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Journal articles on the topic 'Extraction du signal'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Pollard, Valerie, and Donald S. Prough. "Signal Extraction Technology." Anesthesia & Analgesia 83, no. 2 (1996): 213–14. http://dx.doi.org/10.1213/00000539-199608000-00002.

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2

Pollard, Valerie, and Donald S. Prough. "Signal Extraction Technology." Anesthesia & Analgesia 83, no. 2 (1996): 213–14. http://dx.doi.org/10.1097/00000539-199608000-00002.

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3

Barker, Steven J. "Signal Extraction Technology." Anesthesia & Analgesia 84, no. 4 (1997): 938. http://dx.doi.org/10.1097/00000539-199704000-00047.

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4

Barker, Steven J. "Signal Extraction Technology." Anesthesia & Analgesia 84, no. 4 (1997): 938. http://dx.doi.org/10.1213/00000539-199704000-00047.

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5

Short, Kevin M. "Signal Extraction from Chaotic Communications." International Journal of Bifurcation and Chaos 07, no. 07 (1997): 1579–97. http://dx.doi.org/10.1142/s0218127497001230.

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This paper will consider the use of nonlinear dynamic (NLD) forecasting to extract messages from chaotic communication systems. Earlier work has shown that one-step prediction methods have sometimes been able to reveal the presence of hidden messages as well as the frequency content of the hidden messages. However, recovery of the actual hidden message usually involved filtering in the frequency domain. In this paper we show that it may be possible to extract the hidden message signal without filtering in the frequency domain. The approaches which will be discussed involve either the use of mu

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6

Zhang, Shou Cheng. "An Improved Blind Source Extraction Algorithm." Advanced Materials Research 926-930 (May 2014): 2964–67. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2964.

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One-unit independent component analysis with reference (ICA-R) is an efficient method capable of extracting a desired source signal by using reference signal. In this paper, a new fast one-unit ICA-R algorithm is derived by using kurtosis contrast function based on new constrained independent component analysis (cICA) theory. The proposed algorithm has lower computational complexity and accurate extraction. Experiments with synthetic signals demonstrate the efficacy and accuracy of the proposed algorithm.

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7

Hassani, Hossein, Emmanuel Sirimal Silva, and Zara Ghodsi. "Optimizing bicoid signal extraction." Mathematical Biosciences 294 (December 2017): 46–56. http://dx.doi.org/10.1016/j.mbs.2017.09.008.

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8

Wallace, Neil. "Lucas's signal-extraction model." Journal of Monetary Economics 30, no. 3 (1992): 433–47. http://dx.doi.org/10.1016/0304-3932(92)90005-m.

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9

Zhao, Yong Jian, and Hai Ning Jiang. "Extraction of Signals with Reference." Advanced Materials Research 989-994 (July 2014): 3613–16. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3613.

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As a famous one-unit algorithm, FastICA can extract source signals one by one. In many applications, someone is only interested in a specific source signal. Through incorporating reference about the desired signal into a negentropy based contrast function, a constrained optimization problem is formed. Then an improved method is proposed which can extract the desired source signal exclusively. Computer simulations demonstrate its good performance.

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Chen, Ye Qu, Wen Zheng, and Xie Ben Wei. "Application of EMD to Integrated Signal Trend Extraction." Advanced Materials Research 591-593 (November 2012): 2072–76. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2072.

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Huang’s data-driven technique of Empirical Mode Decomposition (EMD) is presented, and issues related to its effective implementation are discussed. Integrating signal directly will produce a trend, it will cause distortion and interfere with the calculation results. This paper discusses the reasons that cause the integrated signal trend, compares the different methods for extracting trend. The traditional steps use the linear fitting and a high-pass filter to remove low frequency signal to extract trend. This paper uses Empirical Mode Decomposition (EMD) method to extract integrated signals tr

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McElroy, Tucker S., and Agustin Maravall. "Optimal Signal Extraction with Correlated Components." Journal of Time Series Econometrics 6, no. 2 (2014): 237–73. http://dx.doi.org/10.1515/jtse-2013-0016.

