• Title/Summary/Keyword: Normalized Algorithm

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The Study on the Performance Improvement and the Development of Active Intake Noise Control System (능동홉기소음제어 시스템의 개발 및 성능향상에 관한 연구)

  • 이충휘;오재응;심현진;이유엽
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2002.05a
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    • pp.326-329
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    • 2002
  • Engine noise is one of the major causes of the interior noise, and so has been studied in various ways in recent days. Recently Intake noise has been extensively studied to reduce the engine noise. Conventional method to reduce the noise is adding several resonators to the induction system. However this causes a reduction of engine output power and an increase of fuel consumption. In this study, the prototype of Active Intake Noise Control System is developed by using the Filtered-x LMS algorithm to reduce the Intake noise during acceleration. Intake noise is more excessively increased when the engine is rapidly accelerated. So, Normalized LMS algorithm is applied to improve the control performance under the rapid acceleration.

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Probabilistic Target Speech Detection and Its Application to Multi-Input-Based Speech Enhancement (확률적 목표 음성 검출을 통한 다채널 입력 기반 음성개선)

  • Lee, Young-Jae;Kim, Su-Hwan;Han, Seung-Ho;Han, Min-Soo;Kim, Young-Il;Jeong, Sang-Bae
    • Phonetics and Speech Sciences
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    • v.1 no.3
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    • pp.95-102
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    • 2009
  • In this paper, an efficient target speech detection algorithm is proposed for the performance improvement of multi-input speech enhancement. Using the normalized cross correlation value between two selected channels, the proposed algorithm estimates the probabilistic distribution function of the value from the pure noise interval. Then, log-likelihoods are calculated with the function and the normalized cross correlation value to detect the target speech interval precisely. The detection results are applied to the generalized sidelobe canceller-based algorithm. Experimental results show that the proposed algorithm significantly improves the speech recognition performance and the signal-to-noise ratios.

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New accuracy indicator to quantify the true and false modes for eigensystem realization algorithm

  • Wang, Shuqing;Liu, Fushun
    • Structural Engineering and Mechanics
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    • v.34 no.5
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    • pp.625-634
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    • 2010
  • The objective of this paper is to apply a new proposed accuracy indicator to quantify the true and false modes for Eigensystem Realization Algorithm using output-based responses. First, a discrete mass-spring system and a simply supported continuous beam were modelled using finite element method. Then responses are simulated under random excitation. Natural Excitation Technique using only response measurements is applied to compute the impulse responses. Eigensystem Realization Algorithm is employed to identify the modal parameters on the simulated responses. A new accuracy indicator, Normalized Occurrence Number-NON, is developed to quantitatively partition the realized modes into true and false modes so that the false portions can be disregarded. Numerical simulation demonstrates that the new accuracy indicator can determine the true system modes accurately.

Improved Image Restoration Algorithm about Vehicle Camera for Corresponding of Harsh Conditions (가혹한 조건에 대응하기 위한 차량용 카메라의 개선된 영상복원 알고리즘)

  • Jang, Young-Min;Cho, Sang-Bock;Lee, Jong-Hwa
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.2
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    • pp.114-123
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    • 2014
  • Vehicle Black Box (Event Data Recorder EDR) only recognizes the general surrounding environments of load. In addition, general EDR is difficult to recognize the images of a sudden illumination change. It appears that the lens is being a severe distortion. Therefore, general EDR does not provide the clues of the circumstances of the accident. To solve this problem, we estimate the value of Normalized Luminance Descriptor(NLD) and Normalized Contrast Descriptor(NCD). Illumination change is corrected using Normalized Image Quality(NIQ). Second, we are corrected lens distortion using model of Field Of View(FOV) based on designed method of fisheye lens. As a result, we propose integration algorithm of two methods that correct distortions of images using each Gamma Correction and Lens Correction in parallel.

Development of Algorithm for Stereoscopic PIV using Normalized Cross-correlation (정규상호상관도를 이용한 입체 입자영상유속계 알고리즘 개발)

  • Oh, Jung-Keun;Kim, Yoo-Chul;Ryu, Min-Cheol;Koh, Won-Kyou;Suh, Jung-Chun
    • Journal of the Society of Naval Architects of Korea
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    • v.44 no.6
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    • pp.579-589
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    • 2007
  • Contrary to the conventional single-point measuring devices such as LDV, pitot-tube, hot-wire, etc., it would be possible to measure instantaneously 3-D flow fields with a stereoscopic PIV system. In this paper, we present an analysis algorithm for a stereoscopic PIV system using the normalized cross-correlation (NCC) and a 3-D calibration based reconstruction method. The evaluation method based on NCC is one of the most accurate correlation-based methods. We validated the developed algorithm through a benchmarking comparison with 3-D artificial SPIV images and calibration target images.

