• Journal of IKEEE
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• v.24 no.2
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• pp.661-664
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• 2020

In this paper, we propose a maritime region segmentation method and a segment-based destination prediction method for vessel path prediction. In order to perform maritime segmentation, clustering on destination candidates generated from the past paths is conducted. Then the segment-based destination prediction is followed. For destination prediction, different prediction methods are applied according to whether the current region is linear or not. In the linear domain, the vessel is regarded to move constantly, and linear prediction is applied. In the nonlinear domain with an uncertainty, we assume that the vessel moves similarly to the most similar past path. Experimental results show that applying the linear prediction and the prediction method using a similar path differently depending on the linearity and the uncertainty of the path is better than applying one of them alone.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1278-1289
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• 2008

Currently in increasing number of urban tunnels with small overburden are excavated according to the principle of the New Austrian Tunneling Method (NATM). Successful design, construction and maintenance of NATM tunnel in urban area demands prediction, control and monitoring of surface settlement, gradient and ground displacement with high accuracy. Use of measured displacement for parameter determination has been researched over the years, and one geotechnical engineering principle has been formed as back analysis. In this paper, back analysis of a ground deformational behavior involving nonlinear behavior is discussed. It is of primary importance to make reliable prediction of deformational behavior for shallow tunnels in soft ground. However, predictions made often prove to be incorrect due to complexity of constitutive law and other relevant factors. Back analysis therefore becomes more important, for it may be used to interpret measured displacement to derive nonlinear material characteristics. The paper shows some example in which a deformational mechanism is studied in the light of inhomogeneous distrubution of Young's module, from which a logic is derived to identify two different types of nonlinear constitutive relationships.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.6
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• pp.98-106
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• 2011

This paper reviews the state-of-the-art thermoacoustic(TA) modeling techniques and research trend to predict major parameters determining combustion instabilities in lean premixed gas turbine combustors. Linear TA modeling results give us an information on eigenfrequencies and initial growth rate of the instabilities. For the prediction, linear relation equation between acoustic waves and heat release oscillations should be derived in the determined system. Key information for this analysis is to determine the heat release fluctuations in the combustor, which is typically obtained by using n-${\tau}$ function from flame transfer function measurements and/or predictions. Great advancement in the linear TA modeling has been made over a couple of decades, and some successful prediction results have been reported in actual gas turbine combustors. However nonlinear TA model developments which are required to analyze nonlinear system behaviors such as limit cycle saturation and transition phenomena are still limited in a very simple system. In order to fully understand combustion instabilities in a complicated real system, nonlinear flame dynamics and acoustic wave interaction with nonlinear system boundary conditions should be explained from the nonlinear TA model developments.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.713-718
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• 2016

Predicting precise specifications of differential mixed-signal circuits is a difficult problem, because analytically derived correlation between process variations and conventional specifications exhibits the limited prediction accuracy due to the phase unbalance, for most self-tests. This paper proposes an efficient prediction technique to provide accurate specifications of differential mixed-signal circuits in a system-on-chip (SoC) based on a nonlinear statistical nonlinear regression technique. A spectrally pure sinusoidal signal is applied to a differential DUT, and its output is fed into another differential DUT through a weighting circuitry in the loopback configuration. The weighting circuitry, which is employed from the previous work [3], efficiently produces different weights on the harmonics of the loopback responses, i.e., the signatures. The correlation models, which map the signatures to the conventional specifications, are built based on the statistical nonlinear regression technique, in order to predict accurate nonlinearities of individual DUTs. In production testing, once the efficient signatures are measured, and plugged into the obtained correlation models, the harmonic coefficients of DUTs are readily identified. This work provides a practical test solution to overcome the serious test issue of differential mixed-signal circuits; the low accuracy of analytically derived model is much lower by the errors from the unbalance. Hardware measurement results showed less than 1.0 dB of the prediction error, validating that this approach can be used as production test.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.2
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• pp.20-30
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• 2001

In the paper, we propose a predictive control scheme using multiple neural network-based prediction models. To construct the multiple models, we select several specific values of a parameter whose variation affects serious control performance in the plant. Among the multiple prediction models, we choose one that shows the best predictions for future outputs of the plant by a switching technique. Based on a nonlinear programming method, we calculate the current process input in the nonlinear predictive control system with multiple prediction models. The proposed control method is shown to be very effective when a parameter of the plant changes or the time delay, if it exists, varies. It is also shown that the proposed method is successfully applied for the control of suspension in a electro-magnetic levitation system.

