• Title/Summary/Keyword: discrete models

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An Efficient Hybrid Simulation Methodology Using the Game Physics Engine (물리엔진을 이용한 효과적인 하이브리드 시뮬레이션 방법론)

  • Lee, Wan-Bok;Ryu, Seuc-Ho
    • Journal of Digital Convergence
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    • v.10 no.10
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    • pp.539-544
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    • 2012
  • Most of the man-made systems can be modeled as a hybrid system which consists of both the high-level and the low-level component model. High level model is responsible for decision-making and the low-level one takes control of the mechanical component parts. Since the two models requires different interpretation method according to their type, analysis of a hybrid system becomes a difficult job. For the Analysis of the high-level model, methods for discrete event system models such as FSM can be used. On the contrary, numerical analysis techniques are required for the low-level continuous-time system model. Since it becomes a difficult thing for a modeller specifies and develops both the two-level models altogether, we propose an efficient hybrid simulation method which employs a game physics engine that has been widely and successfully used in the area of game industry.

Evolutionary Hypernetwork Model for Higher Order Pattern Recognition on Real-valued Feature Data without Discretization (이산화 과정을 배제한 실수 값 인자 데이터의 고차 패턴 분석을 위한 진화연산 기반 하이퍼네트워크 모델)

  • Ha, Jung-Woo;Zhang, Byoung-Tak
    • Journal of KIISE:Software and Applications
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    • v.37 no.2
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    • pp.120-128
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    • 2010
  • A hypernetwork is a generalized hypo-graph and a probabilistic graphical model based on evolutionary learning. Hypernetwork models have been applied to various domains including pattern recognition and bioinformatics. Nevertheless, conventional hypernetwork models have the limitation that they can manage data with categorical or discrete attibutes only since the learning method of hypernetworks is based on equality comparison of hyperedges with learned data. Therefore, real-valued data need to be discretized by preprocessing before learning with hypernetworks. However, discretization causes inevitable information loss and possible decrease of accuracy in pattern classification. To overcome this weakness, we propose a novel feature-wise L1-distance based method for real-valued attributes in learning hypernetwork models in this study. We show that the proposed model improves the classification accuracy compared with conventional hypernetworks and it shows competitive performance over other machine learning methods.

Prediction of Groundwater Levels in Hillside Slopes Using the Autoregressive Model (AR 모델을 이용한 산사면에서의 지하수위 예측)

  • Lee, In-Mo;Park, Gyeong-Ho;Im, Chung-Mo
    • Geotechnical Engineering
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    • v.9 no.3
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    • pp.67-76
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    • 1993
  • Korea being composed of a number of mountains has been damaged and destroyed in lives and properties by the occurrence of many landslides during the wet seasons. Therefore, it is necessary to study the forecast system and risk analysis for the occurrence of landslides : the rise of groundwater levels due to rainfall is the main cause of landslides. In this paper, the autoregressive models are used to predict the grondwater levls using cases of both time invariant and time -varing autoregressive coefficients. In the former case, AR(1), AR(2), and AR(3) models are selected and their single-valued parameters are estimated to fit them to the observed groundwater level series. In the latter case, modified AR(1) and typical AR(2) models are used as process model and a discrete Kalman Filtering technique is utilized to estimate the parameters which are themselves a function of time. The results show that the real time forecast system using the time-varying autoregressive coefficinets as well as time -invariant AR model is good to predict the groundwater level in hillside slopes and we might get better result if we use the time-hourly rainfall intensity as well as the observed groundwater level.

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On Implementation of the Finite Difference Lattice Boltzmann Method with Internal Degree of Freedom to Edgetone

  • Kang, Ho-Keun;Kim, Eun-Ra
    • Journal of Mechanical Science and Technology
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    • v.19 no.11
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    • pp.2032-2039
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    • 2005
  • The lattice Boltzman method (LBM) and the finite difference-based lattice Boltzmann method (FDLBM) are quite recent approaches for simulating fluid flow, which have been proven as valid and efficient tools in a variety of complex flow problems. They are considered attractive alternatives to conventional finite-difference schemes because they recover the Navier-Stokes equations and are computationally more stable, and easily parallelizable. However, most models of the LBM or FDLBM are for incompressible fluids because of the simplicity of the structure of the model. Although some models for compressible thermal fluids have been introduced, these models are for monatomic gases, and suffer from the instability in calculations. A lattice BGK model based on a finite difference scheme with an internal degree of freedom is employed and it is shown that a diatomic gas such as air is successfully simulated. In this research we present a 2-dimensional edge tone to predict the frequency characteristics of discrete oscillations of a jet-edge feedback cycle by the FDLBM in which any specific heat ratio $\gamma$ can be chosen freely. The jet is chosen long enough in order to guarantee the parabolic velocity profile of a jet at the outlet, and the edge is of an angle of $\alpha$=23$^{o}$. At a stand-off distance w, the edge is inserted along the centerline of the jet, and a sinuous instability wave with real frequency is assumed to be created in the vicinity of the nozzle exit and to propagate towards the downstream. We have succeeded in capturing very small pressure fluctuations resulting from periodic oscillation of the jet around the edge.

