Relevant bibliographies by topics / Non-stationary signal classification

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  1. Journal articles

Academic literature on the topic 'Non-stationary signal classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Non-stationary signal classification"

1

Ratnayake, TA, DBW Nettasinghe, GMRI Godaliyadda, MPB Ekanayake, and JV Wijayakulasooriya. "An information rich subspace separation for non-stationary signal classification." Journal of the National Science Foundation of Sri Lanka 44, no. 3 (2016): 257. http://dx.doi.org/10.4038/jnsfsr.v44i3.8008.

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Świercz, Ewa. "Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution." International Journal of Applied Mathematics and Computer Science 20, no. 1 (2010): 135–47. http://dx.doi.org/10.2478/v10006-010-0010-x.

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Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distributionA new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requiresa prioriknowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to themaxdiscriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.
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Lowne, D. R., S. J. Roberts, and R. Garnett. "Sequential non-stationary dynamic classification with sparse feedback." Pattern Recognition 43, no. 3 (2010): 897–905. http://dx.doi.org/10.1016/j.patcog.2009.09.004.

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Shin, Younghak, Seungchan Lee, Minkyu Ahn, Hohyun Cho, Sung Chan Jun, and Heung-No Lee. "Noise robustness analysis of sparse representation based classification method for non-stationary EEG signal classification." Biomedical Signal Processing and Control 21 (August 2015): 8–18. http://dx.doi.org/10.1016/j.bspc.2015.05.007.

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TACER, BERKANT, and PATRICK J. LOUGHLIN. "NON-STATIONARY SIGNAL CLASSIFICATION USING THE JOINT MOMENTS OF TIME-FREQUENCY DISTRIBUTIONS." Pattern Recognition 31, no. 11 (1998): 1635–41. http://dx.doi.org/10.1016/s0031-3203(98)00031-4.

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6

Ge, Mingtao, Jie Wang, Yicun Xu, Fangfang Zhang, Ke Bai, and Xiangyang Ren. "Rolling Bearing Fault Diagnosis Based on EWT Sub-Modal Hypothesis Test and Ambiguity Correlation Classification." Symmetry 10, no. 12 (2018): 730. http://dx.doi.org/10.3390/sym10120730.

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Because of the cyclic symmetric structure of rolling bearings, its vibration signals are regular when the rolling bearing is working in a normal state. But when the rolling bearing fails, whether the outer race fault or the inner race fault, the symmetry of the rolling bearing is broken and the fault destroys the rolling bearing’s stable working state. Whenever the bearing passes through the fault point, it will send out vibration signals representing the fault characteristics. These signals are often non-linear, non-stationary, and full of Gaussian noise which are quite different from normal signals. According to this, the sub-modal obtained by empirical wavelet transform (EWT), secondary decomposition is tested by the Gaussian distribution hypothesis test. It is regarded that sub-modal following Gaussian distribution is Gaussian noise which is filtered during signal reconstruction. Then by taking advantage of the ambiguity function superiority in non-stationary signal processing and combining correlation coefficient, an ambiguity correlation classifier is constructed. After training, the classifier can recognize vibration signals of rolling bearings under different working conditions, so that the purpose of identifying rolling bearing faults can be achieved. Finally, the method effect was verified by experiments.
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7

Dong, Zeng Shou, Zhao Jing Ren, and You Dong. "Research of Mechanical Vibration Signal Classification Based on LMD." Applied Mechanics and Materials 764-765 (May 2015): 350–58. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.350.

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The traditional signal processing methods are difficult to accurately extract fault information, because mechanical fault vibration signals have non-stationary, which will cause system instability. Local mean decomposition is adaptive signal processing method. However, in the local mean decomposition of the signal, the trend of the endpoint can not be predicted which cause contaminating the entire signal sequence, the original moving average of the signal used over-smoothing treatment, resulting in fault characteristics can not accurately extract. The article introduces waveform matching to solve the original features of signals at the endpoints, using linear interpolation to get local mean and envelope function, then obtain production function PF vector through making use of the local mean decomposition. The energy entropy of PF vector take as identification input vectors. These vectors are respectively inputted BP neural networks, support vector machines, least squares support vector machines to identify faults. Experimental result show that the accuracy of least squares support vector machine with higher classification accuracy has been improved.
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8

Biswal, B., P. K. Dash, and S. Mishra. "Non-stationary power signal classification using local linear radial basis function neural networks." International Journal of Knowledge-based and Intelligent Engineering Systems 13, no. 2 (2009): 79–90. http://dx.doi.org/10.3233/jad-2009-0176.

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9

Jiang, Lingli, Yilun Liu, Xuejun Li, and Anhua Chen. "Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis." Shock and Vibration 18, no. 1-2 (2011): 127–37. http://dx.doi.org/10.1155/2011/703210.

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This paper proposes a new approach combining autoregressive (AR) model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.
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10

Chatterjee, Shre Kumar, Saptarshi Das, Koushik Maharatna, et al. "Exploring strategies for classification of external stimuli using statistical features of the plant electrical response." Journal of The Royal Society Interface 12, no. 104 (2015): 20141225. http://dx.doi.org/10.1098/rsif.2014.1225.

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Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli—sodium chloride (NaCl), sulfuric acid (H 2 SO 4 ) and ozone (O 3 ). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.
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