• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.197-205
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• 2014

In applications of adaptive noise control or active noise control, the presence of a transfer function in the secondary path following the adaptive controller and the error path, been shown to generally degrade the performance of the Least Mean Square (LMS) algorithm. Thus, the convergence rate is lowered, the residual power is increased, and the algorithm can become unstable. In general, in order to solve these problems, the filtered-x LMS (FX-LMS) type algorithms can be used. But these algorithms have slow convergence speed and weakness in the environment that the secondary path and error path are varied. Therefore, I present the new algorithm called the "Bi-directional Filtered-x (BFX) LMS" algorithm with nearly equal computation complexity. Through experimental study, the proposed BFX-LMS algorithm has better convergence speed and better performance than the conventional FX-LMS algorithm, especially when the secondary path or error path is varied and the impulsive disturbance is flow in.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.259-270
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• 2013

This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.2
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• pp.57-66
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• 2010

In this paper, we propose an enhanced voice recognition algorithm using adaptive MFCC(Mel Frequency Cepstral Coefficients) and neural network. Though it is very important to extract voice data from the raw data to enhance the voice recognition ratio, conventional algorithms are subject to deteriorating voice data when they eliminate noise within special frequency band. Differently from the conventional MFCC, the proposed algorithm imposed bigger weights to some specified frequency regions and unoverlapped filterbank to enhance the recognition ratio without deteriorating voice data. In simulation results, the proposed algorithm shows better performance comparing with MFCC since it is robust to variation of the environment.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.93-95
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• 2006

In many control system applications, the system designed must not only satisfy the damping and accuracy specifications, but the control must also yield performance that is robust to external disturbance and parameter variations. We have shown that feedback in conventional control systems has the inherent ability of reducing the effects of external disturbance and parameter variations. Unfortunately, robustness with the conventional feedback configuration is achieved only with a high loop gain, which is normally detrimental to stability. The design of intelligent, autonomous machines to perform tasks that are dull, repetitive, hazardous, or that require skill, strength, or dexterity beyond the capability of humans is the ultimate goal of robotics research. This paper prove the robust control using Analog Adaptive Resonance Theorv(ART2) Algorithm about case study.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.279-284
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• 1999

Although the switched reluctance motor (SRM) has a several advantages such as simple magnetic structure, robustness, wide range of speed characteristics and simple driving, it has a considerable inherent torque ripple and speed variation duet to the driving characteristics of pulse current waveform and the nonlinear inductance profile. The high torque ripple and speed variation inhibits wide application. The minimization of the torque ripple is very important in high performance servo drive applications, which require smooth operation with minimum torque pulsations. This paper presents the new SRM torque modeling technique for the control of instantaneous torque. The SRM is modeled by the database of torque profiles for every small variation in currents and rotor angles, which is inferred from the several measured data by the adaptive neuro-fuzzy inference technique. Simulation results demonstrating the effectiveness of proposed torque modeling technique are presented.

• International Journal of Contents
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• v.8 no.4
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• pp.7-11
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• 2012

Cloud Computing can be viewed as a dynamically-scalable pool of resources. Virtualization is one of the key technologies enabling Cloud Computing functionalities. Virtual machines (VMs) scheduling and allocation is essential in Cloud Computing environment. In this paper, two dynamic VMs scheduling and allocating schemes are presented and compared. One dynamically on-demand allocates VMs while the other deploys optimal threshold to control the scheduling and allocating of VMs. The aim is to dynamically allocate the virtual resources among the Cloud Computing applications based on their load changes to improve resource utilization and reduce the user usage cost. The schemes are implemented by using SimPy, and the simulation results show that the proposed adaptive scheme with one threshold can be effectively applied in a Cloud Computing environment both performance-wise and cost-wise.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.668-672
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• 2009

In wireless ad-hoc network, knowing the available bandwidth of the time varying channel is imperative for live video streaming applications. This is because the available bandwidth is varying all the time and strictly limited against the large data size of video streaming. Additionally, adapting the encoding rate to the suitable bit-rate for the network, where an overlarge encoding rate induces congestion loss and playback delay, decreases the loss and delay. While some effective rate controlling methods have been proposed and simulated well like VTP (Video Transport Protocol) [1], implementing to cooperate with the encoder and tuning the parameters are still challenging works. In this paper, we show our result of the implementation experiment of VTP based encoding rate controlling method and then introduce some techniques of our parameter tuning for a video streaming application over wireless environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1733-1751
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• 2015

The IEEE 802.15.4 standard not only provides a maximum of seven guaranteed time slots (GTSs) for allocation within a superframe to support time-critical traffic, but also achieves ultralow complexity, cost, and power in low-rate and short-distance wireless personal area networks (WPANs). Real-time wireless body area sensor networks (WBASNs), as a special purpose WPAN, can perfectly use the IEEE 802. 15. 4 standard for its wireless connection. In this paper, we propose an adaptive GTS allocation scheme for real-time WBASN data transmissions with different priorities in consideration of low latency, fairness, and bandwidth utilization. The proposed GTS allocation scheme combines a weight-based priority assignment algorithm with an innovative starvation avoidance scheme. Simulation results show that the proposed method significantly outperforms the existing GTS implementation for the traditional IEEE 802.15.4 in terms of average delay, contention free period bandwidth utilization, and fairness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1846-1863
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• 2016

High dynamic range (HDR) images can present the perfect real scene and rich color information. A commonly encountered problem in practical applications is how to well visualize HDR images on standard display devices. In this paper, we propose a multi-scale decomposition method using guided filtering for HDR image tone mapping. In our algorithm, HDR images are directly decomposed into three layers：base layer, coarse scale detail layer and fine detail layer. We propose an effective function to compress the base layer and the coarse scale detail layer. An adaptive function is also proposed for detail adjustment. Experimental results show that the proposed algorithm effectively accomplishes dynamic range compression and maintains good global contrast as well as local contrast. It also presents more image details and keeps high color saturation.

• Journal of the Optical Society of Korea
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• v.10 no.1
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• pp.23-27
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• 2006

We are studying the application of an adaptive optics system to upgrade the beam quality of a laser. The adaptive optics (AO) system consists of a bimorph deformable mirror, a Shack-Hartmann sensor and a control system. In most AO applications, the beam aperture is considered to be circular. However, in some cases such as laser beams from unstable resonators, the beam apertures are annulus or a holed-rectangle. In this paper, we investigate how well a bimorph deformable mirror of ${\Phi}120\;mm$ clear aperture can compensate phase distortions for three different beam configurations; 1) ${\Phi}120\;mm$ circular aperture, 2) ${\Phi}100\;mm$ annulus aperture with a ${\Phi}20\;mm$ hole and 3) $70\;mm{\times}70\;mm$ square aperture with a hole of $30\;mm{\times}30\;mm$. This study concludes that the bimorph mirror, which might be considered as a modal controller, can compensate tilt, defocus, coma and astigmatism, and spherical aberration for all three beams.