• Title/Summary/Keyword: Noise robustness

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On the Noise Robustness of Multilayer Perceptrons (다층퍼셉트론의 잡음 강건성)

  • 오상훈
    • Proceedings of the Korea Contents Association Conference
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    • 2003.11a
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    • pp.213-217
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    • 2003
  • In this paper, we analysize the noise robustness of MLPs(Multilayer perceptrons). Also, as a preprocessing stage of MLPs to improve noise robustness, we consider the ICA(independent component analysis) and PCA(principle component analysis). After analyzing the noise redunction effect using PCA or ICA, we verify the noise robustness of MLPs through handwritten-digit recognition simulations.

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Improvement in rise time and robustness of AC servomotor (AC servo motor 제어시 rise time 과 강인성 개선)

  • 정광조;임선종
    • 제어로봇시스템학회:학술대회논문집
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    • 1991.10a
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    • pp.446-450
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    • 1991
  • PID controller is popular but have defect inversing following reference input and noise elimination. Therefor, this paper focus on reducing rise time and robustness against noise. The result that is simulated with feedforward method and sliding mode show that rise time decrease and robustness increase.

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An Analysis of Noise Robustness for Multilayer Perceptrons and Its Improvements (다층퍼셉트론의 잡음 강건성 분석 및 향상 방법)

  • Oh, Sang-Hoon
    • The Journal of the Korea Contents Association
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    • v.9 no.1
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    • pp.159-166
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    • 2009
  • In this paper, we analyse the noise robustness of MLPs(Multilayer perceptrons) through deriving the probability density function(p.d.f.) of output nodes with additive input noises and the misclassification ratio with the integral form of the p.d.f. functions. Also, we propose linear preprocessing methods to improve the noise robustness. As a preprocessing stage of MLPs, we consider ICA(independent component analysis) and PCA(principle component analysis). After analyzing the noise reduction effect using PCA or ICA in the viewpoints of SNR(Singal-to-Noise Ratio), we verify the preprocessing effects through the simulations of handwritten-digit recognition problems.

Robustness of a Sensorless Algorithm for Switched Reluctance Motor Considering Noise (노이즈 성분을 고려한 SRM 센서리스 알고리즘의 강인성)

  • 최재동
    • Proceedings of the KIPE Conference
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    • 2000.07a
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    • pp.717-720
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    • 2000
  • The sensorless scheme for Switched Reluctance Motor(SRM) dives must have the robustness and reliability because the noise and error are sensitive. These elements make electrically noisy environments due to the proximity of high current power circuits with small signal electronic circuits when SRM drives. Also the leakage inductances and finite coupling capacitances these can cause the noise on any low voltage current and voltage measurement. the error can occur because the current and voltage including the noise are used as the input of sensorless algorithm In this paper the high robustness and resistance of input noise are described and the fuzzy logic based rotor estimation algorithm is used to reduce the tolerance of input data.

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Analysis of Uniqueness and Robustness Properties of Ordinal Signature for Video Matching (비디오 정합을 위한 오디널 특징의 유일성 및 강건성 분석)

  • Jeong Kwang-Min;Kim Jeong-Yeop;Hyun Ki-Ho;Ha Yeong-Ho
    • Journal of Korea Multimedia Society
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    • v.9 no.5
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    • pp.576-584
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    • 2006
  • Content-based video matching is measuring a similarity of video signature compared to the original clip and copies of media. Specially, it is very important to match the exact frame position, but it depends on frame rate, noise condition and compression format of video. Ordinal signature shows good performance than other video signatures under normal condition but the previous didn't try to find the uniqueness and robustness. Hua et al. performed a uniqueness test under compressed in different formats or frame size. However, they used other compression format image instead of noise in robustness test. This paper proposes robustness test method using several noise models and analyzes the performance of robustness and uniqueness.