Research about Adjusted Step Size NLMS Algorithm Using SNR (신호 대 잡음비를 이용한 Adjusted Step Size NLMS알고리즘에 관한 연구)

  • Lee, Jae-Kyun;Park, Jae-Hoon;Lee, Chae-Wook
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.4C
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    • pp.305-311
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    • 2008
  • In this paper, we proposed an algorithm for adaptive noise cancellation (ANC) using the variable step size normalized least mean square (VSSNLMS) in real-time automobile environment. As a basic algorithm for ANC, the LMS algorithm has been used for its simplicity. However, the LMS algorithm has problems of both convergence speed and estimation accuracy in real-time environment. In order to solve these problems, the VSSLMS algorithm for ANC is considered in nonstationary environment. By computer simulation using real-time data acquisition system(USB 6009), VSSNLMS algorithm turns out to be more effective than the LMS algorithm in both convergence speed and estimation accuracy.

Learning Algorithm using a LVQ and ADALINE (LVQ와 ADALINE을 이용한 학습 알고리듬)

  • 윤석환;민준영;신용백
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.19 no.39
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    • pp.47-61
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    • 1996
  • We propose a parallel neural network model in which patterns are clustered and patterns in a cluster are studied in a parallel neural network. The learning algorithm used in this paper is based on LVQ algorithm of Kohonen(1990) for clustering and ADALINE(Adaptive Linear Neuron) network of Widrow and Hoff(1990) for parallel learning. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists of 250 patterns of ASCII characters normalized into $8\times16$ and 1124. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists 250 patterns of ASCII characters normalized into $8\times16$ and 1124 samples acquired from signals generated from 9 car models that passed Inductive Loop Detector(ILD) at 10 points. In ASCII character experiment, 191(179) out of 250 patterns are recognized with 3%(5%) noise and with 1124 car model data. 807 car models were recognized showing 71.8% recognition ratio. This result is 10.2% improvement over backpropagation algorithm.

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A Very Efficient Redundancy Analysis Method Using Fault Grouping

  • Cho, Hyungjun;Kang, Wooheon;Kang, Sungho
    • ETRI Journal
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    • v.35 no.3
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    • pp.439-447
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    • 2013
  • To increase device memory yield, many manufacturers use incorporated redundancy to replace faulty cells. In this redundancy technology, the implementation of an effective redundancy analysis (RA) algorithm is essential. Various RA algorithms have been developed to repair faults in memory. However, nearly all of these RA algorithms have low analysis speeds. The more densely compacted the memory is, the more testing and repair time is needed. Even if the analysis speed is very high, the RA algorithm would be useless if it did not have a normalized repair rate of 100%. In addition, when the number of added spares is increased in the memory, then the memory space that must be searched with the RA algorithms can exceed the memory space within the automatic test equipment. A very efficient RA algorithm using simple calculations is proposed in this work so as to minimize both the repair time and memory consumption. In addition, the proposed algorithm generates an optimal solution using a tree-based algorithm in each fault group. Our experiment results show that the proposed RA algorithm is very efficient in terms of speed and repair.

Fast and Accurate Algorithm for Motion Estimation in Mobile Environments (모바일 환경에서 모션 추정을 위한 빠르고 정확한 알고리즘)

  • Kim, Jun-Ho;Oh, Il-Seok
    • The Journal of the Korea Contents Association
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    • v.10 no.3
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    • pp.1-9
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    • 2010
  • In this paper, we propose a new method of improving accuracy of motion estimation in mobile environments, compared with Rosten's algorithm. The present method selects corners as feature points. The Rosten's algorithm uses simple addition and subtraction to detect the corners. Although it has the advantage of faster processing speed, Rosten's algorithm has a drawback of low performance in motion estimation. We use the NCC(Normalized Cross Correlation) coefficients to match the corners, and remove in two steps the outliers of inaccurate matching corners. We compare the proposed algorithm with Rosten's algorithm by applying both to the real images. We find that the proposed method shows better performance than Rosten's algorithm in motion estimation. In addition, we implement the present method on mobile devices and confirm that it works in mobile environments in real time.

Performance Improvement of Acoustic Echo Canceller Using Post-Processor (후처리기를 이용한 음향 반향 제거기의 성능향상)

  • 박장식;김현태;손경식
    • The Journal of the Acoustical Society of Korea
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    • v.18 no.5
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    • pp.35-43
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    • 1999
  • In this paper, a new robust adaptive algorithm and a post-processing method are proposed to improve the performance of AEC without computational burden. Its step-size is normalized by the sum of the powers of the reference input signal and the desired signal. When the near-end speaker's speech and noise are applied into the microphone, the step-size becomes small and the misalignment of coefficients are reduced. To reduce the residual echoes, a new post-processing method, which is co-operated with the proposed noise-robust adaptive algorithm, is proposed in this paper. The method is based on the correlation of the desired signal and the estimation error signal. The residual echoes are attenuated as proportional to the correlation normalized with the power of desired signals. The normalized correlation plays a role as Wiener filter for residual echoes. In the double-talk situation, the estimation error signals, that are residual echoes, dominantly include the near-end speaker's speech and the normalized correlation closes to 1. Therefore, the near-end speaker's speech can be transmitted without being attenuated. When the desired signals consists of only the acoustic echoes, the residual echoes are mostly attenuated and canceled by the proposed post-processor. The computation of AEC using the proposed post-processor is comparable to NLMS algorithm.

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