• Journal of Ocean Engineering and Technology
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• v.33 no.5
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• pp.470-478
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• 2019

Because the global positioning system (GPS) is not available in underwater environments, an inertial navigation system (INS)/doppler velocity log (DVL) integrated navigation system is generally implemented. In general, an INS/DVL integrated system adopts a loosely coupled method. However, in this loosely coupled method, although the measurement equation for the filter design is simple, the velocity of the body frame cannot be accurately measured if even one of the DVL transducer signals is not received. In contrast, even if only one or two velocities are measured by the DVL transducers, the tightly coupled method can utilize them as measurements and suppress the error increase of the INS. In this paper, a filter was designed to regenerate the measurements of failed transducers by taking advantage of the tightly coupled method. The regenerated measurements were the normal DVL transducer measurements and the estimated velocity in RPM. In order to effectively estimate the velocity in RPM, a filter was designed considering the effects of the tide. The proposed filter does not switch all of the measurements to RPM if the DVL transducer fails, but only switches information from the failed transducer. In this case, the filter has the advantage of being able to be used as a measurement while continuously estimating the RPM error state. A Monte Carlo simulation was used to determine the performance of the proposed filters, and the scope of the analysis was shown by the standard deviation ($1{\sigma}$, 68%). Finally, the performance of the proposed filter was verified by comparison with the conventional tightly coupled method.

• Journal of the Korean Society of Radiology
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• v.13 no.1
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• pp.73-80
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• 2019

Neutrons are generated by the nuclear reaction, which is absorbed into the concrete wall and causes the activation during cyclotron operation. The purpose of this study is to investigate the effect of neutron activation and radiative concrete on concrete type. This experiment used Monte Carlo simulation and RESRAD model. The results of the experiment showed that the higher the content of Fe in concrete, the greater the shielding rate. The effect of $^{56}Fe(n,\;2np)^{54}Mn$ reaction on workers is also increased. However, radioactive nuclides have low activity and have very low impact on workers. Radioactive concrete should be treated as general wastes with less than its self-disposal tolerance level, and it should be recycled to the surface such as road repair rather than landfill to minimize the effect of $^{14}C$.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.3
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• pp.169-176
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• 2019

In the present study, a CFD/DSMC hybrid method performed by a coupled analysis between the CFD method and the DSMC method was developed to obtain the flow information on the rarefied gas flows effectively. Flow simulations around the high speed vehicles on the transition flow regimes were conducted by using the developed method. The FRESH-FX vehicle made of cone and cylinder shapes was considered for the simulations. The results of the hybrid method were compared with the results of the pure CFD and the pure DSMC method to confirm the reliability and efficiency of the hybrid method. It was found that the gradient and the intensity of the shock waves were weakened due to the relatively low density on the transition flow regime. It was confirmed that the results of the hybrid analysis were different to those of the pure CFD analysis and almost identical to those of the pure DSMC analysis. In addition, the computational time of the hybrid method was reduced than that of the pure DSMC method. As a result, it was obtained that the validity and the efficiency of the CFD/DSMC hybrid method.

• Geomechanics and Engineering
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• v.17 no.1
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• pp.31-45
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• 2019

In this study the spatial variability of soils is substantiated physically and numerically by using random field theory. Heterogeneous samples are fabricated by combining nine homogeneous soil clusters that are assumed to be elements of an adopted random field. Homogeneous soils are prepared by mixing different percentages of kaolin and bentonite at water contents equivalent to their respective liquid limits. Comprehensive characteristic laboratory tests were carried out before embarking on direct shear experiments to deduce the basic correlations and properties of nine homogeneous soil clusters that serve to reconstitute the heterogeneous samples. The tests consist of Atterberg limits, and Oedometric and unconfined compression tests. The undrained shear strength of nine soil clusters were measured by the unconfined compression test data, and then correlations were made between the water content and the strength and stiffness of soil samples with different consistency limits. The direct shear strength of heterogeneous samples of different stochastic properties was then evaluated by physical and numerical modelling using FISH code programming in finite difference software of $FLAC^{3D}$. The results of the experimental and stochastic numerical analyses were then compared. The deviation of numerical simulations from direct shear load-displacement profiles taken from different sources were discussed, potential sources of error was introduced and elaborated. This study was primarily to explain the mathematical and physical procedures of sample preparation in stochastic soil mechanics. It can be extended to different problems and applications in geotechnical engineering discipline to take in to account the variability of strength and deformation parameters.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.415-426
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• 2019

