• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.614-620
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• 2007

This paper considers vibration suppression of a mechanical transfer system, where the work is connected with the hand flexibly. We adopt the idea of jerk reduction of the hand. From the equation of motion, we first derive a state equation including the jerk and acceleration of the hand, but excluding the displacement and velocity of the work. Then, we design optimal state feedback for a suitable cost function, and show by simulation that jerk reduction of the hand is effective for vibration suppression of the work and improvement of the settling time. Since state feedback including the jerk and acceleration is not practical, we propose a computation method for optimal feedback using displacements and velocities in the state only.

• Journal of KSNVE
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• v.5 no.4
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• pp.525-536
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• 1995

In this study, an ROC(Reduced Order Controller) is designed to increase the flutter velocity of an aircraft wing, and the effect of ROC on the flight performance is also analyzed. The aircraft wing used in the paper is modelled as a 3 DOF two-dimensional rigid body. In the disign of controller, LQG and BACR(Balanced Augmented Controller Reduction) strategy is used as control algorithm and controller reduction method respectively. Simulation has been conducted to evaluate the effectiveness of ROC on the active flutter control, compared to FOC(Full Order Controller). It has been found that ROC using BACR is much effective than FOC in the sense of computation effort, without sacrificing the active flutter control performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1665-1670
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• 2018

A convolutional neural network (CNN), which is one of the deep learning models, has been very successful in a variety of computer vision tasks. Filters of a CNN are automatically generated, however, they can be further optimized since there exist the possibility of existing redundant and less important features. Therefore, the aim of this paper is a filter reduction to accelerate and compress CNN models. Evolutionary algorithms is adopted to remove the unnecessary filters in order to minimize the parameters of CNN networks while maintaining a good performance of classification. We demonstrate the proposed filter reduction methods performing experiments on CIFAR10 data based on the classification performance. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.179-189
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• 2023

Hybrid sufficient dimension reduction (SDR) methods to a weighted mean of kernel matrices of two different SDR methods by Ye and Weiss (2003) require heavy computation and time consumption due to bootstrapping. To avoid this, Park et al. (2022) recently develop the so-called cross-distance selection (CDS) algorithm. In this paper, two variations of the original CDS algorithm are proposed depending on how well and equally the covk-SAVE is treated in the selection procedure. In one variation, which is called the larger CDS algorithm, the covk-SAVE is equally and fairly utilized with the other two candiates of SIR-SAVE and covk-DR. But, for the final selection, a random selection should be necessary. On the other hand, SIR-SAVE and covk-DR are utilized with completely ruling covk-SAVE out, which is called the smaller CDS algorithm. Numerical studies confirm that the original CDS algorithm is better than or compete quite well to the two proposed variations. A real data example is presented to compare and interpret the decisions by the three CDS algorithms in practice.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.260-263
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• 2000

A technique that uses linear prediction to achieve noise reduction in a voice signal which has been mixed with an ambient noise (Signal to Noise (S-N) ratio = about 0dB) is proposed. This noise reduction method which is based on the linear prediction estimates the voice spectrum while ignoring the spectrum of the noise. The performance of the noise reduction method is first examined using the transversal linear predictor filter. However, with this method there is deterioration in the tone quality of the predicted voice due to the low level of the S-N ratio. An additional processing circuit is then proposed so as to adjust the noise reduction circuit with an aim of improving the problem of tone deterioration. Next, we consider a practical application where the effects of round on errors arising from fixed-point computation has to be minimized. This minimization is achieved by using the lattice predictor filter which in comparison to the transversal type, is Down to be less sensitive to the round-off error associated with finite word length operations. Finally, we consider a practical application where noise reduction is necessary. In this noise reduction method, both the voice spectrum and the actual noise spectrum are estimated. Noise reduction is achieved by using the linear predictor filter which includes the control of the predictor filter coefficient’s update.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.131-137
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• 2011

GPUs were originally designed for graphic processing, and GPGPUs are general-purpose GPUs for numerical computation with high performance and low electric power. In this paper, we implemented the parallel LU factorization program for GPGPUs. In CUDA, which is computational environment for Nvidia GPGPUs, domains are divided into blocks, and multi-threads compute each sub-blocks Simultaneously. In LU factorization program, computation order should be artificially decided due to the data dependence. To resolve the data dependancy, we suggested a parallel LU program for GPGPUs, and also explained parallel reduction algorithm for partial pivoting of LU factorization. We finally present performance analysis to show efficiency of the parallel LU factorization program based on multi-threads on GPGPUs.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.4
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• pp.26-30
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• 2003

The Lanczos algorithm is the most commonly used in approximating a small number of extreme eigenvalues for symmetric large sparse matrices. Global communications in MP(Message Passing) parallel computer decrease the computation speed. In this paper, we introduce the s-step Lanczos method, and s-step method generates reduction matrices which are similar to reduction matrices generated by the standard Lanczos method. One iteration of the s-step Lanczos algorithm corresponds to s iterations of the standard Lanczos algorithm. The s-step method has the minimized global communication and has the superior parallel properties to the standard method. These algorithms are implemented on Cray T3E and performance results are presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.563-568
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• 2007

We propose the computation reduction method of real root method that is used in the EVRC(Enhanced Variable Rate Codec) system. The real root method is that if polynomial equations have the real roots, we are able to find those and transform them into LSP. However, this method takes much time to compute, because the root searching is processed sequentially in frequency region. But, the important characteristic of LSP is that most of coefficients are occurred in specific frequency region. So, to reduce the computation time of real root, we used the met scale that is linear below 1kHz and logarithmic above. In order to compare real root method with proposed method, we measured the following two. First, we compared the position of transformed LSP(Line Spectrum Pairs) parameters in the proposed method with these of real root method. Second, we measured how long computation time is reduced. The experimental result is that the searching time was reduced by about 48% in average without the change of LSP parameters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.4
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• pp.188-194
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• 2014

V2I(Vehicular to Infrastructure) is a one kind of communication systems which is used between the base stations and mobile objects. In V2I communication system, it is difficult to obtain the desired communication performance. Beamforming technology is to find the optimal path. and it can be improved the communication performance. MUSIC algorithm can be estimated the direction of arrival. The directional vector of received signals and the eigenvector has orthogonal property. MUSIC algorithm uses this property. In V2I communication environment, real time optimal path is changed. By the high computational complexity of the MUSIC algorithm, the optimal path estimation error is generated. In this paper, we propose a method of computation reduction algorithm for MUSIC algorithm.

• Journal of the Korean School Mathematics Society
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• v.25 no.4
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• pp.307-332
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• 2022

This action research was carried out to help students learn fractions computation by making and using semi-concrete fraction manipulatives that can be used continuously in math classes. For this purpose, the researcher and students made semi-concrete fraction manipulatives and learned how to use these through reviewing the previously learned fraction contents over 4 class sessions. Afterward, through the 14 classes (7 classes for learning to reduce fractions and to a common denominator, 7 classes for adding and subtracting fractions with different denominators) in which the principle inquiry learning model was applied, students actively engaged in learning activities with fraction manipulatives and explored the principles underneath the manipulations of fraction manipulatives. Students could represent various fractions using fraction manipulatives and solve fraction computation problems using them. The achievement evaluation after class found that the students could connect the semi-concrete fraction manipulatives with fraction representation and symbolic formulas. Moreover, the students showed interest and confidence in mathematics through the classes using fraction manipulatives.