• Journal of the Korean Society of Safety
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• v.16 no.3
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• pp.99-105
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• 2001

This paper presents results km a series of model tests oil vertically loaded single piles to compare the behaviors of H and pipe piles under the same ground condition. The aims of this paper were to compare the bearing capacity of H-pile md pipe piles under in the same ground condition and to estimate the effect of gravity acceleration and relative soil density. Relative density of soil were made to be 40%, 80% and embedded length of pile on sand was increased by 10, 12, 14, 16 times of the diameter of pile, respectively. As a results of test series, allowable load of H-pile is from 6.4% to 18.2% larger than allowable load of pipe pile in relative density 80% and from 9.1% to 39.4% larger than allowable load of pipe pile in relative density 40%. As a results of numerical analysis, we were predicted behaviour of stress-displacement of pile with model test. In the case of relative density 80% and 40%, bearing capacity of H pile represent from 17.74% to 18.6% larger than allowable load of pipe pile.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1346-1349
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• 2010

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Journal of Ship and Ocean Technology
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• v.5 no.1
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• pp.38-54
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• 2001

A finite difference method for calculating turbulent flow and surface wave fields around a ship model is evaluated through the comparison with the experimental data of a Series 60 $C_B$=0.6 ship model. The method solves the Reynolds-averaged Navior-Stokes Equations using the non-staggered grid system, the four-stage Runge-Kutta scheme for the temporal integration of governing equations and the Bladwin-Lomax model for the turbulence closure. The free surface waves are captured by solving the equation of the kinematic free-surface condition using the Lax-Wendroff scheme and free-surface conforming grids are generated at each time step so that one of the grid surfaces coincides always with the free surface. The computational results show an overall close agreement with the experimental data and verify that the present method can simulate well the turbulent boundary layers and wakes as well as the free-surface waves.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• Smart Structures and Systems
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• v.22 no.2
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.278-285
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• 2012

Purpose: Weapon systems have the long life cycle unlike the consumer product. Thus, the reliability of weapon system is improved during the life cycle through the steady technical change. In this paper, we deal with the method of evaluating the reliability of weapon system with the field failure data. Methods: Especially, we present how to gather the field failure data and evaluate the reliability through the case of K-series weapon system. To evaluate reliability, the reliability growth model is used and the result is discussed. Results: It is steadily improved the reliability of K-series weapon system deployed from 2000 to 2004. The frequency of the failures that affect the mission is largely reduced and MTBMF(mean time between mission failure) is also improved. Conclusion: We can guess the trend of the reliability of weapon system with the field data through this study. Furthermore, it can be used to improve the reliability and make maintenance policy.

• International Journal of Contents
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• v.8 no.1
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• pp.23-29
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• 2012

Multiple expression levels of genes obtained using time series microarray experiments have been exploited effectively to enhance understanding of a wide range of biological phenomena. However, the unique nature of microarray data is usually in the form of large matrices of expression genes with high dimensions. Among the huge number of genes presented in microarrays, only a small number of genes are expected to be effective for performing a certain task. Hence, discounting the majority of unaffected genes is the crucial goal of gene selection to improve accuracy for disease diagnosis. In this paper, a non-Gaussian weight matrix obtained from an incremental model is proposed to extract useful features of multivariate time series microarrays. The proposed method can automatically identify a small number of significant features via discovering hidden variables from a huge number of features. An unsupervised hierarchical clustering representative is then taken to evaluate the effectiveness of the proposed methodology. The proposed method achieves promising results based on predictive accuracy of clustering compared to existing methods of analysis. Furthermore, the proposed method offers a robust approach with low memory and computation costs.

• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.2
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• pp.105-114
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• 2000

To chive a traveling wave type ultrasonic motor with the series""-resonant inverter, the external inductor is i lnserted between the motor and inverter. In $\psi$is paper, we proposed a novel 3Ilalysis method to design the 3 ~xternal inductor. An equivalent model of the ultrasonic motor is expressed, and a selection method of the , ;;eries inductor is proposed from the basis of the model. When the series inductor has an optimal value, it is v velified by computer simulation results that power is efficiently transmitted to the mechanical resonant c component in the motor. The frequency and speed characteristics of the ultrasonic motor are investigated by e experiments for several external inductors. The validity of the proposed method for selecting external inductor i is clarified.clarified.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.79-89
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• 2008

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

• Wind and Structures
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• v.32 no.3
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• pp.239-248
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• 2021

In this study, three airfoil families, NACA, FX and S, in each case three from each series with different shapes were investigated at different angles of attack using Computational Fluid Dynamics (CFD) method. To verify the CFD model, simulation results of the NACA 0012 airfoil was compared against the available experimental data and k-ω SST was used as the turbulence model. Lift coefficients, lift to drag ratios and pressure distributions around airfoils were obtained from the CFD simulations and compared each other. The simulations were performed at three Reynolds numbers, Re=2×105, 1×106and 2×106, and angle of attack was varied between -6 and 12 degrees. According to the results, similar lift coefficient values were obtained for symmetric airfoils reaching their maximum values at similar angles of attack. Maximum lift coefficients were obtained for FX 60-157 and S 4110 airfoils having lift coefficient values around 1.5 at Re=1×106 and 12 degrees of angle of attack. Flow separation occurred close to the leading edge of some airfoils at higher angles of attack, while some other airfoils were more successful in keeping the flow attached on the surface.