• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.387-391
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• 2008

For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

• Nuclear Engineering and Technology
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• v.35 no.2
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• pp.108-120
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• 2003

In assessing the long term post closure radiological safety assessment of a potential HLW repository in Korea, three categories of uncertainties exist. The first one is the scenario uncertainty where series of different natural events are translated into written statements. The second one is the modeling uncertatinty where different mathematical models are applied for an identical scenario. The last one is the data uncertainty which can be expressed in terms of probabilistic density functions. In this analysis, three different scenarios are seleceted; a small well scenario, a radiolysis scenario, and a naturally discharged scenario. The MASCOT-K and the AMBER, probabilistic safety assessment codes based on connection of sub-modules and a compartment theory respectively, are applied to assess annual individual doses for a generic biosphere. Results illustrate that for a given scenario, predictions from two different codes fairly match well each other But the discrepancies for the different scenarios are significant. However, total doses are still well below the guideline of 2 mRem/yr. Detailed analyses with model and data uncertainties are underway to further assure the safety of a Korean reference dispsoal concept.

• Wind and Structures
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• v.27 no.6
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• pp.417-430
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• 2018

The concentration fields in the proximities of a local gas emission source are experimentally analyzed in several combinations of wind incidences and source emissions. These conditions are determined by the plume buoyancy, emission velocity and incident flow wind speed. Concentration measurements are performed by an aspirating probe in a boundary layer wind tunnel. The analysis included the mean concentration values and the intensity of concentration fluctuations in a neutral atmospheric boundary layer flow. Different configurations are tested: an isolated stack in a homogeneous terrain and a stack with a bluff body in close proximity, located windward and leeward from the emission source. The experimental mean concentration values are contrasted with Gaussian profiles and the dilution factor is analyzed with respect to the empirical curves of the minimum dilution. Finally, a study on the plume intermittency is performed in a cross-sectional plane near the emission source. It is possible to highlight the following observations: a) plume vertical asymmetry in the case of an isolated emission source, b) significant differences in the dispersion process related to the relative location of the emission source and bluff body effects, and c) different probabilistic behavior of the concentration fluctuation data in a cross-sectional measurement plane inside the plume.

• Wind and Structures
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• v.28 no.4
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• pp.225-238
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• 2019

In this paper a novel and efficient computational framework to estimate the stress range versus number of cycles curves experienced by a cable due to external excitations (e.g., seismic excitations, traffic and wind-induced vibrations, among others) is proposed. This study is limited to the wind-cable interaction governed by the Vortex Shedding mechanism which mainly rules cables vibrations at low amplitudes that may lead to their failure due to bending fatigue damage. The algorithm relies on a stochastic approach to account for the uncertainties in the cable properties, initial conditions, damping, and wind excitation which are the variables that govern the wind-induced vibration phenomena in cables. These uncertainties are propagated adopting Monte Carlo simulations and the concept of importance sampling, which is used to reduce significantly the computational costs when new scenarios with different probabilistic models for the uncertainties are evaluated. A high fidelity cable model is also proposed, capturing the effect of its internal wires distribution and helix angles on the cables stress. Simulation results on a 15 mm diameter high-strength steel strand reveal that not accounting for the initial conditions uncertainties or using a coarse wind speed discretization lead to an underestimation of the stress range experienced by the cable. In addition, parametric studies illustrate the computational efficiency of the algorithm at estimating new scenarios with new probabilistic models, running 3000 times faster than the base case.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.1199-1203
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• 2005

Combined sewer overflows(CSOs) are themselves a significant source of water pollution. Therefore, the control of urban drainage for CSOs reduction and receiving water quality protection is needed. Examples in combined sewer systems include downstream storage facilities that detain runoff during periods of high flow and allow the detained water to be conveyed by an interceptor sewer to a centralized treatment plant during periods of low flow. The design of such facilities as stormwater detention storage is highly dependant on the temporal variability of storage capacity available(which is influenced by the duration of interevent dry periods) as well as the infiltration capacity of soil and recovery of depression storage. As a result, a contiunous approach is required to adequately size such facilities. This study for the continuous long-term analysis of urban dranage system used analytical Probabilistic model based on derived probability distribution theory. As an alternative to the modeling of urban drainage system for planning or screening level analysis of runoff control alternatives, this model have evolved that offer much ease and flexibility in terms of computation while considering long-term meteorology. This study presented rainfall and runoff characteristics or the subject area using analytical Probabilistic model. Runoff characteristics manifasted the unique characteristics of the subject area with the infiltration capacity of soil and recovery of depression storage and was examined appropriately by sensitivity analysis. This study presented the average annual COSs and number of COSs when the interceptor capacity is in the range 3xDWF(dry weather flow). Also, calculated the average annual mass of pollutant lost in CSOs using Event Mean Concentration. Finally, this study presented a dicision of storage volume for CSOs reduction and water quality protection.

