• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.428-433
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• 2011

In this paper, a prediction method for dynamic stability derivatives is studied using steady state simulations in rotational coordinates. The simulations require the extension of a standard CFD formulations based on inertial coordinate. A new CFD code based on the method are developed. Flows induced by steady circular motions of airfoils with a constant pitch rate are simulated with the code. From the numerical simulations, the pitch rate derivatives are obtained at various Mach numbers, and the results are compared with other numerical results. The numerical simulations show that the new code are capable of predicting dynamic stability derivatives.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.309-314
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• 1992

Many dynamic mechanical systems, such as parts-feeders, walking machines, and percussive power tools, are described by equations of motion which are discontinuous. The discontinuities result from kinematic constraint changes which are difficult to foresee, especially in presence of impact. A simulation algorithm for these types of systems must be able to algorithmically predict and detect the kinematic constraint changes without any prior knowledge of the system's motion. This paper presents a rule-based approach to the prediction and detection of kinematic constraint changes between bodies with arc and line boundaries. The developed algorithm's ability to accurately and automatically detect the unpredicted changes of kinematic constraints is demonstrated with a numerical example.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.795-805
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• 1997

In this paper, we consider a Bayesian forecasting method for the analysis of repeated surveys. It is assumed that the parameters of the superpopulation model at each time follow a stochastic model. We propose Bayesian prediction procedures for the finite population total under dynamic generalized linear models. Some numerical studies are provided to illustrate the behavior of the proposed predictors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.34
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• pp.107-114
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• 1995

The objective of this study is to design a one-stage dynamic prediction model with Kalman state space model. For a model verification, it is compared with EWMA(Exponentially Weighed Moving Average) model. The model designed in this research can be extended to process prevention control and quality monitoring.

• Journal of Communications and Networks
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• v.8 no.2
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• pp.234-240
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• 2006

We study the dynamic burst assembly based on traffic prediction and offset and delay differentiation in optical burst switching network. To improve existing burst assembly mechanism and build an adaptive flexible optical burst switching network, an approach called quality of service (QoS) based adaptive dynamic assembly (QADA) is proposed in this paper. QADA method takes into account current arrival traffic in prediction time adequately and performs adaptive dynamic assembly in limited burst assembly time (BAT) range. By the simulation of burst length error, the QADA method is proved better than the existing method and can achieve the small enough predictive error for real scenarios. Then the different dynamic ranges of BAT for four traffic classes are introduced to make delay differentiation. According to the limitation of BAT range, the burst assembly is classified into one-dimension limit and two-dimension limit. We draw a comparison between one-dimension and two-dimension limit with different prediction time under QoS based offset time and find that the one-dimensional approach offers better network performance, while the two-dimensional approach provides strict inter-class differentiation. Furthermore, the final simulation results in our network condition show that QADA can execute adaptive flexible burst assembly with dynamic BAT and achieve a latency reduction, delay fairness, and offset time QoS guarantee for different traffic classes.

• Journal of the Korean Geotechnical Society
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• v.26 no.8
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• pp.59-66
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• 2010

In this study, an applicability by using the FEM was investigated for the prediction of both the depth of improvement and the vibration effect when dynamic compaction method is applied. The region was modelled by the field conditions applying dynamic compaction method and the rigid body force was applied to the dynamic load model. Predicted depth of improvement calculated by the vertical peak particle acceleration was compared and analyzed with an existing empirical equation, and the effect of groundwave by deducing the peak particle velocity from vibration sources was compared and analyzed with the results of another existing empirical equation. The results showed that the prediction of the depth of improvement has similar tendency to practice, and the vibration effect has some differences in a particular section from existing equation, but it could predict the safety distance to some degree. The analyzed results are expected to be basic data for the development of reliability of dynamic compaction design with existing empirical method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1681-1688
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• 2000

A dynamic model for the prediction of surface topography in high speed end milling process is developed. In this model the effect of tool runout, tool deflection and spindle vibration were taken in to account. An equivalent diameter of end mill is obtained by finite element method and tool deflection experiment. A modal parameter of machine tool is extracted by using frequency response function. The tool deflection, spindle vibration chip thickness and cutting force were calculated in dynamic cutting condition. The tooth pass is calculated at the current angular position for each point of contact between the tool and the workpiece. The new dynamic model for surface predition are compared with several investigated model. It is shown that new dynamic model is more effective to predict surface topography than other suggested models. In high speed end milling, the tool vibration has more effect on surface topography than the tool deflection.

• Journal of the Korean Society of Safety
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• v.7 no.2
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• pp.71-79
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• 1992

This paper presents a least-square algorithms of lattice structures and their use for adaptive prediction of time series generated from the dynamic system. As the view point of adaptive prediction, a new method of Identification of dynamic characteristics by means of estimating the parameters of linear auto regressive model is proposed. The fast convergence of adaptive lattice algorithms is seen to be due to the orthogonalization and decoupling properties of the lattice. The superiority of the least-square lattice is verified by computer simulation, then predictor coefficients are computed from the linear sequential time data. For the application to the dynamic characteristic analysis of unknown system, the transfer function of ideal system represented in frquency domain and the estimated one obtained by predicted coefficients are compared. Using the proposed method, the damping ratio and the natural frequency of a dynamic structure subjected to random excitations can be estimated. It is expected that this method will be widely applicable to other technical dynamic problem in which estimation of damping ratio and fundamental vibration modes are required.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.241-247
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• 2016

The tracking filter plays a key role in the accurate estimation and prediction of maneuvering a vessel's position and velocity when attempting to enhance safety by avoiding collision. Therefore, in order to achieve accurate estimation and prediction, many oceangoing vessels are equipped with the Automatic Radar Plotting Aid (ARPA) system. However, the accuracy of prediction depends on the tracking filter's ability to reduce noise and maintain a stable transient response. The purpose of this paper is to derive the optimal values of the gain parameters used in tracking a High Dynamic Warship. The algorithm employs a ${\alpha}-{\beta}-{\gamma}$ filter to provide accurate estimates and updates of the state variables, that is, positions, velocity and acceleration of the high dynamic warship based on previously observed values. In this study, the filtering coefficients ${\alpha}$, ${\beta}$ and ${\gamma}$ are determined from set values of the damping parameter, ${\xi}$. Optimization of the damping parameter, ${\xi}$, is achieved experimentally by plotting the residual error against different values of the damping parameter to determine the least value of the damping parameter that results in the optimum smoothing coefficients leading to a reduction in the noise corruption effect. Further investigation of the performance of the filter indicates that optimal smoothing coefficients depend on the initial and average velocity of the target.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.191-194
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• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Also, the results of prediction are not a good performance so far, especially in the high-level ozone concentration. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system.