• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.6
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• pp.1510-1517
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• 1995

A theoretical method is described to simulate the propagation of turbulent premixed flames in a closed vessel. The objective is to develop and test an efficient technique to predict the propagation speed of flame as well as the geometric structure of the flame surfaces. Flame is advected by the statistically generated turbulent flow field and propagates as a wave by solving twodimensional Hamilton-Jacobi equation. In the simulation of the unburned gas flow field, following turbulence properties were satisfied: mean velocity field, turbulence intensities, spatial and temporal correlations of velocity fluctuations. It is assumed that these properties are not affected by the expansion of the burned gas region. Predictions were compared with existing experimental data for flames propagating in a closed vessel charged with hydrogen/air mixture with various turbulence intensities and Reynolds numbers. Comparisons were made in flame radius growth rate, rms flame radius fluctuations, and average perimeter and fractal dimensions of the flame boundaries. Two dimensional time dependent simulation resulted in correct trends of the measured flame data. The reasonable behavior and high efficiency proves the usefulness of this method in difficult problems of flame propagation such as in internal combustion engines.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.18 no.1
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• pp.63-68
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• 2006

Time series of waterline changes during a flood/ebb cycle can be utilized for supplementary data for measuring bottom topography. The waterlines extracted from consecutive images are substituted for depth contours using water level data. The distances between contours are quantified through a rectification image process. This technique is applied to the Keunpoolan beach in the Daeijak Island near Incheon. A camera monitoring technique supported by natural water level changes produces bottom topography with high precision. It is also less time consuming and more economical. The technique also can be utilized effectively to the physical modeling f3r measuring bottom changes in the three dimensional basin.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.9 no.6
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• pp.761-767
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• 1998

The Perfectly Matched Layer(PML) provide good performance in absorption over a wide frequency range and is an appropriate ABC for waveguides with high dispersion. In this paper, a novel algorithm is proposed to improve the computational efficiency of the PML. In the input and output ports, the fields are decomposed into a series of modes, and then an appropriate ABC is applied to each mode. CPU time and memory storage requirements are greatly reduced, since the computational region is analyzed in one dimension. A WG-90 rectangular waveguide with a thick asymmetric iris is analyzed by Finite-Difference Time-Domain(FDTD) simulations with the conventional PML and the proposed one-dimensional (1-D) PML. Numerical results show that the computational efficiency is significantly improved by the proposed method.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1545-1553
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• 2018

Recent eruptive activity at Kīlauea Volcano started on at the end of April in 2018 showed rapid ground deflation between May and June in 2018. On summit area Halema'uma'u lava lake continued to drop at high speed and Kīlauea's summit continued to deflate. GPS receivers and electronic tiltmeters detected the surface deformation greater than 2 meters. We explored the time-series surface deformation at Kīlauea Volcano, focusing on the early stage of eruptive activity, using multi-temporal COSMO-SkyMed SAR imagery. The observed maximum deformation in line-of-sight (LOS) direction was about -1.5 meter, and it indicates approximately -1.9 meter in subsiding direction by applying incidence angle. The results showed that summit began to deflate just after the event started and most of deformation occurred between early May and the end of June. Moreover, we confirmed that summit's deflation rarely happened since July 2018, which means volcanic activity entered a stable stage. The best-fit magma source model based on time-series surface deformation demonstrated that magma chambers were lying at depths between 2-3 km, and it showed a deepening trend in time. Along with the change of source depth, the center of each magma model moved toward the southwest according to the time. These results have a potential risk of including bias coming from single track observation. Therefore, to complement the initial results, we need to generate precise magma source model based on three-dimensional measurements in further research.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.685-701
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• 2022

FDR is widely used in high-dimensional data inference since it provides more liberal criterion contrary to FWER which is known to be very conservative by controlling Type-1 errors. This paper proposes a sparse VHAR model estimation method controlling FDR by adapting the knockoff introduced by Barber and Candès (2015). We also compare knockoff with conventional method using adaptive Lasso (AL) through extensive simulation study. We observe that AL shows sparsistency and decent forecasting performance, however, AL is not satisfactory in controlling FDR. To be more specific, AL tends to estimate zero coefficients as non-zero coefficients. On the other hand, knockoff controls FDR sufficiently well under desired level, but it finds too sparse model when the sample size is small. However, the knockoff is dramatically improved as sample size increases and the model is getting sparser.

