parameters, i.e., the absolute and the relative elastic input energy equivalent velocity, have been receiving a lot of research attention. Several studies, in fact, have demonstrated the potential of these intensity measures in the prediction of the seismic structural response. Although some ground motion prediction equations have been developed for these parameters, they only provide marginal distributions without information about the joint occurrence of the spectral values at different periods. In order to build new prediction models for the two equivalent velocities, a large set of ground motion records is used to calculate the correlation coefficients between the response spectral values corresponding to different periods and components of the ground motion. Then, functional forms adopted in models from the literature are calibrated to fit the obtained data. A new functional form is proposed to improve the predictions of the considered models from the literature. The components of the ground motion considered in this study are the two horizontal ones only. Potential uses of the proposed equations in addition to the prediction of the correlation coefficients of the equivalent velocity spectral values are shown, such as the prediction of derived intensity measures and the development of conditional mean spectra.

• ETRI Journal
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• v.42 no.3
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• pp.366-375
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• 2020

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA-WNN. A comparative analysis of the experimental results shows that the performance of the IGSA-WNN-based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.1
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• pp.40-49
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• 2004

In this paper, vibration response of aerial gas pipeline due to vehicle loads was quantitatively estimated through experiment and analysis in open cut construction site. The vehicle vibration of various construction machines causes serious effect to the aerial gas pipeline. The new vibration prediction equations presented in this study can estimate the vibration velocity response of the aerial gas pipeline. In the nitration prediction equations, the vehicle′s weight and traveling velocity, which are the sources of vibration, are combined into the term called, "scaled weight" Methods to reduce vibration were proposed in case the vibration velocity response of the gas pipeline exceeded the vibration criterion, using the vibration prediction equations presented in this study. One was to limit the vehicle′s traveling velocity and the other to install the isolation equipment. Both methods can be estimated quantitatively.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.342-347
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• 2003

It is essential to develop an algorithm for the estimate of link travel velocity and for the supply and control of travel information in the context of intelligent transportation information system. The paper proposes the fuzzy logic based prediction of link travel velocity. Three factors such as time, date and velocity are considered as major components to represent the travel situation. In the fuzzy modeling, those factors were expressed by fuzzy membership functions. We acquire position／velocity data through GPS antenna with PDA embedded probe vehicles. The link travel velocity is calculated using refined GPS data and the prediction results are compared with actual data for its accuracy.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.4
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• pp.97-105
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• 2001

The ultrasonic pulse velocity test has a strong potential to be developed into a very useful and relatively inexpensive in-place test for assuring the quality of concrete placed in structure. The main problem in realizing this potential is that the relationship between compressive strength ad ultrasonic pulse velocity is uncertain and concrete is an inherently variable material. The objective of this study is to improve the reliability of in-place concrete strength predictions by ultrasonic pulse velocity method. Experimental cement content, s/a rate, and curing condition of concrete. Accuracy of the prediction expressed in empirical formula are examined by multiple regression analysis and linear regression analysis and practical equation for estimation the concrete strength are proposed. Multiple regression model uses water-cement ratio cement content s/a rate, and pulse velocity as dependent variables and the compressive strength as an independent variable. Also linear regression model is used to only pulse velocity as dependent variables. Comparing the results of the analysis the proposed equation expressed highest reliability than other previous proposed equations.

• Journal of the Korean Institute of Navigation
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• v.1 no.1
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• pp.1-16
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• 1977

The aspects of Omega phase prediction are briefly reviewed, and Swanson's Model and Pierce's Model are presented. The equations for the Omega phase prediction and the most probable coefficients of the propagating equations are derived on the base of Pierce's Model by the least square method. The coefficients are calculated from the data which are the phase differences between the pairs of the Station A (Aldra, Norway), C(Haiku, Hawaii), and D(La Mour, North Dakota) observed at Busan Harbor of the South Coast of Korea in June and September, 1976. It is clearly shown that the standard deviations of the observed lane values at Busan Harbor are as followed: 1. June, 1976. Pair (A-C) : 0.1446 Pair (C-D) : 0.2598 2.September, 1976. Pair (A-D) : 0.3958 Pafr (C-D) : 0.3278 As a conclusion of the above investigation, it is shown that the Omega phase velocity can be predicted by the method, proposed in this paper, of analyzing the diurnal and seasonal variations of the Omege phase velocity except SID, PCD and AZD. If more observed data are employed, more exact Omega phase velocity is expected to be obtained.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.1
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• pp.1-9
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• 2018

In this paper, we have proposed a prediction method for sabot-trajectory of projectile using high speed camera data analysis. Through analyzing trajectory of sabot with high speed camera data, we can extract its real velocity and acceleration including effects of friction force, pressure of flume, etc. Using these data, we suggest a prediction method for sabot-trajectory of projectile having variable acceleration, especially for minimum and maximum acceleration, by using interpolation method for velocity and acceleration data of sabot. Also we perform the projectile launching tests to achieve the trajectory of sabot in case of minimum and maximum thrust. Simulation results show that they are similar to real tests data, for example velocity, acceleration and the trajectory of sabot.