• Structural Engineering and Mechanics
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• v.87 no.1
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• pp.1-18
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• 2023

The paper presents a Bayesian Finite element (FE) model updating methodology by utilizing modal data. The dynamic condensation technique is adopted in this work to reduce the full system model to a smaller model version such that the degrees of freedom (DOFs) in the reduced model correspond to the observed DOFs, which facilitates the model updating procedure without any mode-matching. The present work considers both the MPV and the covariance matrix of the modal parameters as the modal data. Besides, the modal data identified from multiple setups is considered for the model updating procedure, keeping in view of the realistic scenario of inability of limited number of sensors to measure the response of all the interested DOFs of a large structure. A relationship is established between the modal data and structural parameters based on the eigensystem equation through the introduction of additional uncertain parameters in the form of modal frequencies and partial mode shapes. A novel sampling strategy known as the Metropolis-within-Gibbs (MWG) sampler is proposed to sample from the posterior Probability Density Function (PDF). The effectiveness of the proposed approach is demonstrated by considering both simulated and experimental examples.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.48.4-49
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• 2020

We reconstruct the expansion history of the universe using type Ia supernovae (SN Ia) in a manner independent of any cosmological model assumptions. To do so, we implement a nonparametric iterative smoothing method on the Joint Light-curve Analysis (JLA) data while exploring the SN Ia light-curve hyperparameter space by Markov Chain Monte Carlo (MCMC) sampling. We test to see how the posteriors of these hyperparameters depend on cosmology, whether using different dark energy models or reconstructions shift these posteriors. Our constraints on the SN Ia light-curve hyperparameters from our model-independent analysis are very consistent with the constraints from using different parameterizations of the equation of state of dark energy, namely the flat ΛCDM cosmology, the Chevallier-Polarski-Linder model, and the Phenomenologically Emergent Dark Energy (PEDE) model. This implies that the distance moduli constructed from the JLA data are mostly independent of the cosmological models. We also studied that the possibility the light-curve parameters evolve with redshift and our results show consistency with no evolution. The reconstructed expansion history of the universe and dark energy properties also seem to be in good agreement with the expectations of the standard ΛCDM model. However, our results also indicate that the data still allow for considerable flexibility in the expansion history of the universe. This work is published in ApJ.

• Smart Structures and Systems
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• v.31 no.1
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• pp.29-43
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• 2023

This study investigates the possibility of damage detection of a real bridge by means of a modal parameter-based finite element (FE) model update. Field moving vehicle experiments were conducted on an actual steel plate girder bridge. In the damage experiment, cracks were applied to the bridge to simulate damage states. A fast Bayesian FFT method was employed to identify and quantify uncertainties of the modal parameters then these modal parameters were used in the Bayesian model update. Material properties and boundary conditions are taken as uncertainties and updated in the model update process. Observations showed that although some differences existed in the results obtained from different model classes, the discrepancy between modal parameters of the FE model and those experimentally obtained was reduced after the model update process, and the updated parameters in the numerical model were indeed affected by the damage. The importance of boundary conditions in the model updating process is also observed. The capability of the MCMC model update method for application to the actual bridge structure is assessed, and the limitation of FE model update in damage detection of bridges using only modal parameters is observed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.150-152
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• 2022

In this work, we propose a model that considers the behavior and synaptic plasticity of sensory neurons based on Liquid Time-constant Network (LTC). The neuron connection structure was experimented with four types: the increasing number of neurons, the decreasing number, the decreasing number, and the decreasing number. In this study, we experimented using a time series prediction dataset to see if the performance of the changed model improved compared to LTC. Experimental results show that the application of modeling of sensory neurons does not always bring about performance improvements, but improves performance through proper selection of learning rules depending on the type of dataset. In addition, the connective structure of neurons showed improved performance when it was less than four layers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.64-88
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• 2024

Security and reliability are the utmost importance facts in intelligent networked vehicles. Stochastic Petri Net and Z (SPZN) as an excellent formal verification tool for modeling concurrent systems, can effectively handles concurrent operations within a system, establishes relationships among components, and conducts verification and reasoning to ensure the system's safety and reliability in practical applications. However, the application of a system with numerous nodes to Petri Net often leads to the issue of state explosion. To tackle these challenges, a refinement and abstraction method based on SPZN is proposed in this paper. This approach can not only refine and abstract the Stochastic Petri Net but also establish a corresponding relationship with the Z language. In determining the implementation rate of transitions in Stochastic Petri Net, we employ the interval average and weighted average method, which significantly reduces the time and space complexity compared to alternative techniques and is suitable for expert systems at various levels. This reduction facilitates subsequent comprehensive system analysis and module analysis. Furthermore, by analyzing the properties of Markov Chain isomorphism in the case study, recommendations for minimizing system risks in the application of intelligent parking within the intelligent networked vehicle system can be put forward.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.509-521
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• 1994

