• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.289-291
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• 2002

Host of source separation methods focus on stationary sources so higher-order statistics is necessary In this paler we consider a problem of source separation when sources are second-order nonstationary stochastic processes . We employ the natural gradient method and develop learning algorithms for both 1inear feedback and feedforward neural networks. Thus our algorithms possess equivariant property Local stabi1iffy analysis shows that separating solutions are always locally stable stationary points of the proposed algorithms, regardless of probability distributions of

• The Journal of Natural Sciences
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• v.8 no.2
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• pp.63-67
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• 1996

Adaptive algorithms based on the higher order error criterion such as the LVCMS and the LMF show performance degradation if input signal contains additive noise with a heavier-tailed density. Conventional analysis often neglects higher order terms in the recursion and my not suit for prediction exact behavior of these higher order algorithms. This paper presents a new convergence analysis which contains all the higher order term in the recursion. The analysis shows that the higher order terms, which are often neglected, dose not affect the upper bound on the step size but the misadjustment. However, the effect decreases sharply proportional to the square of the step size.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.135-151
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• 2022

The generalized displacement method is a nonlinear solution scheme that follows the equilibrium path of the structure based on the development of the generalized displacement. This method traces the path uniformly with a constant amount of generalized displacement. In this article, we first develop higher-order generalized displacement methods based on multi-point techniques. According to the concept of generalized stiffness, a relation is proposed to adjust the generalized displacement during the path-following. This formulation provides the possibility to change the amount of generalized displacement along the path due to changes in generalized stiffness. We, then, introduce higher-order algorithms of variable generalized displacement method using multi-point methods. Finally, we demonstrate with numerical examples that the presented algorithms, including multi-point generalized displacement methods and multi-point variable generalized displacement methods, are capable of following the equilibrium path. A comparison with the arc length method, generalized displacement method, and multi-point arc-length methods illustrates that the adjustment of generalized displacement significantly reduces the number of steps during the path-following. We also demonstrate that the application of multi-point methods reduces the number of iterations.

• Genomics & Informatics
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• v.1 no.2
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• pp.87-93
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• 2003

DNA microarray is currently the most prominent tool for investigating large-scale gene expression data. Different algorithms for measuring gene expression levels from scanned images of microarray experiments may significantly impact the following steps of functional genomic analyses. $Affymetrix^{(R)}$ recently introduced high-density microarrays and new statistical algorithms in Microarray Suit (MAS) version 5.0$^{(R)}$. Very high correlations (0.92 - 0.97) between the new algorithms and the old algorithms (MAS 4.0) across several species and conditions were reported. We found that the column-wise array correlations had a tendency to be much higher than the row-wise gene correlations, which may be much more meaningful in the following higher-order data analyses including clustering and pattern analyses. In this paper, not only the detailed comparison of the two sets of algorithms is illustrated, but the impact of the introducing new algorithms on the further clustering analysis of microarray data and of possible pitfalls in mixing the old and the new algorithms were also described.

• Smart Structures and Systems
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• v.25 no.1
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• pp.37-45
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• 2020

This paper aims at enlightening the properties, computational and implementation issues related to Kalman filter based state estimation algorithms and sliding mode observers, by applying them for estimating the states of a smart structure system. The Kalman based estimators considered in this work are Kalman filter and information filter and, the sliding mode observers considered are Utkin observer and higher order sliding mode observer. A fourth order linear time invariant model of a piezo actuated beam is used in this work. This structure is embedded with four number of piezo patches, of which two act as sensors, one as disturbance actuator and the other as control actuator. The performance of the state estimation algorithms is evaluated through simulation, for the first two vibrating modes of the piezo actuated structure, when the structure is maintained at first mode and second mode resonance.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of computational fluids engineering
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• v.19 no.3
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• pp.52-61
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• 2014

The present paper deals with the efficient computation of higher-order CFD methods for compressible flow using graphics processing units (GPU). The higher-order CFD methods, such as discontinuous Galerkin (DG) methods and correction procedure via reconstruction (CPR) methods, can realize arbitrary higher-order accuracy with compact stencil on unstructured mesh. However, they require much more computational costs compared to the widely used finite volume methods (FVM). Graphics processing unit, consisting of hundreds or thousands small cores, is apt to massive parallel computations of compressible flow based on the higher-order CFD methods and can reduce computational time greatly. Higher-order multi-dimensional limiting process (MLP) is applied for the robust control of numerical oscillations around shock discontinuity and implemented efficiently on GPU. The program is written and optimized in CUDA library offered from NVIDIA. The whole algorithms are implemented to guarantee accurate and efficient computations for parallel programming on shared-memory model of GPU. The extensive numerical experiments validates that the GPU successfully accelerates computing compressible flow using higher-order method.

• The Journal of the Acoustical Society of Korea
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• v.16 no.3
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• pp.81-85
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• 1997

In this paper, a recursive estimation algorithm for FIR systems is proposed using the 3rd and 4th order cumulants. To obtain the Overdetermined Recursive Instrumental Variable(ORIV) method type algorithm, we transform the 3'th and 4'th order cumulant relationship to a certain matrix form which is consist of only output data. From the matrix form, we induce the proposed algorithm procedure following the ORIV method. The proposed algorithm provides improved estimation accuracy with smaller data and can be applied to a time varying system as well. In addition, it reduces the estimation error due to the additive Gaussian noise compared to conventional 2'rd order based algorithms since it only uses higher than 2'rd order cumulant. Simulation results are presented to compare the performance with other HOS-based algorithms.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.7 no.2
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• pp.75-81
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• 2003

Compact schemes are shown to be effective for a class of problems including convection-diffusion equations when combined with multigrid algorithms [7, 8] and V-cycle convergence is proved[5]. We apply the multigrid algorithm for an semianalytic finite difference scheme, which is desinged to preserve high order accuracy despite of singularities.

• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.208-216
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• 2009

A number of estimation of distribution algorithms have been proposed that do not use explicitly crossover and mutation of traditional genetic algorithms, but estimate the distribution of population for more efficient search. But because it is not easy to discover higher-order correlations of variables, lower-order correlations are estimated most cases under various constraints. In this paper, we propose a new estimation of distribution algorithm that represents higher-order correlations of the data and finds global optimum more efficiently. The proposed algorithm represents the higher-order correlations among variables by building random hypergraph model composed of hyperedges consisting of variables which are expected to be correlated, and generates the next population by Bayesian sampling algorithm Experimental results show that the proposed algorithm can find global optimum and outperforms the simple genetic algorithm and BOA(Bayesian Optimization Algorithm) on decomposable functions with deceptive building blocks.