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AbstractWhile it is typical in the econometric signal extraction literature to assume that the unobserved signal and noise components are uncorrelated, there is nevertheless an interest among econometricians in the hypothesis of hysteresis, i.e. that major movements in the economy are fundamentally linked. While specific models involving correlated signal and noise innovation sequences have been developed and applied using state space methods, there is no systematic treatment of optimal signal extraction with correlated components. This paper provides the mean square error optimal formulas for

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12

Chai, Lin, and Jun Ru Sun. "Voltage Flicker Extraction Based on Wavelet Analysis." Applied Mechanics and Materials 385-386 (August 2013): 1389–93. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1389.

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Extracting voltage flicker from the sampling voltage signal is a precondition for management of flicker. Voltage flicker signal is a low frequency time-varying non-stationary signal. The traditional fourier transform has great limitations when analyze the non-stationary signal for not having the time resolution. As wavelet transform has good property of time-frequency localization, it become a powerful tool for analyze this kind of signal. This paper adopts multi-resolution analysis of wavelet to extract voltage flicker signal. Furthermore, according to the characteristics of wavelet function,

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Nekrutkin, Vladimir, and Irina Vasilinetc. "Asymptotic extraction of common signal subspaces from perturbed signals." Statistics and Its Interface 10, no. 1 (2017): 27–32. http://dx.doi.org/10.4310/sii.2017.v10.n1.a3.

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14

Unoki, Masashi, and Masato Akagi. "A method for signal extraction from noise-added signals." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 80, no. 11 (1997): 1–11. http://dx.doi.org/10.1002/(sici)1520-6440(199711)80:11<1::aid-ecjc1>3.0.co;2-8.

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15

Zhang, Zengmeng, Xing Cheng, Dayong Ning, Jiaoyi Hou, and Yongjun Gong. "Underwater acoustic beacon signal extraction based on dislocation superimposed method." Advances in Mechanical Engineering 9, no. 2 (2017): 168781401769167. http://dx.doi.org/10.1177/1687814017691671.

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Flight data are recorded in an acoustic beacon. A new signal extraction method led by random decrement technique is proposed to detect sound signals from thousands of meters under the sea. This method involves dislocation superimposed method and cross-correlation function to extract acoustic beacon signals with noise interference. First, the starting point is selected and the length of each segment is determined via two superposition ways. Second, the signal segment for linear superposition is intercepted to complete acoustic beacon signal extraction. Finally, the signals are subjected to cros

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16

Rosenthal, A., D. Razansky, and V. Ntziachristos. "Quantitative Optoacoustic Signal Extraction Using Sparse Signal Representation." IEEE Transactions on Medical Imaging 28, no. 12 (2009): 1997–2006. http://dx.doi.org/10.1109/tmi.2009.2027116.

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Chen, Yan Long, and Pei Lin Zhang. "Signal Extraction Based on an Improved EMD Method." Advanced Materials Research 490-495 (March 2012): 583–88. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.583.

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The main purpose of this paper is to propose an improved empirical mode decomposition (EMD) approach based on principal component analysis (PCA). EMD is a time-frequency analytical method used to deal with non-linear and non-stable signals. But it generally fails to separate IMFs from primordial signal consisting of several adjacent frequencies,especially when noise is strong.In this papar the PCA is introduced to solve this problem.First,PCA is used to pro-process sample signal to get principal components. Then several signals are reconstructed by principal components.Reconstructing signals c

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18

Wu, Hai, Li Guo Tian, Shan Hu, Zhi Liang Chen, and Meng Li. "Detection System on Weak Electrical Signal in Plants." Applied Mechanics and Materials 427-429 (September 2013): 2037–40. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2037.

