• 전기전자학회논문지
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• 제16권2호
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• pp.153-158
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• 2012

A new diversity combining scheme is proposed for time-of-arrival (TOA) estimation of chirp signal in dense multipath channel. In the multipath channel, the performance of TOA estimation using conventional correlation matrix-based diversity combining scheme is degraded due to the lack of de-correlation effect. To increase the de-correlation effect, the proposed diversity scheme employs interleaving method based on the property of de-chirped signal. As a result, the proposed scheme increases de-correlation effect and also reduces the noise of TOA estimation. Finally, the diversity achieved from the proposed scheme improves TOA estimation performance. The de-correlation effect is analyzed mathematically. The estimation accuracy of the proposed diversity scheme is superior to that of conventional diversity scheme in multipath channel.

• 한국소음진동공학회논문집
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• 제16권8호
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• pp.872-878
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• 2006

Mass estimation was derived as functions of acceleration magnitude and primary frequency. The conventional method of mass estimation used frequency data directly in the frequency domain. The signals that can be obtained sensor contained noise as well as impact signal. Therefore, how well we can detect the frequency data in noise directly determines the quality of mass estimation. To find exact frequency data, we used time-frequency analysis. The time-frequency methods are expected to be more useful than the conventional frequency domain analyses for the mass estimation problem on a plate type structure. Also it has been concluded that the smoothed WVD can give more reliable means than the other methodologies for the mass estimation in a noisy environment.

• Journal of Positioning, Navigation, and Timing
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• 제10권2호
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• pp.131-137
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• 2021

The Angle-of-Arrival (AOA) estimation in real time is one of core technologies for the real-time tracking system, such as a radar or a satellite. Although AOA estimation algorithms for various antenna types have been studied, most of them are for the single-shaped array antenna suitable to the specific frequency. In this paper, we propose the cascade AOA estimation algorithm for the combined array antenna with Uniform Rectangular Frame Array (URFA) and Uniform Circular Array (UCA), with the excellent performance for various frequencies. The proposed technique is consisted of Capon for roughly finding AOA groups with multiple signal AOAs and Beamspace Multiple Signal Classification (MUSIC) for estimating the detailed signal AOA in the AOA group, for the combined array antenna. In addition, we provide computer simulation results for verifying the estimation performance of the proposed algorithm.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.176-178
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• 2004

Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of speed estimation and control for IPMSM using hybrid intelligent control. The hybrid combination of neural network and adaptive fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using adaptive neural network fuzzy(A-NNF) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed.

• Journal of Communications and Networks
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• 제12권6호
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• pp.624-631
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• 2010

Effective traffic matrix estimation is the basis of efficient traffic engineering, and therefore, quality of service provision support in IP networks. In this study, traffic matrix estimation is investigated in IP networks and an Elman neural network-based traffic matrix inference (ENNTMI) method is proposed. In ENNTMI, the conventional Elman neural network is modified to capture the spatio-temporal correlations and the time-varying property, and certain side information is introduced to help estimate traffic matrix in a network accurately. The regular parameter is further introduced into the optimal equation. Thus, the highly ill-posed nature of traffic matrix estimation is overcome effectively and efficiently.

• 대한의용생체공학회:의공학회지
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• 제26권4호
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• pp.207-214
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• 2005

Breast cancer detection using electrical impedance techniques is based on numerous experimental findings that cancerous tissues have higher electrical conductivity values than normal tissues. Lately, by taking advantage of the structure of current flows underneath a planar probe of array electrodes, a mathematical formula to find lesions from a measured trans­admittance map has been derived. In order to experimentally validate its mathematical analysis and the suggested lesion estimation algorithm, we developed a 256-channel trans-admittance scanner (TAS) for probing anomalies underneath a planar array of electrodes. In this paper, we describe the design and implementation of the TAS. Its performance together with the lesion estimation algorithm was evaluated using saline phantoms. Further studies are proposed to validate the system on human subjects.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 추계학술발표대회
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• pp.7-8
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• 2009

기존해 있는 다수의 멀티미디어 아키텍쳐에서 Motion Estimation Block은 ASIC에 의해 구현되어 왔다. 하지만 유연성의 결여와 NRE cost의 증가로 인해 ASIC에 의한 구현 외에 다양한 방법이 소개되고 있는 추세이다. 본 논문에서는 ASIP을 이용하여 Motion Estimation을 효과적으로 구현할 수 있음을 보이고 있다.

• Journal of Electrical Engineering and information Science
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• 제1권2호
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• pp.119-124
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• 1996

Variable step size LMS(VS-LMS) algorithms improve performance of LMS algorithm by means of varying the step size. This paper presents a new VS-LMS algorithm using normalized absolute estimation error. Normalizing the estimation error to the expected valus of the desired signal, we determined the step size using the relative size of estimation error, Because parameters and computational load are less, our algorithm is easy to implement in hardware. The performance of the proposed algorithm is analyzed theoretically and estimated through simulations. Based on the theoretical analysis and computer simulations, the proposed algorithm is shown to be effective compared to conventional VS-LMS algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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• pp.3498-3511
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• 2016

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.

• 대한원격탐사학회지
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• 제28권3호
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• pp.307-318
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• 2012

To understand forest structures, the Geoscience Laser Altimeter System (GLAS) instrument have been employed to measure and monitor forest canopy with feasibility of acquiring three dimensional canopy structure information. This study tried to examine the potential of GLAS dataset in measuring forest canopy structures, particularly maximum canopy height estimation. To estimate maximum canopy height using feasible GLAS dataset, we simply used difference between signal start and ground peak derived from Gaussian decomposition method. After estimation procedure, maximum canopy height was derived from airborne Light Detection and Ranging (LiDAR) data and it was applied to evaluate the accuracy of that of GLAS estimation. In addition, several influences, such as topographical and biophysical factors, were analyzed and discussed to explain error sources of direct maximum canopy height estimation using GLAS data. In the result of estimation using direct method, a root mean square error (RMSE) was estimated at 8.15 m. The estimation tended to be overestimated when comparing to derivations of airborne LiDAR. According to the result of error occurrences analysis, we need to consider these error sources, particularly terrain slope within GLAS footprint, and to apply statistical regression approach based on various parameters from a Gaussian decomposition for accurate and reliable maximum canopy height estimation.