• Structural Engineering and Mechanics
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• v.69 no.3
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• pp.243-255
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• 2019

Utilization of fiber beam-column element has gained considerable attention in recent years due mainly to its ability to model distributed plasticity over the length of the element through a number of integration points. However, the relatively high sensitivity of the method to modeling parameters as well as material behavior models can pose a significant challenge. Residual drift is one of the seismic demands which is highly sensitive to modeling parameters and material behavior models. Permanent deformations play a prominent role in the post-earthquake evaluation of serviceability of bridges affected by a near-fault ground shaking. In this research, the influence of distributed plasticity modeling parameters using both force-based and displacement-based fiber elements in the prediction of internal forces obtained from the nonlinear static analysis is studied. Having chosen suitable type and size of elements and number of integration points, the authors take the next step by investigating the influence of material behavioral model employed for the prediction of permanent deformations in the nonlinear dynamic analysis. The result shows that the choice of element type and size, number of integration points, modification of cyclic concrete behavior model and reloading strain of concrete significantly influence the fidelity of fiber element method for the prediction of permanent deformations.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.02a
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• pp.36-47
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• 1999

Terzaghi's theory of one-dimensional consolidation is restricted in its applicability to relatively thin layers and small incremental loading. Because it is assumed to infinitesimal strain and linear material function. For this reason, Gibson et al established a rigorous formulation for the one-dimensional nonlinear finite strain consolidation theory. There are some difficulties in the application of finite strain consolidation theory. The developed program consisted of several forms and modules. These forms and modules with graphic-user-interfaced format are used in analysis of consolidation practices. For the purpose of verification of developed program. the results of case study and prediction of developed program are compared. The results of comparison is fairly well with prediction and measured data. And with varying finite strain consolidation parameter, g(e) or λ(e), the sensitivity of predicted values were examined.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.9
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• pp.929-938
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• 2009

This paper presents a novel control methodology for communication network based nonlinear systems with time delay nature. We construct a nominal nonlinear control law for representing a linear model and a reset control system which is aimed for corrective control strategy to compensate system error due to uncertain time delay through wireless communication network. Next, online neural control approach is proposed for overcoming nonstationary statistical nature in the network topology. Additionally, DBN (Dynamic Bayesian Network) technique is accomplished for modeling of its dynamics in terms of casuality, which is then utilized for estimating prediction of system output. We evaluate superiority and reliability of the proposed control approach through numerical simulation example in which a nonlinear inverted pendulum model is employed as a networked control system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.65-75
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• 2004

This paper presents a generalized predictive control method based on a fuzzy neural network(FNN) model, which uses the on-line multi-step prediction, fur the intelligent control of chaotic nonlinear systems whose mathematical models are unknown. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of FNN are determined adaptively during the operation of the system. In order to design a generalized predictive controller effectively, this paper describes computing procedure for each of the two important parameters. Also, we introduce a projection matrix to determine the control input, which deceases the control performance function very rapidly. Finally, in order to evaluate the performance of our controller, the proposed method is applied to the Doffing and Henon systems, which are two representative continuous-time and discrete-time chaotic nonlinear systems, res reactively.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.587-598
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• 2008

In the trackbed design using elastic multi-layer model, the stress-dependent resilient modulus is an important input parameter, which reflects substructure performance under repeated traffic loading. The resilient moduli of crushed stone and weathered granite soil were developed using nonlinear dynamic stiffness, which can be measured by in-situ and laboratory seismic tests. The prediction models of resilient modulus varying with the deviatoric or bulk stress were proposed (Park et al., 2008). To investigate the performance of the prediction models proposed herein, the elastic response of the test trackbed near PyeongTaek, Korea was evaluated using a 3-D nonlinear elastic computer program (GEOTRACK) and compared with measured elastic vertical displacement during the passages of freight and passenger trains. The material types of the test sub-ballasts are crushed stone and weathered granite soil, respectively. The calculated vertical displacements within the sub-ballasts are within the order of 1mm, and agree well with measured values with the reasonable margin. The prediction models are thus concluded to work properly in the preliminary investigation. The prediction models proposed for resilient modulus were verified by the comparison of the calculated vertical displacements with measured ones during train passages.