• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1990.04a
• /
• pp.31-36
• /
• 1990

The probabilistic characteristics of wind loads have been analyzed using statistical data on wind speeds, pressure coefficient, exposure coefficient, and gust factor. The wind speed data collected in 25 nationwide weather stations have been modified to be consistent in measuring height, exposure condition as well as averaging time, Having performed Monte Carlo simulation for various heights and site conditions, the statistical models of wind loads were determined, in which Type-I extreme value distribution has been applied. The models also incorporate a reduction factor of 0.85 to account for the reduced probability that the maximum wind speed will occur in a direction most unfavorable to the response of structure.

• Journal of the Korea Society for Simulation
• /
• v.9 no.3
• /
• pp.65-75
• /
• 2000

In recent years, significant advances in computers and communication technologies have made multimedia services feasible. As a result, various queuing models and cost models on architecture and data placement for multimedia server have been proposed. However, these analytical techniques use only probabilistic models to represent the behavior of a system, and then they have several limitations like accuracy. Simulation is a viable alternative to analytical model. It avoids many of the limitations associated with analytical techniques, allowing for more precise representation of system attributes like workload in program code. In this paper, we propose a simulation test bed that can evaluate performance of real-time multimedia server by using simulation model.

• Proceedings of the KOSOMBE Conference
• /
• v.1991 no.05
• /
• pp.65-70
• /
• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• Computers and Concrete
• /
• v.26 no.2
• /
• pp.127-136
• /
• 2020

There is an inherent randomness for concrete microstructure even with the same manufacturing process. Meanwhile, the concrete material under the aqueous environment is usually not fully saturated by water. This study aimed to develop a stochastic micromechanical framework to investigate the probabilistic behavior of the unsaturated concrete from microscale level. The material is represented as a multiphase composite composed of the water, the pores and the intrinsic concrete (made up by the mortar, the coarse aggregates and their interfaces). The differential scheme based two-level micromechanical homogenization scheme is presented to quantitatively predict the concrete's effective properties. By modeling the volume fractions and properties of the constituents as stochastic, we extend the deterministic framework to stochastic to incorporate the material's inherent randomness. Monte Carlo simulations are adopted to reach the different order moments of the effective properties. A distribution-free method is employed to get the unbiased probability density function based on the maximum entropy principle. Numerical examples including limited experimental validations, comparisons with existing micromechanical models, commonly used probability density functions and the direct Monte Carlo simulations indicate that the proposed models provide an accurate and computationally efficient framework in characterizing the material's effective properties. Finally, the effects of the saturation degrees and the pore shapes on the concrete macroscopic probabilistic behaviors are investigated based on our proposed stochastic micromechanical framework.

• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.721-725
• /
• 2004

Multiple-count problem is occurred when rectangle objects span across several buckets. The Cumulative Density (CD) histogram is a technique which solves multiple-count problem by keeping four sub-histograms corresponding to the four points of rectangle. Although it provides exact results with constant response time, there is still a considerable issue. Since it is based on a query window which aligns with a given grid, a number of errors may be occurred when it is applied to real applications. In this paper, we proposed selectivity estimation techniques using the generalized cumulative density histogram based on two probabilistic models: (1) probabilistic model which considers the query window area ratio, (2) probabilistic model which considers intersection area between a given grid and objects. In order to evaluate the proposed methods, we experimented with real dataset and experimental results showed that the proposed technique was superior to the existing selectivity estimation techniques. The proposed techniques can be used to accurately quantify the selectivity of the spatial range query on rectangle objects.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.19 no.3
• /
• pp.93-102
• /
• 2015

This study evaluates collapse probabilities of structures which are designed according to a domestic seismic design code, KBC2009. In evaluating their collapse probabilities, to do this, probabilistic distribution models for seismic hazard and structural capacity are required. In this paper, eight major cities in Korea are selected and the demand probabilistic distribution of each city is obtained from the uniform seismic hazard. The probabilistic distribution for the structural capacity is assumed to follow a underlying design philosophy implicitly defined in ASCE 7-10. With the assumptions, the structural collapse probability in 50 years is evaluated based on the concept of a risk integral. This paper then defines an mean value of the collapse probabilities in 50 years of the selected major cities as the target risk. Risk-targeted spectral accelerations are finally suggested by modifying a current mapped spectral acceleration to meet the target risk.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.8 s.179
• /
• pp.2115-2122
• /
• 2000

The understanding of impact-induced delamination is important in safety and reliability of composite structure. In this study, a model for arrest toughness is proposed in consideration of fracture behavior of composite materials. Also, the probabilistic model is proposed to describe the variability of arrest toughness due to the nonhomogeneity of material. For these models, experiments were conducted on the Carbon/Epoxy composite plates with various thickness using the impact hammer. The elastic work factor used in J-Integral is applicable to the evaluation of energy release rate. The fracture behavior can be described by crack arrest concept and the arrest toughness is independent of the delamination size. Additionally, a probabilistic characteristics of arrest toughness is well described by the Weibull distribution function. A variation of arrest toughness increases with specimen thickness.

• Proceedings of the KSME Conference
• /
• 2000.04a
• /
• pp.132-137
• /
• 2000

In this study, model for arrest toughness is proposed in consideration of fracture behavior of composite materials. Also, the probabilistic model is proposed to describe the variability of arrest toughness due to the nonhomogeneity of material. For these models. experiments were conducted on the Carbon/Epoxy composite plates with various thickness using the impact hammer. The elastic work fatter used in J-Integral is applicable to the evaluation of energy release rate. The fracture behavior call be described by crack arrest concept and the arrest toughness is independent of the delamination size. Additionally, a probabilistic characteristics of arrest toughness is well described by the Weibull distribution function. An increasing of thickness raises a variation of arrest toughness.

• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06b
• /
• pp.426-428
• /
• 2012

In order to identify proteins that are present in biological samples, these samples are separated and analyzed under the sequential procedure as follows: protein purification and digestion, peptide fragmentation by tandem mass spectrometry (MS/MS) which breaks peptides into fragments, peptide identification, and protein identification. One of the widely used methods for protein identification is based on probabilistic approaches such as ProteinProphet and BaysPro. However, they do not consider the difference in peptide identification probabilities according to their length. Here, we propose a probabilistic graphical model-based approach to protein identification from MS/MS data considering peptide identification probabilities, number of sibling peptides, and peptide length. We compared our approach with ProteinProphet using a yeast MS/MS dataset. As a result, our model identified 27 more proteins than ProteinProphet at 1% of FDR (false discovery rate), confirming the importance of peptide length information in protein identification.

• Journal of Communications and Networks
• /
• v.16 no.5
• /
• pp.534-547
• /
• 2014

Large-scale failures resulting from natural disasters or intentional attacks are now causing serious concerns for communication network infrastructure, as the impact of large-scale network connection disruptions may cause significant costs for service providers and subscribers. In this paper, we propose a new framework for the analysis and prevention of network service disruptions in large-scale failure scenarios. We build dynamic deterministic and probabilistic models to capture the impact of regional failures as they evolve with time. A probabilistic failure model is proposed based on wave energy behaviour. Then, we develop a novel approach for preventive protection of the network in such probabilistic large-scale failure scenarios. We show that our method significantly improves uninterrupted delivery of data in the network and reduces service disruption times in large-scale regional failure scenarios.