• Journal of Korean Society on Water Environment
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• v.22 no.6
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• pp.1068-1074
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• 2006

For receiving water quality protection a control systems of urban drainage for CSOs reduction is needed. Examples in combined sewer systems include downstream storage facilities that detain runoff during periods of high flow and allow the detained water to be conveyed by an interceptor sewer to a centralized treatment plant during periods of low flow. The design of such facilities as storm-water detention storage is highly dependant on the temporal variability of storage capacity available as well as the infiltration capacity of soil and recovery of depression storage. For the continuous long-term analysis of urban drainage system this study used analytical probabilistic model based on derived probability distribution theory. As an alternative to the modeling of urban drainage system for planning or screening level analysis of runoff control alternatives, this model has evolved that offers much ease and flexibility in terms of computation while considering long-term meteorology. This study presented rainfall and runoff characteristics of the subject area using analytical probabilistic model. Runoff characteristics manifested the unique characteristics of the subject area with the infiltration capacity of soil and recovery of depression storage and was examined appropriately by sensitivity analysis. This study presented the average annual CSOs, number of CSOs and event mean CSOs for the decision of storage volume.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.61-69
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• 2010

The collapse of a slope or cut embankment brings much damage to life and property. Accordingly, it is very important to analyze the spatial distribution by calculating the landslide susceptibility in the estimation of the risk of landslide occurrence. The heuristic, statistic, deterministic, and probabilistic methods have been introduced to make landslide susceptibility maps. In many cases, however, the reliability is low due to insufficient field data, and the qualitative experience and knowledge of experts could not be combined with the quantitative mechanical?analysis model in the existing methods. In this paper, new modeling method for a probabilistic landslide susceptibility analysis combined Bayesian Network with ontology model about experts' knowledge and spatial data was proposed. The ontology model, which was made using the reasoning engine, was automatically converted into the Bayesian Network structure. Through conditional probabilistic reasoning using the created Bayesian Network, landslide susceptibility with uncertainty was analyzed, and the results were described in maps, using GIS. The developed Bayesian Network was then applied to the test-site to verify its effect, and the result corresponded to the landslide traces boundary at 86.5% accuracy. We expect that general users will be able to make a landslide susceptibility analysis over a wide area without experts' help.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.1
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• pp.48-55
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• 2016

The assessment and prediction of service life of a structure are usually under uncertainty so that rational probabilistic concepts and methods have to be applied. Based on these rational assessment and prediction, optimum maintenance strategies to minimize the life-cycle cost and/or maximize the structural safety can be established. The service life assessment and prediction considering maintenance actions generally includes effects of maintenance types and times of the structural components on the service life extensions of structural system. Existing researches on the service life management have revealed the appropriate system modeling considering the correlation among the components is required for system reliability analysis and probabilistic service life estimation. However, the study on correlation among the maintenance actions is still required. This paper deals with such a study for more effective and efficient service life management. In this paper, both the preventive and essential maintenances are considered for the extended service life estimation and management.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.19-26
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• 2005

Image interpolation in the wavelet domain usually takes advantage of the probabilistic models for the intrascale statistics and the interscale dependency. In this paper, we adopt the linear model for the absolute values of wavelet coefficients of interpolated image across scale to estimate the variances of extrapolated bands. The proposed algorithm uses randomly generated wavelet coefficients based on the estimated parameters for probabilistic model. Random number generation according to the estimated probabilistic model may induce the 'salt and pepper' noise in subbands. We reduce the noise power by Wiener filtering. We observe that the proposed method generates the histogram of the subband coefficients similar to the that of original image. Experimental results show that our method outperforms the previous wavelet-domain interpolation method as well as the conventional bicubic method.

• Journal of Korean Society on Water Environment
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• v.23 no.4
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• pp.490-497
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• 2007

A Combined sewer overflows (CSOs) are themselves a significant source of water pollution. Therefore, the control of urban drainage for CSOs reduction and receiving water quality protection is needed. Examples in combined sewer systems include downstream storage facilities that detain runoff during periods of high flow and allow the detained water to be conveyed by an interceptor sewer to a centralized treatment plant during periods of low flow. The design of such facilities as stormwater detention storage is highly dependant on the temporal variability of storage capacity available (which is influenced by the duration of interevent dry periods) as well as the infiltration capacity of soil and recovery of depression storage. As a result, a continuous approach is required to adequately size such facilities. This study for the continuous long-term analysis of urban drainage system used analytical probabilistic model based on derived probability distribution theory. As an alternative to the modeling of urban drainage system for planning or screening level analysis of runoff control alternatives, this model have evolved that offer much ease and flexibility in terms of computation while considering long-term meteorology. This study presented rainfall and runoff characteristics of the subject area using analytical probabilistic model. This study presented the average annual COSs and number of COSs when the interceptor capacity is in the range $3{\times}DWF$ (dry weather flow). Also, calculated the average annual mass of pollutant lost in CSOs using Event Mean Concentration. Finally, this study presented a decision of storage volume for CSOs reduction and water quality protection.