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To achieve the accurate extraction and characteristics analysis of electrical signal in plants, the system was developed for detecting and extracting weak electrical signal in plants. As experimental platform for the extraction and detection of plants electric signal, training experiment system on the growth of plants, had done the experiments of extraction and feature analysis on plants electric signal by it, described in detail the extraction and analysis process of plants electrical signal, and also discussed the methods of anti-interference process for the system. The system construction i

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19

Xie, Shengkun. "Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs." Computation 9, no. 7 (2021): 78. http://dx.doi.org/10.3390/computation9070078.

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Feature extraction plays an important role in machine learning for signal processing, particularly for low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, and non-stationary. Extracting key features of this type of data is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet power spectra and functional principal component analysis. We focus on how the feature extraction method can help improve the separation of signals in a low-dimensional feature subspace. By

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20

Aijun, Hu, Lin Jianfeng, Sun Shangfei, and Xiang Ling. "A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing." Shock and Vibration 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9375491.

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The fault signals of rolling element bearing are often characterized by the presence of periodic impulses, which are modulated high-frequency harmonic components. The features of early fault in rolling bearing are very weak, which are often masked by background noise. The impulsiveness of the vibration signal has affected the identification of characteristic frequency for the early fault detection of the bearing. In this paper, a novel approach based on morphological operators is presented for impulsive signal extraction of early fault in rolling element bearing. The combination Top-Hat (CTH)

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21

Putra, Darma Setiawan, and Yuril Umbu WW. "Feature Extraction of Facial Electromyograph (EMG) Signal for Aceh Languages Speech using Discrete Wavelet Transform (DWT)." Jurnal Inotera 4, no. 1 (2019): 31. http://dx.doi.org/10.31572/inotera.vol4.iss1.2019.id73.

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The facial electromyograph (FEMG) signal is a signal that occurs in the muscles of the contracted human face. This FEMG signal is one of the techniques used to study human speech recognition. It can be acquired by placing an electrode surface on the skin around the facial articulation muscle. Three types of muscles in this study are the masseter, risorius and depressor muscle. This study aims to extract and analyze the features in the FEMG signal. The extraction method is the discrete wavelet transform (DWT). The type of wavelet transform is Daubechies2 with level 5. After extraction and analy

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22

Kitagawa, Genshiro, Tetsuo Takanami, and Norio Matsumoto. "Signal Extraction Problems in Seismology." International Statistical Review / Revue Internationale de Statistique 69, no. 1 (2001): 129. http://dx.doi.org/10.2307/1403533.

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23

Chandra, Shanti, Ambalika Sharma, and Girish Kumar Singh. "Feature extraction of ECG signal." Journal of Medical Engineering & Technology 42, no. 4 (2018): 306–16. http://dx.doi.org/10.1080/03091902.2018.1492039.

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24

Maravall, Agustin. "Revisions in ARIMA Signal Extraction." Journal of the American Statistical Association 81, no. 395 (1986): 736–40. http://dx.doi.org/10.1080/01621459.1986.10478330.

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25

LUO, YULEI, and ERIC R. YOUNG. "SIGNAL EXTRACTION AND RATIONAL INATTENTION." Economic Inquiry 52, no. 2 (2014): 811–29. http://dx.doi.org/10.1111/ecin.12073.

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26

Kitagawa, Genshiro, Tetsuo Takanami, and Norio Matsumoto. "Signal Extraction Problems in Seismology." International Statistical Review 69, no. 1 (2001): 129–52. http://dx.doi.org/10.1111/j.1751-5823.2001.tb00483.x.

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27

Thury, Gerhard. "Seasonal adjustment by signal extraction." Empirica 12, no. 2 (1985): 191–207. http://dx.doi.org/10.1007/bf00924927.

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28

Pollock, D. S. G. "Econometric methods of signal extraction." Computational Statistics & Data Analysis 50, no. 9 (2006): 2268–92. http://dx.doi.org/10.1016/j.csda.2005.07.010.

