• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.55-63
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• 2019

In engineering problems, many random variables have correlation, and the correlation of input random variables has a great influence on reliability analysis results of the mechanical systems. However, correlated variables are often treated as independent variables or modeled by specific parametric joint distributions due to difficulty in modeling joint distributions. Especially, when there are insufficient correlated data, it becomes more difficult to correctly model the joint distribution. In this study, multivariate kernel density estimation with bounded data is proposed to estimate various types of joint distributions with highly nonlinearity. Since it combines given data with bounded data, which are generated from confidence intervals of uniform distribution parameters for given data, it is less sensitive to data quality and number of data. Thus, it yields conservative statistical modeling and reliability analysis results, and its performance is verified through statistical simulation and engineering examples.

• Smart Structures and Systems
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• v.31 no.2
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• pp.113-130
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• 2023

Hybrid simulation (HS) has attracted community attention in recent years as an efficient and effective experimental technique for structural performance evaluation in size-limited laboratories. Traditional hybrid simulations usually take deterministic properties for their numerical substructures therefore could not account for inherent uncertainties within the engineering structures to provide probabilistic performance assessment. Reliable structural performance evaluation, therefore, calls for stochastic hybrid simulation (SHS) to explicitly account for substructure uncertainties. The experimental design of SHS is explored in this study to account for uncertainties within analytical substructures. Both computational simulation and laboratory experiments are conducted to evaluate the pseudo-random Sobol sequence for the experimental design of SHS. Meta-modeling through polynomial chaos expansion (PCE) is established from a computational simulation of a nonlinear single-degree-of-freedom (SDOF) structure to evaluate the influence of nonlinear behavior and ground motions uncertainties. A series of hybrid simulations are further conducted in the laboratory to validate the findings from computational analysis. It is shown that the Sobol sequence provides a good starting point for the experimental design of stochastic hybrid simulation. However, nonlinear structural behavior involving stiffness and strength degradation could significantly increase the number of hybrid simulations to acquire accurate statistical estimation for the structural response of interests. Compared with the statistical moments calculated directly from hybrid simulations in the laboratory, the meta-model through PCE gives more accurate estimation, therefore, providing a more effective way for uncertainty quantification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12C
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• pp.1200-1208
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• 2003

Affective computing plays an important role in intelligent Human Computer Interactions(HCI). To detect emotional events, it is desirable to construct a computing model for extracting emotion related features from video. In this paper, we propose a statistical model based on the probabilistic distribution of low level features in video shots. The proposed method extracts low level features from video shots and then from a GMM(Gaussian Mixture Model) for them to detect emotional shots. As low level features, we use color, camera motion and sequence of shot lengths. The features can be modeled as a GMM by using EM(Expectation Maximization) algorithm and the relations between time and emotions are estimated by MLE(Maximum Likelihood Estimation). Finally, the two statistical models are combined together using Bayesian framework to detect emotional events in video.

• Steel and Composite Structures
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• v.20 no.4
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• pp.913-930
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• 2016

It is common practice to use Reduced Web Beam Sections (RWBS) in steel moment resisting frames. Perforation of beam web in these members may cause stress and strain concentration around the opening area and facilitate ductile fracture under cyclic loading. This paper presents a numerical study on the cyclic fracture of these structural components. The considered connections are configured as T-shaped assemblies with beams of elongated circular perforations. The failure of specimens under Ultra Low Cycle Fatigue (ULCF) condition is simulated using Cyclic Void Growth Model (CVGM) which is a micromechanics based fracture model. In each model, CVGM fracture index is calculated based on the stress and strain time histories and then models with different opening configurations are compared based on the calculated fracture index. In addition to the global models, sub-models with refined mesh are used to evaluate fracture index around the beam to column weldment. Modeling techniques are validated using data from previous experiments. Results show that as the perforation size increases, opening corners experience greater fracture index. This is while as the opening size increases the maximum observed fracture index at the connection welds decreases. However, the initiation of fracture at connection welds occurs at lower drift angles compared to opening corners. Finally, a probabilistic framework is applied to CVGM in order to account for the uncertainties existing in the prediction of ductile fracture and results are discussed.

