• Wind and Structures
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• v.22 no.3
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• pp.349-368
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• 2016

A preconditioning technique is presented for a simultaneous solution to wind-membrane interaction. In the simultaneous equations, a linear elastic model was employed to deal with the fluid-structure data transfer at the interface. A Lagrange multiplier was introduced to impose the specified boundary conditions at the interface and strongly coupled simultaneous equations are derived after space and time discretization. An initial linear elastic model preconditioner and modified one were derived by treating the linearized elastic model equation as a saddle point problem, respectively. Accordingly, initial and modified fluid-structure interaction (FSI) preconditioner for the simultaneous equations were derived based on the initial and modified linear elastic model preconditioners, respectively. Wind-membrane interaction analysis by the proposed preconditioners, for two and three dimensional membranous structures respectively, was performed. Comparison was made between the performance of initial and modified preconditioners by comparing parameters such as iteration numbers, relative residuals and convergence in FSI computation. The results show that the proposed preconditioning technique greatly improves calculation accuracy and efficiency. The priority of the modified FSI preconditioner is verified. The proposed preconditioning technique provides an efficient solution procedure and paves the way for practical application of simultaneous solution for wind-structure interaction computation.

• ETRI Journal
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• v.43 no.5
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• pp.775-786
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• 2021

To translate in real time, a simultaneous translation system should determine when to stop reading source tokens and generate target tokens corresponding to a partial source sentence read up to that point. However, conventional attention-based neural machine translation (NMT) models cannot produce translations with adequate latency in online scenarios because they wait until a source sentence is completed to compute alignment between the source and target tokens. To address this issue, we propose a reinforced learning (RL)-based attention mechanism, the reinforced attention mechanism, which allows a neural translation model to jointly train the stopping criterion and a partial translation model. The proposed attention mechanism comprises two modules, one to ensure translation quality and the other to address latency. Different from previous RL-based simultaneous translation systems, which learn the stopping criterion from a fixed NMT model, the modules can be trained jointly with a novel reward function. In our experiments, the proposed model has better translation quality and comparable latency compared to previous models.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.149-161
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• 2019

In this article, we suggest the following approaches to simultaneous variable selection and outlier detection. First, we determine possible candidates for outliers using properties of an intercept estimator in a difference-based regression model, and the information of outliers is reflected in the multiple regression model adding mean shift parameters. Second, we select the best model from the model including the outlier candidates as predictors using stochastic search variable selection. Finally, we evaluate our method using simulations and real data analysis to yield promising results. In addition, we need to develop our method to make robust estimates. We will also to the nonparametric regression model for simultaneous outlier detection and variable selection.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.6
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• pp.563-570
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• 2015

In this paper, Finite Element Analysis (FEA) for gear heat treatment using simultaneous dual frequency (SDF) induction heating is conducted. To do this, thermal-electromagnetic coupled FE model is built. A two dimensional FE model of gear and heater is introduced to reduce computation time. For more time-efficient analysis, harmonic analysis for electromagnetic model is adopted and transient analysis model, for heat transfer model. Through the coupled analysis, it can be found that the proposed FE model can solve for SDF induction heating of gear and heat treatment parameters can also be determined.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.1009-1019
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• 2006

In this paper, we consider the simultaneous estimation problem for the normal means. We set up the model structure using the several different distributions of the errors for observing their effects of model perturbation for the error terms in obtaining the empirical Bayes and hierarchical Bayes estimators. We compare the performance of those estimators under model perturbation based on a simulation study.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.506-514
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• 2008

This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.2
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• pp.199-206
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• 2008

Simultaneous nitrification and denitrification means that nitrification and denitrification occur concurrently in the same reaction vessel under low DO concentration. Some mathematical models developed to simulate simultaneous nitrification and denitrification reaction, but they have the complex model structures or have limitations of model application. To solve these problems, if possible that predict the behavior of simultaneous nitrification and denitrification reaction by activated sludge model, structures of the model is less complex than previous models and applies the various operation conditions. But original activated sludge models have difficulties in representing the denitrification reaction under aerobic condition. So the aim of this study is to interpret simultaneous nitrification and denitrification reaction by modifying activated sludge model. Original activated sludge model No.1(ASM1) was selected and modified. The simulation result in modified ASM1 predicted appropriately for the measured data. This indicates the structures of ASM1 are properly improved for interpretation of simultaneous nitrification and denitrification reaction.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.203-212
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• 2024

Simultaneous motion is essential in the activities of daily living (ADL). For motion intention recognition, surface electromyogram (sEMG) and corresponding motion label is necessary. However, this process is time-consuming and it may increase the burden of the user. Therefore, we propose a simultaneous motion recognition framework using data augmentation based on muscle activation model. The model consists of multiple point sources to be optimized while the number of point sources and their initial parameters are automatically determined. From the experimental results, it is shown that the framework has generated the data which are similar to the real one. This aspect is quantified with the following two metrics: structural similarity index measure (SSIM) and mean squared error (MSE). Furthermore, with k-nearest neighbor (k-NN) or support vector machine (SVM), the classification accuracy is also enhanced with the proposed framework. From these results, it can be concluded that the generalization property of the training data is enhanced and the classification accuracy is increased accordingly. We expect that this framework reduces the burden of the user from the excessive and time-consuming data acquisition.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

• The Mathematical Education
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• v.46 no.4
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• pp.483-492
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• 2007

The purpose of this study was to investigate the relation between the cognitive processes and the mathematical achievement of the 4th grade students. And according to the several studies, there were significant relation between cognitive processes and achievement. Based on the PASS(Planning-Attention-Simultaneous-Successive Processes) Model presented by Das and Naglieri, four cognitive process variables were selected. The results of this study as follows. First, there was not significant relation between attention and mathematical achievement. Second, there was significant relation between planning and mathematical achievement. Third, there was significant relation between simultaneous/successive processes and mathematical achievement. Fourth, the students who got higher scores in the two types (simultaneous/successive)of information processing had more mathematical achievement. Specially, the students who got higher scores in the type of simultaneous information processing had higher scores in mathematical achievement. These results indicated that planning and simultaneous information processing had influence on the mathematical achievement.