• Steel and Composite Structures
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• v.41 no.5
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• pp.761-773
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• 2021

This study presents a 3D non-linear finite element (FE) assessment of dynamic soil-structure interaction (SSI). The numerical investigation has been performed on the time domain through a Finite Element (FE) system, while considering the nonlinear behavior of soil and the multi-directional nature of genuine seismic events. Later, the FE outcomes are analyzed to the recorded in-situ free-field and structural movements, emphasizing the numerical model's great result in duplicating the observed response. In this work, the soil response is simulated using an isotropic hardening elastic-plastic hysteretic model utilizing HSsmall. It is feasible to define the non-linear cycle response from small to large strain amplitudes through this model as well as for the shift in beginning stiffness with depth that happens during cyclic loading. One of the most difficult and unexpected tasks in resolving soil-structure interaction concerns is picking an appropriate ground motion predicted across an earthquake or assessing the geometrical abnormalities in the soil waves. Furthermore, an artificial neural network (ANN) has been utilized to properly forecast the non-linear behavior of soil and its multi-directional character, which demonstrated the accuracy of the ANN based on the RMSE and R² values. The total result of this research demonstrates that complicated dynamic soil-structure interaction processes may be addressed directly by passing the significant simplifications of well-established substructure techniques.

• Animal Bioscience
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• v.36 no.8
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• pp.1285-1292
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• 2023

Objective: The objective of this study was to develop a novel endolysin (PanLys.1) for the specific killing of the ruminal hyper-ammonia-producing bacterium Peptostreptococcus anaerobius (P. anaerobius). Methods: Whole genome sequences of P. anaerobius strains and related bacteriophages were collected from the National Center for Biotechnology Information database, and the candidate gene for PanLys.1 was isolated based on amino acid sequences and conserved domain database (CDD) analysis. The gene was overexpressed using a pET system in Escherichia coli BL21 (DE3). The lytic activity of PanLys.1 was evaluated under various conditions (dosage, pH, temperature, NaCl, and metal ions) to determine the optimal lytic activity conditions. Finally, the killing activity of PanLys.1 against P. anaerobius was confirmed using an in vitro rumen fermentation system. Results: CDD analysis showed that PanLys.1 has a modular design with a catalytic domain, amidase-2, at the N-terminal, and a cell wall binding domain, from the CW-7 superfamily, at the C-terminal. The lytic activity of PanLys.1 against P. anaerobius was the highest at pH 8.0 (p<0.05) and was maintained at 37℃ to 45℃, and 0 to 250 mM NaCl. The activity of PanLys.1 significantly decreased (p<0.05) after Mn²⁺ or Zn²⁺ treatment. The relative abundance of P. anaerobius did not decrease after administration PanLys.1 under in vitro rumen conditions. Conclusion: The application of PanLys.1 to modulate P. anaerobius in the rumen might not be feasible because its lytic activity was not observed in in vitro rumen system.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.33-40
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• 2000

The assessment of earthquake hazards involved consideration of earthquake magnitude, frequency, last movement and probability of occurrence. The appraisal of earthquake potential is feasible because historical data show a good correlation between earthquake size and the fault rupture parameters of length, displacement, and area. Additionally, the characteristics of fault behavior should be considered to evaluate the earthquake potential magnitude. In this study, in order to evaluate the earth quake potential by the fault behavioral patterns, based on the experimental background which the geometric characteristics of the individual domains, such as strike, width, fault tip patterns, and orientation of secondary shears reflect sliding behavioral patterns in each section, the straight sections of A, D and E domains were examined to the creeping section of stably sliding. In contrast, the curved section of B domain was examined to the locked section of stick-slip movement. These results of studies can be applied to evaluate the earthquake potential magnitude from the fault structural parameters.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1136-1141
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• 2004

This paper presents a control method for time-delay systems and verifies the performance of the designed control system via real experiments. Specifically, the control method is applied to a flexible-link system with time delays. The method combines time- and frequency-domain controllers: linear quadratic optimal controller (or LQR) and lag compensator. The LQR is used to stabilize the system in optimal fashion, whereas the lag compensator is used to compensate time-delay effects by increasing the delay margin of the system. With this methodology, the maximum allowable time delay can be increased significantly. The proposed method is simple but quite practical for time-delay system control as it is based on the conventional loop-shaping method, which gives practical insights on delay-phase relationship. Simulation and experiment results show that the method presented in this paper is feasible and practical.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.61-66
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• 2005