• Earthquakes and Structures
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• v.11 no.2
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• pp.347-369
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• 2016

The main objective of this paper is the robust multi-objective optimization design of semi-active tuned mass damper (STMD) system using genetic algorithms and fuzzy logic. For optimal design of this system, it is required that the uncertainties which may exist in the system be taken into account. This consideration is performed through the robust design optimization (RDO) procedure. To evaluate the optimal values of the design parameters, three non-commensurable objective functions namely: normalized values of the maximum displacement, velocity, and acceleration of each story level are considered to minimize simultaneously. For this purpose, a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) approach is used to find a set of Pareto-optimal solutions. The torsional effects due to irregularities of the building and/or unsymmetrical placements of the dampers are taken into account through the 3-D modeling of the building. Finally, the comparison of the results shows that the probabilistic robust STMD system is capable of providing a reduction of about 52%, 42.5%, and 37.24% on the maximum displacement, velocity, and acceleration of the building top story, respectively.

• Journal of the Korean Society for Railway
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• v.12 no.1
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• pp.135-143
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• 2009

The majority of development projects fail to meet their target schedule and cost, with the overrun typically between 40 and 200 percent. These overruns happen because the planners underestimate the work or do not consider the need to rework at project planning. Representative schedule planning/management techniques such as Gantt Chart, PERT/CPM etc. that are used in domestic project planning are unable to reflect rework. This paper proposes a method to consider rework to provide more realistic estimates at schedule planning. Additionally, to prevent the underestimation of the work this paper proposes a simulation method that calculates a probabilistic estimated schedule and the associated variance based on the random variable modeling of individual task completion dates.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.306-309
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• 2004

The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the Geographic Information System (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat TM (Thermatic Mapper) satellite images; and the vegetation index value from SPOT HRV (High Resolution Visible) satellite images. Landslide hazardous areas were analysed and mapped using the landslide-occurrence factors employing the probability-frequency ratio method. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, land 'cover had relatively positive effects, and lithology had relatively negative effects on the landslide susceptibility maps in the study area. In addition, the landslide susceptibility maps using the all factors showed the relatively good results.

• Journal of Radiation Protection and Research
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• v.29 no.1
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• pp.41-48
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• 2004

A safety assessment code, called SAGE (Safety Assessment Groundwater Evaluation), has been developed to describe post-closure radionuclide releases and potential radiological doses for low- and intermediate-level radioactive waste (LILW) disposal in an engineered vault facility in Korea. The conceptual model implemented in the code is focused on the release of radionuclide from a gradually degrading engineered barrier system to an underlying unsaturated zone, thence to a saturated groundwater zone. The radionuclide transport equations are solved by spatially discretizing the disposal system into a series of compartments. Mass transfer between compartments is by diffusion/dispersion and advection. In all compartments, radionuclides ate decayed either as a single-member chain or as multi-member chains. The biosphere is represented as a set of steady-state, radionuclide-specific pathway dose conversion factors that are multiplied by the appropriate release rate from the far field for each pathway. The code has the capability to treat input parameters either deterministically or probabilistically. Parameter input is achieved through a user-friendly Graphical User Interface. An application is presented, which is compared against safety assessment results from the other computer codes, to benchmark the reliability of system-level conceptual modeling of the code.

• Journal of Communications and Networks
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• v.12 no.6
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• pp.582-591
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• 2010

Probabilistic modeling and analysis of correlation metrics have been receiving considerable interest for a long period of time because they can be used to evaluate the performance of communication receivers, including satellite broadcasting receivers. Although differential correlators have a simple structure and practical importance over channels with severe frequency offsets, closedform expressions for the output distribution of differential correlators do not exist. In this paper, we present detection error probability expressions for frame synchronization using differential correlation, and demonstrate their accuracy over channel parameters of practical interest. The derived formulas are presented in terms of the Marcum Q-function, and do not involve numerical integration, unlike the formulas derived in some previous studies. We first determine the distributions and error probabilities for single-span differential correlation metric, and then extend the result to multispan differential correlation metric with certain approximations. The results can be used for the performance analysis of various detection strategies that utilize the differential correlation structure.

• Journal of Korean Society of Disaster and Security
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• v.8 no.1
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• pp.15-19
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• 2015

In rainfall-runoff models employed in hydrological applications, runoff amount is estimated through temporal delay of effective precipitation based on a linear system. Its amount is resulted from the linearized ratio by analyzing the convolution multiplier. Furthermore, in case of kernel density estimate (KDE) used in probabilistic analysis, the definition of the kernel comes from the convolution multiplier. Individual data values are smoothed through the kernel to derive KDE. In the current study, the roles of the convolution multiplier for KDE and rainfall-runoff models were revisited and their similarity and dissimilarity were investigated to discover the mathematical applicability of the convolution multiplier.