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A damage localization method based on the singular value decomposition (SVD) for plates

  • Yang, Zhi-Bo;Yu, Jin-Tao;Tian, Shao-Hua;Chen, Xue-Feng;Xu, Guan-Ji
    • Smart Structures and Systems
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    • v.22 no.5
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    • pp.621-630
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    • 2018
  • Boundary effect and the noise robustness are the two crucial aspects which affect the effectiveness of the damage localization based on the mode shape measurements. To overcome the boundary effect problem and enhance the noise robustness in damage detection, a simple damage localization method is proposed based on the Singular Value Decomposition (SVD) for the mode shape of composite plates. In the proposed method, the boundary effect problem is addressed by the decomposition and reconstruction of mode shape, and the noise robustness in enhanced by the noise filtering during the decomposition and reconstruction process. Numerical validations are performed on plate-like structures for various damage and boundary scenarios. Validations show that the proposed method is accurate and effective in the damage detection for the two-dimensional structures.

Frequency-Temporal Filtering for a Robust Audio Fingerprinting Scheme in Real-Noise Environments

  • Park, Man-Soo;Kim, Hoi-Rin;Yang, Seung-Hyun
    • ETRI Journal
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    • v.28 no.4
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    • pp.509-512
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    • 2006
  • In a real environment, sound recordings are commonly distorted by channel and background noise, and the performance of audio identification is mainly degraded by them. Recently, Philips introduced a robust and efficient audio fingerprinting scheme applying a differential (high-pass filtering) to the frequency-time sequence of the perceptual filter-bank energies. In practice, however, the robustness of the audio fingerprinting scheme is still important in a real environment. In this letter, we introduce alternatives to the frequency-temporal filtering combination for an extension method of Philips' audio fingerprinting scheme to achieve robustness to channel and background noise under the conditions of a real situation. Our experimental results show that the proposed filtering combination improves noise robustness in audio identification.

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Robustness of 2nd-order Iterative Learning Control for a Class of Discrete-Time Dynamic Systems

  • Kim, Yong-Tae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.3
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    • pp.363-368
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    • 2004
  • In this paper, the robustness property of 2nd-order iterative learning control(ILC) method for a class of linear and nonlinear discrete-time dynamic systems is studied. 2nd-order ILC method has the PD-type learning algorithm based on both time-domain performance and iteration-domain performance. It is proved that the 2nd-order ILC method has robustness in the presence of state disturbances, measurement noise and initial state error. In the absence of state disturbances, measurement noise and initialization error, the convergence of the 2nd-order ILC algorithm is guaranteed. A numerical example is given to show the robustness and convergence property according to the learning parameters.

S-Band Low Noise Amplifier Based on GaN HEMT for High Input Power Robustness (고입력 내성을 위한 GaN HEMT 기반 S-대역 저잡음 증폭기)

  • Kim, Hong-Hee;Kim, Sang-Hoon;Choi, Jin-Joo;Choi, Gil-Wong;Kim, Hyoung-Joo
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.26 no.2
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    • pp.165-170
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    • 2015
  • In this paper, we present design and measurement of LNA(Low Noise Amplifier) based on GaN HEMT(Gallium Nitride High Electron Mobility Transistor) to reduce the total noise figure of radar receiver and for robustness of LNA. In radar receiver using LNA based on GaAs(Gallium Arsenide) technology, limiter is necessary at the very front of the radar receiver to protect LNA. As a result, total noise figure of radar receiver is deteriorated. In this research, measured noise figure of LNA based on GaN HEMT is below 2 dB. In the case of commercialized GaAs LNA, recommended maximum input power is about 30 dBm. On the other hand, GaN HEMT LNA which is designed and measured is burned-out when input power is 43 dBm and robustness is guaranteed at input power 45.4 dBm.

Input Noise Immunity of Multilayer Perceptrons

  • Lee, Young-Jik;Oh, Sang-Hoon
    • ETRI Journal
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    • v.16 no.1
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    • pp.35-43
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    • 1994
  • In this paper, the robustness of the artificial neural networks to noise is demonstrated with a multilayer perceptron, and the reason of robustness is due to the statistical orthogonality among hidden nodes and its hierarchical information extraction capability. Also, the misclassification probability of a well-trained multilayer perceptron is derived without any linear approximations when the inputs are contaminated with random noises. The misclassification probability for a noisy pattern is shown to be a function of the input pattern, noise variances, the weight matrices, and the nonlinear transformations. The result is verified with a handwritten digit recognition problem, which shows better result than that using linear approximations.

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