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29

Song, Yongxing, Jingting Liu, Linhua Zhang, and Dazhuan Wu. "Improvement of Fast Kurtogram Combined with PCA for Multiple Weak Fault Features Extraction." Processes 8, no. 9 (2020): 1059. http://dx.doi.org/10.3390/pr8091059.

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Demodulation plays an important role in fault feature extraction for rotating machinery. The fast kurtogram method was proved to be effective for rotating machinery demodulation. However, the demodulation effectiveness of fast kurtogram was poor for multiple fault features extraction under low signal-to-noise ratio. In this paper, an improved method of fast kurtogram, called P-kurtogram, is presented. The proposed method extracted the multiple weak fault features from multiple envelope signals-based principal component analysis. Compared with extracting features from one envelope signal of fas

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30

Fengzhen, Zhang, Li Guijuan, Zhang Zhaohui, and Hu Chen. "Doppler shift extraction of wideband signal using spectrum scaling matching." MATEC Web of Conferences 208 (2018): 01001. http://dx.doi.org/10.1051/matecconf/201820801001.

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Doppler shift is an important feature of moving targets. It can be used to extract target velocity, distance, track and other movement parameters. According to the problem of extracting Doppler shift for wideband signals with unstable line spectrum or no line spectrum, we proposed a Doppler shift extraction method for wideband signals based on spectral scaling matching. Firstly, a spectrum reference matrix corresponding to different relative Doppler shift is generated. Then, the matching degree of Doppler signal spectrum and reference matrix is measured by linear correlation coefficient. Final

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31

Zhao, Yong Jian. "Research on Blind Signal Extraction Based on Normalized Kurtosis." Advanced Materials Research 989-994 (July 2014): 3609–12. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3609.

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Blind source extraction (BSE) is a promising technique to solve signal mixture problems while only one or a few source signals are desired. In biomedical applications, one often knows certain prior information about a desired source signal in advance. In this paper, we explore specific prior information as a constrained condition so as to develop a flexible BSE algorithm. One can extract a desired source signal while its normalized kurtosis range is known in advance. Computer simulations on biomedical signals confirm the validity of the proposed algorithm.

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32

Yu, Wei, Qiang Han, Jing Jing Ma, and Pei Xie. "A New Method for Biomedical Signal Processing with EMD and ICA Approach." Advanced Materials Research 546-547 (July 2012): 548–52. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.548.

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Faint signal extraction is always a difficult issue in biomedical signal processing field, because the desired signal is often submerged in several relatively large signals or noises. A novel faint signal processing method based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is developed to enhance the sensitivity and reliability of faint signal detection. This novel method includes two major steps, which is, firstly the decomposition of the biomedical composite signal using EMD, then the classification or extraction of the desired faint signal component through

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33

Shayeb, Ismail, Naseem Asad, Ziad Alqadi, and Qazem Jaber. "Evaluation of speech signal features extraction methods." Journal of Applied Science, Engineering, Technology, and Education 2, no. 1 (2020): 69–78. http://dx.doi.org/10.35877/454ri.asci2151.

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Human speech digital signals are famous and important digital types, they are used in many vital applications which require a high speed processing, so creating a speech signal features is a needed issue. In this research paper we will study more widely used methods of features extraction, we will implement them, and the obtained experimental results will be compared, efficiency parameters such as extraction time and throughput will be obtained and a speedup of each method will be calculated. Speech signal histogram will be used to improve some methods efficiency.

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34

Lv, Miao Rong, Bao Jian Wei, Jian Lu, and Jian Bo Diao. "The Method Based on Pattern Filter for Sonnd & Vibration Signal." Applied Mechanics and Materials 333-335 (July 2013): 526–30. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.526.

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The difficulty of the signal processing is not the acquisition of the signals, but how to get the reasonble interpretations from the signals. Since the 1960s, Wavelet Transform, Fast Fourier method and other theoryies have done some works by some innovative processing methods to achieve a breakthrough. But for their limitions, these methods can not achieve a complete separation if the there are two or more signals in one time domain or frequency domain. In this article, a new engineering signal processing method-pattern filter method has is introduced, by which the signal extraction, sepration

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35

Dong, Yu Hua, and Jun Xing Zhang. "Trend Extraction in Vibration Signal Based on EMD." Advanced Materials Research 459 (January 2012): 377–80. http://dx.doi.org/10.4028/www.scientific.net/amr.459.377.

