• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.2
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• pp.143-149
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• 2011

In this paper, IIR structures are proposed to reduce the computation complexity of the secondary-pass estimation in active noise control(ANC) systems. However, there are stability problems of using IIR models to reduce the computation complexity in ANC systems. To overcome these problems, we propose a stabilizing procedure of recursive least mean squares (RLMS) algorithms for eatimating the parameters of IIR models of the secondary path transfer functions. The multichannel ANC systems are implemented by using the TMS320C6713 DSP board to test the performance of computation complexity and stability of the proposed methods. Comparing the IIR filters with the FIR filters, the IIR filters can reduce 50[%] of the computation and obtain similar noise reduction result.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1210-1212
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• 1987

Adaptive control for robot manipulator controller has been considered as an effective approach because robot dynamic models contain the nonlinearities and uncertainties. This paper present an approach for the position and velocity control of a manipulator by using the seif-tuning type controller for each point. The complicated model manipulator system is modeled by a set of time series difference equation. The parameters of the models are determined by online recursive algorithms. Finally some remarks on the effectiveness and applications of adaptive controller are discussed.

• Communications of the Korean Mathematical Society
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• v.14 no.2
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• pp.405-423
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• 1999

We consider a statistical multiplexer model with finite buffer capacity and finite number of independent identical 3-state bursty voice sources. The burstiness of the sources is modeled by describing both two different active periods (at the rate of one packet perslot) and the passive periods during which no packets are generated. Assuming a mixture of two geometric distributions for active period and a geometric distribution for passive period and geometric distribution for passive period, we derive the recursive algorithm for the probability mass function of the buffer contents (in packets). We also obtain loss probability and the distribution of packet delay. Numerical results show that the system performance deteriorates considerably as the variance of the active period increases. Also, we see that the loss probability of 2-state Markov models is less than that of 3-state Markov models.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.65-86
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• 2000

The estimates from an EM when it is applied to a large causal model of 10 or more categorical variables are often subject to the initial values for the estimates. This phenomenon becomes more serious as the model structure becomes more serious as the model structure becomes more complicated involving more variables. In this regard Wu(1983) recommends among others that EMs are implemented several times with different sets of initial values to obtain more appropriate estimates. in this paper a new approach for initial values is proposed. The main idea is that we use initials that are calibrated to data. A simulation result strongly indicates that the calibrated initials give rise to the estimates that are far closer to the true values than the initials that are not calibrated.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.293-301
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• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.101-101
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• 2020

Traditional stochastic simulation of hydroclimatological variables often underestimates the variability and correlation structure of larger timescale due to the difficulty in preserving long-term memory. However, the Long Short-Term Memory (LSTM) model illustrates a remarkable long-term memory from the recursive hidden and cell states. The current study, therefore, employed the LSTM model in stochastic generation of hydrologic and climate variables to examine how much the LSTM model can preserve the long-term memory and overcome the drawbacks of conventional time series models such as autoregressive (AR). A trigonometric function and the Rössler system as well as real case studies for hydrological and climatological variables were tested. Results presented that the LSTM model reproduced the variability and correlation structure of the larger timescale as well as the key statistics of the original time domain better than the AR and other traditional models. The hidden and cell states of the LSTM containing the long-memory and oscillation structure following the observations allows better performance compared to the other tested conventional models. This good representation of the long-term variability can be important in water manager since future water resources planning and management is highly related with this long-term variability.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.65-70
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• 2005

This paper introduces an approach to multi-layered product knowledge model for collaborative engineering environment. The participants in collaborative engineering want to share and reason product knowledge through internet without any heterogeneity and ambiguity. However the previous knowledge models are limited in providing those aspects. In this paper, the collaborative engineering domain is analyzed and then the product knowledge is organized into four levels such as product context model, product specific model, product design model and product manufacturing model. The four levels are represented by first-order logic in layered fashion. The concepts and the instances of a formal ontology are used for recursive representation of the four levels. The instances of the concepts of an upper level like product context model are considered as the concepts of an adjacent lower level like product specific model, and this mechanism is applied to the other levels. These logic representations are integrated with the schema and the instances of a relational database. OWL representation of the four levels is defined through the integration of the logic representation and OWL primitives. The four product knowledge models have their major representation according to the characteristics of each model. This approach enables engineer to share product knowledge through internet without any ambiguity and utilize it as basis for additional reasoning.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2949-2952
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• 2000

A new neural networks and learning algorithm are proposed in order to measure nonlinear heights of complexed road environments in realtime without pre-information. This new neural networks is Error Self Recurrent Neural Networks(ESRN), 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 back-propagation 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. We can estimate nonlinear models in realtime by ESRN and learning algorithm and control nonlinear models. To show the performance of this one. we control 7 degree of freedom full car model with several control method. From this simulation. this estimation and controller were proved to be effective to the measurements of nonlinear road environment systems.

• Journal of Ocean Engineering and Technology
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• v.36 no.3
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• pp.161-167
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• 2022

Three-dimensional finite element analysis (3D-FEA) models have been used to evaluate the shock-resistance responses of various equipments, including armaments mounted on a warship caused by underwater explosion (UNDEX). This paper aims to check the possibility of using one-dimensional (1D) FEA models for the shock-resistance responses. A frigate was chosen for the evaluation of the shock-resistance responses by the UNDEX. The frigate was divided into the thirteen discrete segments along the length of the ship. The 1D Timoshenko beam elements were used to model the frigate. The explosive charge mass and the stand-off distance were determined based on the ship length and the keel shock factor (KSF), respectively. The UNDEX pressure fields were generated using the Geers-Hunter doubly asymptotic model. The pseudo-velocity shock response spectrum (PVSS) for the 1D-FEA model (1D-PVSS) was calculated using the acceleration history at a concerned equipment position where the digital recursive filtering algorithm was used. The 1D-PVSS was compared with the 3D-PVSS that was taken from a reference, and a relatively good agreement was found. In addition, the 1D-PVSS was compared with the design criteria specified by the German Federal Armed forces, which is called the BV043. The 1D-PVSS was proven to be relatively reasonable, reducing the computing cost dramatically.