• Earthquakes and Structures
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• v.23 no.6
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• pp.549-580
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• 2022

This paper assesses the influences of modeling assumptions and uncertainties on the performance of the non-linear finite element (FE) model updating procedure and model clustering method. The results of a shaking table test on a four-story steel moment-resisting frame are employed for both calibrations and clustering of the FE models. In the first part, simple to detailed non-linear FE models of the test frame is calibrated to minimize the difference between the various data features of the models and the structure. To investigate the effect of the specified data feature, four of which include the acceleration, displacement, hysteretic energy, and instantaneous features of responses, have been considered. In the last part of the work, a model-based clustering approach to group models of a four-story frame with similar behavior is introduced to detect abnormal ones. The approach is a composition of property derivation, outlier removal based on k-Nearest neighbors, and a K-means clustering approach using specified data features. The clustering results showed correlations among similar models. Moreover, it also helped to detect the best strategy for modeling different structural components.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.287-287
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• 2023

Surface water-groundwater interaction (SWGI) is an important hydrological process that influences both the quantity and quality of water resources. However, regional scale SWGI model calibration and uncertainty analysis have been a challenge because integrated models inherently carry a vast number of parameters, modeling assumptions, and inputs, potentially leaving little time and budget to explore questions related to model performance and forecasting. In this study, we have proposed the application of iterative ensemble smoother (IES) for uncertainty analysis and calibration of the widely used integrated surface-subsurface model, SWAT-MODFLOW. SWAT-MODFLOW integrates Soil and Water Assessment Tool (SWAT) and a three-dimensional finite difference model (MODFLOW). The model was calibrated using a parameter estimation tool (PEST). The major advantage of the employed IES is that the number of model runs required for the calibration of an ensemble is independent of the number of adjustable parameters. The pilot point approach was followed to calibrate the aquifer parameters, namely hydraulic conductivity, specific storage, and specific yield. The parameter estimation process for the SWAT model focused primarily on surface-related parameters. The uncertainties both in the streamflow and groundwater level were assessed. The work presented provides valuable insights for future endeavors in coupled surface-subsurface modeling, data collection, model development, and informed decision-making.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.2
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• pp.163-170
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• 2008

The aeroelastic stability analysis of a soft-in-plane, composite hingeless rotor blade in hover and in forward flight has been performed by combining the mixed beam method and the aeroelastic analysis system that is based on a moderate deflection beam approach. The aerodynamic forces and moments acting on the blade are obtained using the Leishman-Beddoes unsteady aerodynamic model. Hamilton's principle is used to derive the governing equations of composite helicopter blades undergoing extension, lag and flap bending, and torsion deflections. The influence of key structural modeling issues on the aeroelastic stability behavior of helicopter blades is studied. The issues include the shell wall thickness, elastic couplings and the correct treatment of constitutive assumptions in the section wall of the blade. It is found that the structural modeling effects are largely dependent on the layup geometries adopted in the section of the blade and these affect on the stability behavior in a large scale.

• Journal of Biosystems Engineering
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• v.42 no.1
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• pp.44-55
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• 2017

Background: Heat recovery is one of the prominent ways to save a considerable amount of conventional fossil fuel and minimize its adverse effects on the environment. The rotary heat exchanger is one of the most effective and efficient devices for heat recovery or heat exchanging purposes. It is a regenerative type of heat exchanger, which has been studied and used for many heat recovery purposes. However, regenerative thermal wheels have been mostly used as heat recovery systems in buildings. For modeling a rotary regenerator, it is very important to numerically consider all the factors involved, such as effectiveness, rotational speed, geometrical size and shape, and pressure drop (${\Delta}p$). In recent times, several researchers have actively studied the rotary heat exchangers, both theoretically and experimentally. Reviews: In this paper different advances in the numerical modeling of regenerative rotary heat exchangers in relation to fluid flow and heat transfer have been discussed. Researchers have indicated that the effectiveness of the regenerative rotary heat exchanger depends on various factors including, among many others, rotational speed, rotational period and combustion power. It is reported that with the increase of periodic rotation the deviation of theoretical results from the experimental result increases. The available literature indicates that regenerative heat exchangers are having relatively more effectiveness (60-80%), compared to other heat exchangers. It is also observed that the finite difference method and finite volume methods are mostly used for discretizing the heat transfer governing equations, under some assumptions. Research also indicates that for the effectiveness calculation the ${\varepsilon}-NTU$ method is the most popular and convenient.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.157-165
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• 2011