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This paper proposed a de-trend method for vibration signal of telemetry based on the empirical mode decomposition (EMD) by correlation coefficient matrix. The signal is decomposed to a series of intrinsic mode component and the remainder item by EMD. It mainly distinguishes between the remainder item and the signal trend, according to the correlation coefficient matrix to determine whether some intrinsic mode component belongs to the trend item or not. The results show that signal trends can be extracted accurately through the effective combination EMD with correlation coefficient matrix and t

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36

Fan, Wei, Z. K. Zhu, Wei Guo Huang, and Gai Gai Cai. "Sparse Representation De-Noising Based on Morlet Wavelet Basis and its Application for Transient Feature Extraction." Applied Mechanics and Materials 526 (February 2014): 200–204. http://dx.doi.org/10.4028/www.scientific.net/amm.526.200.

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Signals with multiple transients are often encountered with much noise in engineering. The transient feature extraction has always been the key issue for signal analysis. A new signal de-noising method combining sparse representation and Morlet wavelet basis is proposed for signal de-noising and feature extraction. Simulation study concerning multiple transients signal shows the effectiveness of this method in transient feature extraction. The efficiency of this de-noising method is also verified by its application to extract fault signature for gearbox.

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37

Cheng, Qian, Kexian Gong, Min Zhang, and Xiaoyan Liu. "An efficient wide-band signal detection and extraction method." MATEC Web of Conferences 336 (2021): 04011. http://dx.doi.org/10.1051/matecconf/202133604011.

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Aiming at the influence of time-varying and frequency-varying of noise on the signal detection performance in the short wave wide-band channel and the large amount of computation in the channelized receiver model of the traditional low pass filter bank, a cross-channel reconfigurable multi-phase high-efficiency channelization method based on morphological processing is proposed in this paper .Firstly, The wide-band signal is coarsely filtered by the multi-phase structure of the uniform filter bank which is determined by the protection interval between signals, and then the bandwidth and positi

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38

Xu, Yong Gang, Zhi Cong Xie, Lin Li Cui, and Jing Wang. "The Feature Extraction Method of Gear Magnetic Memory Signal." Advanced Materials Research 819 (September 2013): 206–11. http://dx.doi.org/10.4028/www.scientific.net/amr.819.206.

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Magnetic memory test technology is a new nondestructive testing technique, which is able to detect of the stress concentration area and potential fault of low speed and heavy load gear. Because the magnetic memory signals are easy to be disturbed by various sources of noises, a new method based on the intrinsic time-scale decomposition (ITD) is proposed to achieve the extraction of magnetic memory signal. Firstly, the magnetic memory signals are decomposed into several proper rotation components (PRC) and a trend component by ITD. Then reconstruct the first four order PRCs to eliminate the low

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39

Zhang, Guo Hua, Zhong Fan Yuan, and Shi Xuan Liu. "Algorithm for Feature Extraction of Heart Sound Signal Based on Sym7 Wavelet." Advanced Engineering Forum 1 (September 2011): 252–56. http://dx.doi.org/10.4028/www.scientific.net/aef.1.252.

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In order to extract pathological features of heart sound signal accurately, an algorithm for extracting the sub-band energy is developed based on the wavelet packet analysis. Through the spectrum analysis of heart sound signal, the sym7 wavelet, with high energy concentration and good time localization, is taken as the mother function, and the best wavelet packet basis of heart sound signal is picked out. Then, various heart sound signals are decomposed into four levels and the wavelet packet coefficients of the best basis are obtained. According to the equal-value relation between wavelet pac

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40

Yin, Zhao, Jing Jin, Yan Wang, and Yi Shen. "Doppler Signal Denoising Based on Feature Adaptive Wavelet Shrinkage Method." Advanced Materials Research 532-533 (June 2012): 702–7. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.702.