A probabilistic framework for drought forecasting using hidden Markov models aggregated with the RCP8.5 projection

  • Chen, Si;Kwon, Hyun-Han;Kim, Tae-Woong
    • Proceedings of the Korea Water Resources Association Conference
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    • 2016.05a
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    • pp.197-197
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    • 2016
  • Forecasting future drought events in a region plays a major role in water management and risk assessment of drought occurrences. The creeping characteristics of drought make it possible to mitigate drought's effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, a new probabilistic scheme is proposed to forecast droughts, in which a discrete-time finite state-space hidden Markov model (HMM) is used aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The 3-month standardized precipitation index (SPI) is employed to assess the drought severity over the selected five stations in South Kore. A reversible jump Markov chain Monte Carlo algorithm is used for inference on the model parameters which includes several hidden states and the state specific parameters. We perform an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to derive a probabilistic forecast that considers uncertainties. Results showed that the HMM-RCP forecast mean values, as measured by forecasting skill scores, are much more accurate than those from conventional models and a climatology reference model at various lead times over the study sites. In addition, the probabilistic forecast verification technique, which includes the ranked probability skill score and the relative operating characteristic, is performed on the proposed model to check the performance. It is found that the HMM-RCP provides a probabilistic forecast with satisfactory evaluation for different drought severity categories, even with a long lead time. The overall results indicate that the proposed HMM-RCP shows a powerful skill for probabilistic drought forecasting.

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Experimental and numerical studies of the pre-existing cracks and pores interaction in concrete specimens under compression

  • Haeri, Hadi;Sarfarazi, Vahab;Zhu, Zheming;Marji, Mohammad Fatehi
    • Smart Structures and Systems
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    • v.23 no.5
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    • pp.479-493
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    • 2019
  • In this paper, the interaction between notch and micro pore under uniaxial compression has been performed experimentally and numerically. Firstly calibration of PFC2D was performed using Brazilian tensile strength, uniaxial tensile strength and biaxial tensile strength. Secondly uniaxial compression test consisting internal notch and micro pore was performed experimentally and numerically. 9 models consisting notch and micro pore were built, experimentally and numerically. Dimension of these models are 10 cm*1 cm*5 cm. the length of joint is 2 cm. the angularities of joint are $30^{\circ}$, $45^{\circ}$ and $60^{\circ}$. For each joint angularity, micro pore was situated 2 cm above the lower tip of the joint, 2 cm above the middle of the joint and 2 cm above the upper of the joint, separately. Dimension of numerical models are 5.4 cm*10.8 cm. The size of the cracks was 2 cm and its orientation was $30^{\circ}$, $45^{\circ}$ and $60^{\circ}$. Diameter of pore was 1cm which situated at the upper of the notch i.e., 2 cm above the upper notch tip, 2 cm above the middle of the notch and 2 cm above the lower of the notch tip. The results show that failure pattern was affected by notch orientation and pore position while uniaxial compressive strength is affected by failure pattern.

Preconditioned Jacobian-free Newton-Krylov fully implicit high order WENO schemes and flux limiter methods for two-phase flow models

  • Zhou, Xiafeng;Zhong, Changming;Li, Zhongchun;Li, Fu
    • Nuclear Engineering and Technology
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    • v.54 no.1
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    • pp.49-60
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    • 2022
  • Motivated by the high-resolution properties of high-order Weighted Essentially Non-Oscillatory (WENO) and flux limiter (FL) for steep-gradient problems and the robust convergence of Jacobian-free Newton-Krylov (JFNK) methods for nonlinear systems, the preconditioned JFNK fully implicit high-order WENO and FL schemes are proposed to solve the transient two-phase two-fluid models. Specially, the second-order fully-implicit BDF2 is used for the temporal operator and then the third-order WENO schemes and various flux limiters can be adopted to discrete the spatial operator. For the sake of the generalization of the finite-difference-based preconditioning acceleration methods and the excellent convergence to solve the complicated and various operational conditions, the random vector instead of the initial condition is skillfully chosen as the solving variables to obtain better sparsity pattern or more positions of non-zero elements in this paper. Finally, the WENO_JFNK and FL_JFNK codes are developed and then the two-phase steep-gradient problem, phase appearance/disappearance problem, U-tube problem and linear advection problem are tested to analyze the convergence, computational cost and efficiency in detailed. Numerical results show that WENO_JFNK and FL_JFNK can significantly reduce numerical diffusion and obtain better solutions than traditional methods. WENO_JFNK gives more stable and accurate solutions than FL_JFNK for the test problems and the proposed finite-difference-based preconditioning acceleration methods based on the random vector can significantly improve the convergence speed and efficiency.