It is challenging to detect foreground objects when background includes an illumination variation, shadow or structural variation due to its motion. Basically pixel-based background models including codebook-based modeling suffer from statistical randomness of each pixel. This paper proposes an algorithm that incorporates Markov random field model into pixel-based background modeling to achieve more accurate foreground detection. Under the assumptions the distance between the pixel on the input imaging and the corresponding background model and the difference between the scene estimates of the spatio-temporally neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameters is proposed. The proposed method alternates between estimating the parameters with the intermediate foreground detection and estimating the foreground detection with the estimated parameters, after computing it with random initial parameters. Extensive experiment is conducted with several videos recorded both indoors and outdoors to compare the proposed method with the standard codebook-based algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2603-2609
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• 2014

Reliability analysis of mechanical systems requires statistical modeling of input random variables such as distribution function types and statistical parameters that affect the performance of the mechanical systems. Some random variables are correlated, but considered as independent variables or wrong assumptions on input random variables have been used. In this paper, joint distributions were modeled using copulas and Bayesian method from limited number of data. To verify the proposed method, statistical simulation tests were carried out for various number of samples and correlation coefficients. As a result, the Bayesian method selected the most probable copula types among candidate copulas even though the candidate copula shapes are similar for low correlations or the number of data is limited. The most probable copulas also yielded similar reliabilities with the true reliability obtained from a true copula, so that it can be concluded that the Bayesian method provides accurate statistical modeling for the reliability analysis.

• Journal of Aerospace System Engineering
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• v.16 no.6
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• pp.8-17
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• 2022

This paper presents a novel geometric modeling technique to predict the mechanical properties of an aircraft wing's skin-stringer integrated panel. Due to mechanical and adhesive fastening, this panel is vulnerable to stress concentration and debonding, so we designed it to integrate the skin and stringer using three-dimensional woven composites. Geometric modeling was conducted by measuring the geometric parameters of the specimen and defining the pattern of the yarns as functions. We used a weighted average model with iso-strain and iso-stress assumptions to predict the mechanical properties of the panel parts. We then compared the results of a finite element analysis with a compression test to verify the accuracy of our model. Our proposed technique proved to be more efficient than the traditional experimental method for predicting the mechanical properties of skin-stringer integrated panels.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.62-64
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• 1996

Two current approaches for modeling the vector magnetic hysteretic process are the vector Preisach models and those models based on a system of noninteracting pseudo-particles. The pseudo-particles are intended to mimic the average behavior of real media particles. The simplest switching mechanisms of pseudoparticles is the Stoner-Wholfarth model. The Preisach models are quite precise in specifying the experimental input to the models. The vector properties of the Preisach models are, however, inadequate. This is partly because of the questionable assumptions used in coupling the various vector hysteresis components. Also these models do not include reversible magnetization changes. Unlike Preisach counterpart, the Stoner-Wholfarth model is inherently vector in nature. This is because spatial distribution and switching mechanisms are imposed on the system of pseudo-particles, so they come closer to representing the physical reality. The lack of interaction between pseudo-particles exclude the usefulness of the Stoner-Wholfarth model for small fields when the medium is traversing minor loops. The present work is an attempt at combining the advantages of above two models into one composite model, including the effect of particle interaction.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.7
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• pp.1351-1362
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• 1992

Qualitative models in model-based expert system needs modeling paradigm which provides intelligent control of modeling assumptions and extracts robust inferences without quantitative information about the system to be modeled. Qualitative reasoning methodologies has proved the property of the completeness but not the soundness to the corresponding quantitative model. We propose new methodology of qualitative reasoning by introducing the concept of Centroid-Oriented Composite Interval to improve the soundness problem. Arithmetic operations and equivalence classes were composed using this definition. Qualitative simulation results were compared to Kuipers's results and the improvements in the soundness problem is verified.