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The envelope extraction of Doppler signal spectrum is very important in ultrasonic blood flow detection, due to the fact that it can provide the diagnosis information of blood circulatory system. Doppler signals are often polluted by noises, which will affect the performance of the envelope extraction. Therefore, it is necessary to remove the noises before extracting the spectrum envelope. In this paper, a Doppler denoising method based on the Feature Adaptive Wavelet Shrinkage is proposed. The advantage of this method is that the threshold of each coefficient is set by using the coefficient a

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41

An, Guoping, Qingbin Tong, Yanan Zhang, et al. "An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing." Energies 14, no. 4 (2021): 1079. http://dx.doi.org/10.3390/en14041079.

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The fault diagnosis of rolling element bearing is of great significance to avoid serious accidents and huge economic losses. However, the characteristics of the nonlinear, non-stationary vibration signals make the fault feature extraction of signal become a challenging work. This paper proposes an improved variational mode decomposition (IVMD) algorithm for the fault feature extraction of rolling bearing, which has the advantages of extracting the optimal fault feature from the decomposed mode and overcoming the noise interference. The Shuffled Frog Leap Algorithm (SFLA) is employed in the opt

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42

Liu, Yunjiang, Fuzhong Wang, Lu Liu, and Yamin Zhu. "Secondary signal-induced large-parameter stochastic resonance for feature extraction of mechanical faults." International Journal of Modern Physics B 33, no. 15 (2019): 1950157. http://dx.doi.org/10.1142/s0217979219501571.

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Aiming to solve the problem that it is difficult to extract large parameter signals from a strong noise background, a novel method of large parameter stochastic resonance (SR) induced by a secondary signal is proposed. The SR mechanism of high-frequency signals is expounded by analyzing the density distribution curve. High-frequency signals are converted to low-frequency signals using the scale transformation method, and then large-parameter SR is induced by the secondary signal. Ultimately, the method is applied to the feature extraction of mechanical faults. Simulation and experimental resul

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43

Kumar, R. Suresh, and P. Manimegalai. "Implementation of Neural Network with ALE for the Removal of Artifacts in EEG Signals." Current Signal Transduction Therapy 15, no. 1 (2020): 77–83. http://dx.doi.org/10.2174/1574362414666190613142424.

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Objective: The EEG signal extraction offers an opportunity to improve the quality of life in patients, which has lost to control the ability of their body, with impairment of locomotion. Electroencephalogram (EEG) signal is an important information source for underlying brain processes. Materials and Methods: The signal extraction and denoising technique obtained through timedomain was then processed by Adaptive Line Enhancer (ALE) to extract the signal coefficient and classify the EEG signals based on FF network. The adaptive line enhancer is used to update the coefficient during the runtime

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44

Yang, Xian Zhao, and Geng Guo Cheng. "Extraction of Machine Fault Signal Based on Discrete Wavelet Transform." Advanced Materials Research 338 (September 2011): 388–91. http://dx.doi.org/10.4028/www.scientific.net/amr.338.388.

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The impact signal is contained in the fault signals of some pivotal components such as bearings and gears. Extracting weensy impact information is an important method to diagnose equipment. With the combination of discrete wavelet transform and spectrum analysis, extracting the nonstationary signals from vibrating signal in machine is a practically effective means of diagnosing the fault signals and their types so as to anticipate machine faults. It is very effective to extract impact information in application by utlizing the discrete wavelet transform.

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45

Song, Dingyu. "Feature Extraction Method of Transmission Signal in Electronic Communication Network Based on Symmetric Algorithm." Symmetry 11, no. 3 (2019): 410. http://dx.doi.org/10.3390/sym11030410.

