• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1234-1238
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• 2004

A Controlled neural networks are proposed in order to measure nonlinear environments in adaptive and in realtime. The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between tile output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we have various experiments. And this controller call prove effectively to be control in the environments of various systems.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.25-55
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• 1998

Maximum likelihood estimates (MLEs) for recursive models of categorical variables are discussed under an EM framework. Since MLEs by EM often depend on the choice of the initial values for MLEs, we explore reasonable rules for selecting the initial values for EM. Simulation results strongly support the proposed rules.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.525-526
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• 2008

For measuring nonlinear Vehicle Modeling based on 7-Depth Sensor, the neural networks are proposed m adaptive and in realtime. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3289-3295
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• 2010

In business material management, as the number of products and their parts increases, the employment of databases becomes inevitable. Many researchers have tried to incorporate semantics in traditional models using logic programming, resulting in the deductive method. However, since the designer of this method does not overcome the traditional design concept of relational method, there have been not much successful implementations in deductive method. Therefore, this paper propose the new way designing the retrieval methods for deductive databases and using those relations the query that makes recursive retrievals possible and hypotheses test in business material management.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.177-184
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• 2016

This paper presents a strike velocity estimation using the recursive Bayesian filter that operates both correction and prediction models to probabilistically remove noises of sensors and accurately estimate the strike velocity during the real-time experiments. Four different types of bullets such as 5.56 mm M193, 7.62 mm M80, 5.45 mm 7N10 and 7.62 mm MSC were used to validate the proposed method. Compared to the existing method, the proposed method statistically results in higher stability of the strike velocity estimation as well as its reliability for the ballistic limit velocity computation.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.184-194
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• 2016

In existing identification methods for on-orbit spacecraft, such as eigensystem realization algorithm (ERA) and subspace method identification (SMI), singular value decomposition (SVD) is used frequently to estimate the modal parameters. However, these identification methods are often used to process the linear time-invariant system, and there is a lower computation efficiency using the SVD when the system order of spacecraft is high. In this study, to improve the computational efficiency in identifying time-varying modal parameters of large spacecraft, a faster recursive algorithm called fast approximated power iteration (FAPI) is employed. This approach avoids the SVD and can be provided as an alternative spacecraft identification method, and the latest modal parameters obtained can be applied for updating the controller parameters timely (e.g. the self-adaptive control problem). In numerical simulations, two large flexible spacecraft models, the Engineering Test Satellite-VIII (ETS-VIII) and Soil Moisture Active/Passive (SMAP) satellite, are established. The identification results show that this recursive algorithm can obtain the time-varying modal parameters, and the computation time is reduced significantly.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.754-759
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• 1993

An adaptive model predictive control (AMPC) strategy using auto-regression moving-average (ARMA) models is presented. The characteristic features of this methodology are the small computer memory requirement, high computational speed, robustness, and easy handling of nonlinear and time varying MIMO systems. Since the process dynamic behaviors are expressed by ARMA models, the model parameter adaptation is simple and fast to converge. The recursive least square (RLS) method with exponential forgetting is used to trace the process model parameters assuming the process is slowly time varying. The control performance of the AMPC is verified by both comparative simulation and experimental studies on distillation column control.

• Earthquakes and Structures
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• v.18 no.1
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• pp.1-14
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• 2020

The vehicle-bridge interaction (VBI) analysis might be cumbersome and computationally expensive in bridge engineering due to the necessity of solving large number of coupled system of equations. However, VBI analysis can provide valuable insights into the dynamic behavior of highway bridges under specific loading conditions. Hence, this paper presents a numerical study on the dynamic behavior of a conventional highway bridge under strong near-field and far-field earthquake motions considering the VBI effects. A recursive substructuring method, which enables solving bridge and vehicle equations of motion separately and suitable to be adapted to general purpose finite element softwares, was used. A thorough analysis that provides valuable information about the effect of various traffic conditions, vehicle velocity, road roughness and effect of soil conditions under far-field and near-field strong earthquake motions has been presented. A real-life concrete highway bridge was chosen for numerical demonstrations. In addition, sprung mass models of vehicles consist of conventional truck and car models were created using physical and dynamic properties adopted from literature. Various scenarios, of which the results may help to highlight the different aspects of the dynamic response of concrete highway bridges under strong earthquakes, have been considered.

• Water for future
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• v.25 no.4
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• pp.109-119
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• 1992

The purpose of this study is to develop real-time streamflow forecasting models in order to manage effectively the flood warning system and water resources during the storm. The stochastic system models of the rainfall-runoff process using in this study are constituted and applied the Recursive Least Square and the Instrumental Variable-Approximate Maximum Likelihood algorithm which can estimate recursively the optimal parameters of the model. Also, in order to improve the performance of streamflow forecasting, initial values of the model parameter and covariance matrix of parameter estimate errors were evaluated by using the observed historical data of the hourly rainfall-runoff, and the accuracy and applicability of the models developed in this study were examined by the analysis of the I-step ahead streamflow forecasts.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.4
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• pp.65-71
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• 2000

Finite wordlength effect of the recursive implementation of SDFT(sliding-DFT) is analytically derived in this paper. Representation errors of the twiddle coefficients and the data registers are the two major causes of the spectral errors in the recursive implementation. The noise-to-signal ratio is analytically derived in terms of the coefficients wordlength, the data registers wordlength, and the DFT's block-length used in the computation Error dynamic equation is obtained from the recursive DFT and the probabilistic models for the coefficients error and the round-off error are introduced for the NSR derivation, The result of the NSR derivation is verified with the simulation data.