This study is an effort to develop computer simulation model that produce precipitation patterns from stochastic model. A stochastic model is formulated for the process of daily precipitation with considering the sequences of wet and dry days and the precipitation amounts on wet days. This study consists of 2 papers and the process of precipitation occurrence is modelled by an alternate renewal process (ARP) in paper (I). In the ARP model for the precipitation occurrence, four discrete distributions, used to fit the wet and dry spells, were as follows; truncated binomial distribution (TBD), truncated Poisson distribution (TPD), truncated negative binomial distribution (TNBD), logarithmic series distribution (LSD). In companion paper (II) the process of occurrence is developed by Markov chain. The amounts of precipitation, given that precipitation has occurred, are described by a Gamma. Pearson Type-III, Extremal Type-III, and 3 parameter Weibull distribution. Daily precipitation series model consists of two models, A-Wand A-G model, by combining the process of precipitation occurrence and a continuous probability distribution on the precipitation of wet days. To evaluate the performance of the simulation model, output from the model was compared with historical data of 7 stations in the Nakdong and Seomjin river basin. The results of paper (1) show that it is possible to design a model for the synthetic generation of IX)int precipitation patterns.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12A
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• pp.1922-1931
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• 1999

A multicast traffic’s feature is the function of providing a point to multipoints cell transmission, which is emerging from the main function of ATM switch. However, when a conventional point-to-point switch executes a multicast function, the excess load is occurred because unicast cell as well as multicast cell passed the copy network. Additionally, due to the excess load, multicast cells collide with other cells in a switch. Thus a deadlock that losses cells raises, extremely diminishes the performance of switch. An input queued switch also has a defect of the HOL (Head of Line) blocking that less lessens the performance of the switch. In the proposed multicast switch, we use shared memory switch to reduce HOL blocking and deadlock. In order to decrease switch’s complexity and cell's processing time, to improve a throughput, we utilize the method that routes a cell on a separated paths by traffic pattern and the scheduling algorithm that processes a maximum 2N cell at once in the control part. Besides, when cells is congested at an output port, a cell loss probability increases. Thus we use the Output Memory (OM) to reduce the cell loss probability. And we make use of the method that stores the assigned memory (UM, MM) with a cell by a traffic pattern and clears the cell of the Output memory after a fixed saving time to improve the memory utilization rate. The performance of the proposed switch is executed and compared with the conventional policy under the burst traffic condition through both the analysis based on Markov chain and simulation.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.163-172
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• 2012

Modern society, various great strength crimes are producing. After all crimes happen, it is most important that prevent crime beforehand than that cope. So, many research studied to prevent various crime. However, existing method of studies are to analyze and prevent by society and psychological factors. Therefore we wishes to achieve research to forecast crime by time using Markov chain method. We embody modelling for crime occurrence estimate by crime type time using crime occurrence number of item data that is collected about 5 great strength offender strength, murder, rape, moderation, violence. And examined propriety of crime occurrence estimate modelling by time that propose in treatise that compare crime occurrence type crime occurrence estimate price and actuality occurrence value. Our proposed crime occurrence estimate techniques studied to apply maximum value by critcal value about great strength crime such as strength, murder, rape etc. actually, and heighten crime occurrence estimate probability by using way to apply mean value about remainder crime in this paper. So, we wish to more study about wide crime case and as the crime occurrence estimate rate and actuality value by time are different in crime type hereafter applied examples investigating.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.397-408
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• 2009

In this study, a stochastic precipitation generation framework for simultaneous simulation of daily precipitation at multiple sites is presented. The precipitation occurrence at individual sites is generated using hybrid-order Markov chain model which allows higher-order dependence for dry sequences. The precipitation amounts are reproduced using Anscombe residuals and gamma distributions. Multisite spatial correlations in the precipitation occurrence and amount series are represented with spatially correlated random numbers. The proposed model is applied for a network of 17 locations in the middle of Korean peninsular. Evaluation statistics are reported by generating 50 realizations of the precipitation of length equal to the observed record. The analysis of results show that the model reproduces wet day number, wet and dry day spell, and mean and standard deviation of wet day amount fairly well. However, mean values of 50 realizations of generated precipitation series yield around 23% Root Mean Square Errors (RMSE) of the average value of observed maximum numbers of consecutive wet and dry days and 17% RMSE of the average value of observed annual maximum precipitations for return periods of 100 and 200 years. The provided model also reproduces spatial correlations in observed precipitation occurrence and amount series accurately.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.607-615
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• 2009

There has recently been a trend that IT industry is united with traditional industries such as military, aviation, automobile, and medical industry. Therefore, embedded software which controls hardware of the system should guarantee the high reliability, availability, and maintainability. To guarantee these properties, there are many attempts to develop the embedded software based on COTS (Commercial Off The Shelf) hardware components. However, it can cause additional faults due to software/hardware interactions beside general software faults in this methodology. We called the faults, Linkage Fault. These faults have high severity that makes overall system shutdown although their occurrence frequency is extremely low. In this paper, we propose a new software reliability model which considers those linkage faults in embedded software development with COTS hardware components. We use the Bayesian Analysis and Markov Chain Monte-Cairo method to validate the model. In addition, we analyze real linkage fault data to support the results of the theoretical model.