In this paper, we discussed automatic target tracking algorithms for diver detection sonar that observes penetration forces of coastal military installations and major infrastructures. First of all, we analyzed sea trial data in diver detection sonar and composed automatic target tracking algorithms based on track existence probability as track quality measure in clutter environment. In particular, these are presented track management algorithms which include track initiation, confirmation, termination, merging and target tracking algorithms which include single target tracking IPDAF (Integrated Probabilistic Data Association Filter) and multitarget tracking LMIPDAF (Linear Multi-target Integrated Probabilistic Data Association Filter). And we analyzed performances of automatic target tracking algorithms using sea trial data and monte carlo simulation data.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2813-2827
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• 2018

Progressive censoring schemes have become quite popular in reliability study. Under progressive censored data, however, some units can be failed between two points of observation with exact times of failure of these units unobserved. For example, loss may arise in life-testing experiments when the failure times of some units were not observed due to mechanical or experimental difficulties. Therefore, multiply progressive censoring scheme was introduced. So, we derives a maximum likelihood estimator of the parameter of exponential distribution. And we introduced the goodness-of-fit test statistics using order statistic and Lorenz curve. We carried out Monte Carlo simulation to compare the proposed test statistics. In addition, real data set have been analysed. In Weibull and chi-squared distributions, the test statistics using Lorenz curve are more powerful than test statistics using order statistics.

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1764-1773
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• 2018

A-PET is a quad-head PET scanner developed for use in small-animal imaging. The dimensions of its volumetric field of view (FOV) are $46.1{\times}46.1{\times}46.1mm^3$ and the gap between the detector modules has been minimized in order to provide a highly sensitive system. However, such a small FOV together with the quad-head geometry causes image quality degradation. The main factor related to image degradation for the quad-head PET is the mispositioning of events caused by the penetration effect in the detector. In this paper, we propose a precise method for modelling the system at the high spatial resolution of the A-PET using a LOR (line of response) based ML-EM (maximum likelihood expectation maximization) that allows for penetration effects. The proposed system model provides the detection probability of every possible ray-path via crystal sampling methods. For the ray-path sampling, the sub-LORs are defined by connecting the sampling points of the crystal pair. We incorporate the detection probability of each sub-LOR into the model by calculating the penetration effect. For comparison, we used a standard LOR-based model and a Monte Carlo-based modeling approach, and evaluated the reconstructed images using both the National Electrical Manufacturers Association NU 4-2008 standards and the Geant4 Application for Tomographic Emission simulation toolkit (GATE). An average full width at half maximum (FWHM) at different locations of 1.77 mm and 1.79 mm are obtained using the proposed system model and standard LOR system model, which does not include penetration effects, respectively. The standard deviation of the uniform region in the NEMA image quality phantom is 2.14% for the proposed method and 14.3% for the LOR system model, indicating that the proposed model out-performs the standard LOR-based model.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.723-738
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• 2020

In this paper, we study a method to discriminate between trend stationary and difference stationary processes. Since a crucial ingredient of this discrimination is to determine the existence of unit root, we can use a unit root testing strategy. So, we introduce a discrimination based on unit root testing and propose the method using the adaptive lasso. Our Monte Carlo simulation experiments show that the adaptive lasso improves the discrimination accuracy when the process is trend stationary, but has lower accuracy than unit root strategy where the process is difference stationary.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.187-199
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• 2022

Kriging metamodel, as a flexible machine learning method for approximating deterministic analysis models of an engineering system, has been widely used for efficiently estimating slope reliability in recent years. However, the autocorrelation function (ACF), a key input to Kriging that affects the accuracy of reliability estimation, is usually selected based on empiricism. This paper proposes an adaption of the Kriging method, named as Genetic Algorithm optimized Whittle-Matérn Kriging (GAWMK), for addressing this issue. The non-classical two-parameter Whittle-Matérn (WM) function, which can represent different ACFs in the Matérn family by controlling a smoothness parameter, is adopted in GAWMK to avoid subjectively selecting ACFs. The genetic algorithm is used to optimize the WM model to adaptively select the optimal autocorrelation structure of the GAWMK model. Monte Carlo simulation is then performed based on GAWMK for a subsequent slope reliability analysis. Applications to one explicit analytical example and two slope examples are presented to illustrate and validate the proposed method. It is found that reliability results estimated by the Kriging models using randomly chosen ACFs might be biased. The proposed method performs reasonably well in slope reliability estimation.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.