• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.201-211
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• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.209-215
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• 2012

This paper deals with a reduction of cogging torque and estimate of inductance for IPMSM. The flux barriers(Barrier) and the auxiliary slot(Notch) for the reduction of cogging torque was installed for increase of driving characteristic in IPMSM. The cogging torque, driving torque and inductance are analyzed by using FEM(Finite Element Method) and the results of inductance calculation are compared to experimentation ones.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.418-434
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• 2018

With the increasing use of the Internet and electronic documents, automatic text categorization becomes imperative. Several machine learning algorithms have been proposed for text categorization. The k-nearest neighbor algorithm (kNN) is known to be one of the best state of the art classifiers when used for text categorization. However, kNN suffers from limitations such as high computation when classifying new instances. Instance selection techniques have emerged as highly competitive methods to improve kNN through data reduction. However previous works have evaluated those approaches only on structured datasets. In addition, their performance has not been examined over the text categorization domain where the dimensionality and size of the dataset is very high. Motivated by these observations, this paper investigates and analyzes the impact of instance selection on kNN-based text categorization in terms of various aspects such as classification accuracy, classification efficiency, and data reduction.

• Journal of computational fluids engineering
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• v.17 no.4
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• pp.9-15
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• 2012

Thermal model reduction algorithms and techniques are introduced to condense a huge satellite panel thermal model into the simplified model on the purpose of calculating the thermal responses of a satellite on orbit. Guyan condensation algorithm with the substitution matrix manipulation is developed and the mathematical procedure is depicted step by step. A block-form LU decomposition method is also invited to compare the developed algorithm. The constructed reduced thermal model induced from the detailed model based on a real satellite panel is satisfying the correlation criterion of ${\pm}2^{\circ}C$ for the validity accuracy. Guyan condensation algorithm is superior to the block-form LU decomposition method on computation time.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.692-704
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• 2005

With the improvement of computer hardware, GPUs(Graphics Processor Units) have tremendous memory bandwidth and computation power. This leads GPUs to use in general purpose computation. Especially, GPU implementation of compute-intensive physics based simulations is actively studied. In the solution of differential equations which are base of physics simulations, tridiagonal matrix systems occur repeatedly by finite-difference approximation. From the point of view of physics based simulations, fast solution of tridiagonal matrix system is important research field. We propose a fast GPU implementation for the solution of tridiagonal matrix systems. In this paper, we implement the cyclic reduction(also known as odd-even reduction) algorithm which is a popular choice for vector processors. We obtained a considerable performance improvement for solving tridiagonal matrix systems over Thomas method and conjugate gradient method. Thomas method is well known as a method for solving tridiagonal matrix systems on CPU and conjugate gradient method has shown good results on GPU. We experimented our proposed method by applying it to heat conduction, advection-diffusion, and shallow water simulations. The results of these simulations have shown a remarkable performance of over 35 frame-per-second on the 1024x1024 grid.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.92-99
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• 2002

To obtain visual information of a target object, a camera should be placed within the visibility region. As the visibility region is dependent on the relative position of the target object and the surrounding object, the position change of the surrounding object during a task requires recalculation of the visibility region. For a fast computation of the visibility region so as to modify the camera position to be located within the visibility region, we propose a spherical projection method. After being projected onto the sphere the visibility region is represented in $\theta$-$\psi$ spaces of the spherical coordinates. The reduction of calculation space enables a fast modification of the camera location according to the motion of the surrounding objects so that the continuous observation of the target object during the task is possible.

• Journal of Information Technology Applications and Management
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• v.22 no.3
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• pp.105-114
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• 2015

This paper presents a fast Time Difference of Arrival (TDOA) estimation for sound source localization. TDOA is the time difference between the arrival times of a signal at two sensors. We propose a partial cross correlation method to increase the speed of TDOA estimation for sound source localization. We do this by predicting which part of the cross correlation function contains the required TDOA value with the help of the signal energies, and then we compute the cross correlation function in that direction only. Experiments show approximately 50% reduction in the cross correlation computation time thereby increasing the speed of TDOA computation. This makes it very relevant for real world surveillance.

• Journal of Mechanical Science and Technology
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• v.18 no.4
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• pp.718-730
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• 2004

The solution for the natural convection in internally finned horizontal annuli is obtained by using a numerical simulation of time-dependent and two-dimensional governing equations. The fins existing in annuli influence the flow pattern, temperature distribution and heat transfer rate. The variations of the On configuration suppress or accelerate the free convective effects compared to those of the smooth tubes. The effects of fin configuration, number of fins and ratio of annulus gap width to the inner cylinder radius on the fluid flow and heat transfer in annuli are demonstrated by the distribution of the velocity vector, isotherms and streamlines. The governing equations are solved efficiently by using a parallel implementation. The technique is adopted for reduction of the computation cost. The parallelization is performed with the domain decomposition technique and message passing between sub-domains on the basis of the MPI library. The results from parallel computation reveal in consistency with those of the sequential program. Moreover, the speed-up ratio shows linearity with the number of processor.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.127-130
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• 2007

The conventional feature recombination technique is very effective in the band-limited noise condition, but in broad-band noise condition, the conventional feature recombination technique does not produce notable performance improvement compared with the full-band system. To cope with this drawback, we introduce a new technique of sub-band likelihood computation in the feature recombination, and propose a new feature recombination method by using this sub-band likelihood computation. Furthermore, the reliable sub-band selection based on the signal-to-noise ratio is used to improve the performance of this proposed feature recombination. Experimental results shows that the average error reduction rate in various noise condition is more than 27% compared with the conventional full-band speaker identification system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.757-761
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• 2001

In order to study noise barriers of complex shapes and to assess their efficiency, precise prediction model is required. For instance, geometrical approaches cannot deal with complex diffraction effects. So that in this paper, the time domain numerical computation method(Computational Aeroacoustics method) is applied to estimate noise reduction by diffraction and finite impedance condition. The CAA method can be used to calculate exactly the pressure of complex barrier shape with different impedance condition, such as T-shape, cylindrical edge and multi-edge noise barriers.