• Journal of the Korean Institute of Navigation
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• v.24 no.5
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• pp.443-450
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• 2000

Before some experiments were carried out with analog bandpass filter which used for filtering the noise included in sound source signal. And this filter was constituted by condenser, register and operational amplifier. Hut these elements made the phase characteristics to differentiate in each sensing channel and cause a little of measurement error. We made new measurement system that was substituted digital filter for the analog filter in order to develop the optimal system which could find the time delay between each sensors with high accuracy. This paper describes the new system's constitution and the function of each parts. Specially three digital filters were designed and applied to the digital signal processing Part. And a series of experiments were carried out with the source＇s distance 9.53meters and the random bearing interval within the limits of $0^{\circ}$ ~ $180^{\circ}$. As a result, we have recognized that the accuracy of measurements were differentiated by the methods what kind of digital filter were adopted. And we have confirmed the facts that IIR LPF was suitable for sound source's bearing measurement and FIR LPF reduced the range measurement error.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.419-425
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• 2010

Kernel machine learning is gaining a lot of popularities in analyzing large or high dimensional nonlinear data. We use this technique to estimate a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we show that GARCH models can be estimated using kernel machine learning and that kernel machine has a higher predicting ability than ML methods and support vector machine, when estimating volatility of financial time series data with fat tail.

• Computers and Concrete
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• v.20 no.5
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• pp.583-593
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• 2017

Applications of fibre-reinforced polymer (FRP) composites for retrofitting, strengthening and repairing concrete structures have been expanded dramatically in the last decade. FRPs have high specific strength and stiffness compared to conventional construction materials, e.g., steel. Ease of preparation and installation, resistance to corrosion, versatile fabrication and adjustable mechanical properties are other advantages of the FRPs. However, there are major concerns about long-term performance, serviceability and durability of FRP applications in concrete structures. Therefore, structural health monitoring (SHM) and damage detection in FRP-retrofitted concrete structures need to be implemented. This paper presents a study on investigating the application of Rayleigh wave for detecting debonding defect in FRP-retrofitted concrete structures. A time-of-flight (ToF) method is proposed to determine the location of a debonding between the FRP and concrete using Rayleigh wave. A series of numerical case studies are carried out to demonstrate the capability of the proposed debonding detection method. In the numerical case studies, a three-dimensional (3D) finite element (FE) model is developed to simulate the Rayleigh wave propagation and scattering at the debonding in the FRP-retrofitted concrete structure. Absorbing layers are employed in the 3D FE model to reduce computational cost in simulating the practical size of the FRP-retrofitted structure. Different debonding sizes and locations are considered in the case studies. The results show that the proposed ToF method is able to accurately determine the location of the debonding in the FRP-retrofitted concrete structure.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.61-66
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• 2022

Object classification is one of the main fields in neural networks and has attracted the interest of many researchers. Although there have been vast advancements in this area, still there are many challenges that are faced even in the current era due to its inefficiency in handling large data, linguistic and dimensional complexities. Powerful hardware and software approaches in Neural Networks such as Deep Neural Networks present efficient mechanisms and contribute a lot to the field of object recognition as well as to handle time series classification. Due to the high rate of accuracy in terms of prediction rate, a neural network is often preferred in applications that require identification, segmentation, and detection based on features. Neural networks self-learning ability has revolutionized computing power and has its application in numerous fields such as powering unmanned self-driving vehicles, speech recognition, etc. In this paper, the experiment is conducted to implement a neural approach to identify numbers in different formats without human intervention. Measures are taken to improve the efficiency of the machines to classify and identify numbers. Experimental results show the importance of having training sets to achieve better recognition accuracy.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.