Effective Ray-tracing based Rendering Methods for Point Cloud Data in Mobile Environments (모바일 환경에서 점 구름 데이터에 대한 효과적인 광선 추적 기반 렌더링 기법)

  • Woong Seo;Youngwook Kim;Kiseo Park;Yerin Kim;Insung Ihm
    • Journal of the Korea Computer Graphics Society
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    • v.29 no.3
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    • pp.93-103
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    • 2023
  • The problem of reconstructing three-dimensional models of people and objects from color and depth images captured by low-cost RGB-D cameras has long been an active research area in computer graphics. Color and depth images captured by low-cost RGB-D cameras are represented as point clouds in three-dimensional space, which correspond to discrete values in a continuous three-dimensional space and require additional surface reconstruction compared to rendering using polygonal models. In this paper, we propose an effective ray-tracing based technique for visualizing point clouds rather than polygonal models. In particular, our method shows the possibility of an effective rendering method even in mobile environment which has limited performance due to processor heat and lack of battery.

EEG Patterns of High dose Pilocarpine-Induced Status Epilepticus in Rats (흰쥐에서 고용량의 Pilocarpine에 의하여 유발된 간질중첩증의 양상)

  • Lee, Kyung-Mok;Jung, Ki-Young;Kim, Jae-Moon
    • Annals of Clinical Neurophysiology
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    • v.2 no.2
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    • pp.119-124
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    • 2000
  • Background : We studied EEG changes during pilocarpine-induced status epilepticus(SE), a widely used model whose EEG characteristics have not been fully described previously. Methods : Male Sprague-Dawley rats weighing 250-350 grams were used as subjects. SE was induced 5-7 days after placement of chronic epidural electrodes, using 360-380 mg/Kg pilocarpine IP. Rats were observed with continuous EEG recording following pilocarpine injection until end of the SE episode. Results : SE occurred in 10/12 rats studied. SE began with a series of discrete seizures $11.1{\pm}3.93$ minutes after pilocarpine injection. $5.2{\pm}2.71$ seizures occurred over $10.9{\pm}4.62$ minutes, until the EEG converted to a waxing and waning pattern, during which the amplitude and frequency of epileptiform activity increased. After $1.4{\pm}1.82$ minutes, a pattern of continuous high amplitude rapid spiking was established. Continuous spiking continued for $3.4{\pm}0.48$ hours with a very gradual decline in amplitude and frequency, until periodic epileptiform discharges(PEDs) began to occur. The EEG consisted primarily of PEDs for another $7.4{\pm}3.09$ hours, until electrographic generalized seizures began to occur. These continued for $5.8{\pm}4.82$ hours until death. Duration of SE was $17.0{\pm}5.88$ hours. Flat periods were a prominent feature during all EEG patterns in this model. Conclusion : EEG features distinctive in pilocarpine SE(but not unique to it) include flat periods during all patterns and resumption of continuous spiking episodes after the onset of PEDs. The sequence of discrete seizures to waxing and waning to continuous spiking to PEDs was identical to that which has been described in humans and other animal models.

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Bayesian Analysis of a Zero-inflated Poisson Regression Model: An Application to Korean Oral Hygienic Data (영과잉 포아송 회귀모형에 대한 베이지안 추론: 구강위생 자료에의 적용)

  • Lim, Ah-Kyoung;Oh, Man-Suk
    • The Korean Journal of Applied Statistics
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    • v.19 no.3
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    • pp.505-519
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    • 2006
  • We consider zero-inflated count data, which is discrete count data but has too many zeroes compared to the Poisson distribution. Zero-inflated data can be found in various areas. Despite its increasing importance in practice, appropriate statistical inference on zero-inflated data is limited. Classical inference based on a large number theory does not fit unless the sample size is very large. And regular Poisson model shows lack of St due to many zeroes. To handle the difficulties, a mixture of distributions are considered for the zero-inflated data. Specifically, a mixture of a point mass at zero and a Poisson distribution is employed for the data. In addition, when there exist meaningful covariates selected to the response variable, loglinear link is used between the mean of the response and the covariates in the Poisson distribution part. We propose a Bayesian inference for the zero-inflated Poisson regression model by using a Markov Chain Monte Carlo method. We applied the proposed method to a Korean oral hygienic data and compared the inference results with other models. We found that the proposed method is superior in that it gives small parameter estimation error and more accurate predictions.