Spatiotemporal data mining represents the confluence of several fields including spatiotemporal databases, machine loaming, statistics, geographic visualization, and information theory. Exploration of spatial data mining and temporal data mining has received much attention independently in knowledge discovery in databases and data mining research community. In this paper, we introduce an algorithm Max_MOP for discovering moving sequences in mobile environment. Max_MOP mines only maximal frequent moving patterns. We exploit the characteristic of the problem domain, which is the spatiotemporal proximity between activities, to partition the spatiotemporal space. The task of finding moving sequences is to consider all temporally ordered combination of associations, which requires an intensive computation. However, exploiting the spatiotemporal proximity characteristic makes this task more cornputationally feasible. Our proposed technique is applicable to location-based services such as traffic service, tourist service, and location-aware advertising service.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.97-108
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• 2020

This paper compares several methods for estimating parameters of normal inverse Gaussian distribution. Ordinary maximum likelihood estimation and the method of moment estimation often do not work properly due to restrictions on parameters. We examine the performance of adjusted estimation methods along with the ordinary maximum likelihood estimation and the method of moment estimation by simulation and real data application. We also see the effect of the initial value in estimation methods. The simulation results show that the ordinary maximum likelihood estimator is significantly affected by the initial value; in addition, the adjusted estimators have smaller root mean square error than ordinary estimators as well as less impact on the initial value. With real datasets, we obtain similar results to what we see in simulation studies. Based on the results of simulation and real data application, we suggest using adjusted maximum likelihood estimates with adjusted method of moment estimates as initial values to estimate the parameters of normal inverse Gaussian distribution.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.249-261
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• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• Journal of Environmental Health Sciences
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• v.38 no.6
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• pp.550-560
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• 2012

Objectives: QSAR methodology was applied to explain two different sets of acceptable daily intake (ADI) data of 74 pesticides proposed by both the USEPA and WHO in terms of setting guidelines for food and drinking water. Methods: A subset of calculated descriptors was selected from Dragon$^{(R)}$ software. QSARs were then developed utilizing a statistical technique, genetic algorithm-multiple linear regression (GA-MLR). The differences in each specific model in the prediction of the ADI of the pesticides were discussed. Results: The stepwise multiple linear regression analysis resulted in a statistically significant QSAR model with five descriptors. Resultant QSAR models were robust, showing good utility across multiple classes of pesticide compounds. The applicability domain was also defined. The proposed models were robust and satisfactory. Conclusions: The QSAR model could be a feasible and effective tool for predicting ADI and for the comparison of logADIEPA to logADIWHO. The statistical results agree with the fact that USEPA focuses on more subtle endpoints than does WHO.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1198-1203
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• 2008

An efficient sequential optimization approach for metamodel was presented by Choi et al [6]. This paper describes a new approach of the multilevel optimization method studied in Refs. [5] and [21-25]. The basic idea is concerned with multilevel iterative methods which combine a descent scheme with a hierarchy of auxiliary problems in lower dimensional subspaces. After fitting a metamodel based on an initial space filling design, this model is sequentially refined by the expected improvement criterion. The advantages of the method are that it does not require optimum sensitivities, nonlinear equality constraints are not needed, and the method is relatively easy to understand and use. As a check on effectiveness, the proposed method is applied to a classical cantilever beam.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.3
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• pp.55-62
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• 1993

Autoregressive moving average(ARMA) model which is a time domain parametric modeling method is implemented for modeling and reproducing characteristics of exhaust noise of an automobile in various RPM range. Experiments have been carried out using 9 set of exhaust noise signals measured at 1,000-3,000 RPM range. Characteristics of sampled signals were estimated using ARMA modeling and Akaike's FPE(final prediction error) criterion to define exact model structure and for model validation. The digital filter consisted of the esitmated ARMA(70,1) model parameters was programed to reproduce exhaust noise. The spectral analysis of reproduced noise is very close to original. The results show that our approaching technique for reproducing acoustic characteristics is valid and feasible to apply in the field of noise quality control.