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Because the existing methods extract the signal characteristics of electronic communication networks, there is a problem of poor extraction. In this paper, a feature extraction method based on symmetric algorithm for transmission signals in electronic communication networks is proposed. The transmission signal in the time domain is decomposed by three-layer wavelet packet decomposition through threshold denoising and data dimension reduction. The adaptive floating threshold is used as a threshold to quantify the wavelet coefficients of the signal, which can effectively remove noise while retai

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46

Bobkova, Elena Olegovna, Nataliya Garrievna Kostukevich, and Дмитрий Николаевич Ведерников. "RESONANT ACOUSTIC EFFECT ON EXTRACTION OF BIRCH INNER BARK WITH ALKALI SOLUTION." chemistry of plant raw material, no. 3 (February 8, 2019): 285–90. http://dx.doi.org/10.14258/jcprm.2019034391.

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The article describes the option of intensification extraction process from the birch inner bark. The influence of acoustic action (tensile-pulse modulation) on an increase in the yield of extractive substances on an aqueous solution of alkali (1% NaOH) is discussed. The extraction results were evaluated by the solid residue, the optical density of the solution, and the content of tannins. The yield of extractive substances is increased in 1.5 time with the help of a certain generator signal (meander) with a certain frequency (in the range of 170–190 kHz) and amplitude (2.7–2.9 V). The acousti

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47

Zhang, Rongrui, and Heng Zhao. "A Novel Method for Online Extraction of Small-Angle Scattering Pulse Signals from Particles Based on Variable Forgetting Factor RLS Algorithm." Sensors 21, no. 17 (2021): 5759. http://dx.doi.org/10.3390/s21175759.

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The small-angle optical particle counter (OPC) can detect particles with strong light absorption. At the same time, it can ignore the properties of the detected particles and detect the particle size singly and more accurately. Reasonably improving the resolution of the low pulse signal of fine particles is key to improving the detection accuracy of the small-angle OPC. In this paper, a new adaptive filtering method for the small-angle scattering signals of particles is proposed based on the recursive least squares (RLS) algorithm. By analyzing the characteristics of the small-angle scattering

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Wang, Yi, Dan Liu, Guanghua Xu, and Kuosheng Jiang. "An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 11 (2015): 1830–45. http://dx.doi.org/10.1177/0954406215585186.

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The fast kurtogram, a faint signal extraction method, has been regarded as an effective approach to detect and characterize faint transient features in vibration signals. However, the fast kurtogram, a band-pass filtering method, which extracts transient signals by optimal frequency band selection and leaves the noise in the selected frequency band unprocessed. Therefore, to overcome the shortcoming of the fast kurtogram method, a method which can wipe off the noise in the whole frequency band is necessary. This paper proposes a novel faint signal extraction method by time–frequency distributi

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49

Gu, Xiaojiao, and Changzheng Chen. "Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD." International Journal of Rotating Machinery 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/3595871.

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Aiming at the difficulty of early fault vibration signal extraction of rolling bearing, a method of fault weak signal extraction based on variational mode decomposition (VMD) and quantum particle swarm optimization adaptive stochastic resonance (QPSO-SR) for denoising is proposed. Firstly, stochastic resonance parameters are optimized adaptively by using quantum particle swarm optimization algorithm according to the characteristics of the original fault vibration signal. The best stochastic resonance system parameters are output when the signal to noise ratio reaches the maximum value. Secondl

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50

LIU, YUDONG, and LUIZ C. BARBOSA. "EXTRACTION OF PERIODIC ORBITS FROM A CHAOTIC ATTRACTOR." Modern Physics Letters B 09, no. 06 (1995): 351–58. http://dx.doi.org/10.1142/s0217984995000334.

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Control of chaos present in the Lorenz system was realized numerically by using an unstable periodic oscillation produced from an identical system as a perturbation or driving signal to one of the control parameters. Two identical Lorenz systems were used as the signal system and the target system. Several unstable periodic signals from the signal system were used to control the chaotic attractor of the target system. As a result of the control of chaos, many periodic orbits were probed and extracted out from the chaotic attractor.

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