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Статті в журналах з теми "Wavelet entropy"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Lweesy, K., N. Khasawneh, M. Fraiwan, H. Wenz, H. Dickhaus, and L. Fraiwan. "Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA." Methods of Information in Medicine 49, no. 03 (2010): 230–37. http://dx.doi.org/10.3414/me09-01-0054.

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Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves

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2

Ahmadi, H., G. Dumont, F. Sassani, and R. Tafreshi. "Performance of Informative Wavelets for Classification and Diagnosis of Machine Faults." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 03 (2003): 275–89. http://dx.doi.org/10.1142/s0219691303000189.

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This paper deals with an application of wavelets for feature extraction and classification of machine faults in a real-world machine data analysis environment. We have utilized informative wavelet algorithm to generate wavelets and subsequent coefficients that are used as feature variables for classification and diagnosis of machine faults. Informative wavelets are classes of functions generated from a given analyzing wavelet in a wavelet packet decomposition structure in which for the selection of best wavelets, concepts from information theory, i.e. mutual information and entropy are utilize

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3

Raghu, S., N. Sriraam, and G. Pradeep Kumar. "Effect of Wavelet Packet Log Energy Entropy on Electroencephalogram (EEG) Signals." International Journal of Biomedical and Clinical Engineering 4, no. 1 (2015): 32–43. http://dx.doi.org/10.4018/ijbce.2015010103.

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The scaling behavior of human electroencephalogram (EEG) signals is well exploited by appropriate extraction of time – frequency domain and entropy based features. Such measurable inherently helps understanding the neurophysiological phenomenon of brain as well as its associated cortical activities. Being a non-linear time series, EEG's are assumed to be fragment of fluctuations. Several attempts have been made to study the EEG signals for clinical applications such as epileptic seizure detection, evoked response potential recognition, tumor detection, identification of alcoholics and so on. I

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4

Li, Su Ping, and Yao Ling Fan. "Investigation of Sensor Fault Diagnosis in Air Handling Units Based on Wavelet Energy Entropy." Advanced Materials Research 645 (January 2013): 316–19. http://dx.doi.org/10.4028/www.scientific.net/amr.645.316.

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This paper presents a novel fault diagnosis method for sensors in air-handling units based on wavelet energy entropy. Instead of directly comparing the numerous data under noise conditions, the wavelet energy entropy deviation is used for the fault detection and diagnosis. The actual Three-level wavelet analysis is used to decompose the measurement data captured from sensors first and then the concept of Shannon entropy is referred to define the wavelet energy entropy. Once the wavelet energy entropy is obtained, whether the sensors are faulty can be confirmed through comparing the deviation o

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5

Sun, Zeng Shou, Ke Ju Fan, Xu Guang Yin, and Peng Jie Han. "The Research of Civil Structural Damage Identification Based on Lifting Wavelet Entropy Index." Advanced Materials Research 291-294 (July 2011): 2041–48. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.2041.

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The failure of civil engineering structure will lead to heavy losses. So, identifying structural damage is necessary as early as possible. The excellent localization performance of lifting wavelet transform will facilitate significantly damage diagnosis. On the base of wavelet energy distribution of structural acceleration response, taking advantage of characteristics of lifting wavelet and entropy, the structural damage identification method based on lifting wavelet entropy is proposed in this paper. And the lifting wavelet time entropy index and the relative lifting wavelet entropy index are

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6

Pacheco, Julio César Ramírez, Joel Antonio Trejo-Sánchez, and Luis Rizo-Domínguez. "The shifted wavelet $ (q, q') $-entropy and the classification of stationary fractal signals." Networks and Heterogeneous Media 20, no. 1 (2025): 89–103. https://doi.org/10.3934/nhm.2025006.

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<p>In this article, a wavelet entropy, which behaves as a shifted version of the standard wavelet $ (q, q') $-entropy of fractal signals, is presented. The shifted wavelet $ (q, q') $-entropy is obtained by computing the standard $ (q, q') $-entropy functional on a weighted relative-wavelet-energy (RWE) representation of fractal signals; it is shown that the weight within the RWE plays the role of a shifting factor in the characteristics of the standard wavelet $ (q, q') $-entropy. Therefore the shifted wavelet $ (q, q') $-entropy relocates the wavelet entropy values to any point of the

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7

Jallouli, Malika, Sabrine Arfaoui, Anouar Ben Mabrouk, and Carlo Cattani. "Clifford Wavelet Entropy for Fetal ECG Extraction." Entropy 23, no. 7 (2021): 844. http://dx.doi.org/10.3390/e23070844.

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Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. In the present paper, a wavelet/multiwavelet method is proposed to perfectly extract the fetal ECG parameters from the abdominal mother ECG. In a first step

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8

Noori Hoshyar, Azadeh, Bijan Samali, Ranjith Liyanapathirana, and Saber Taghavipour. "Analysis of failure in concrete and reinforced-concrete beams for the smart aggregate–based monitoring system." Structural Health Monitoring 19, no. 2 (2019): 463–80. http://dx.doi.org/10.1177/1475921719854151.

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Monitoring of structures and defining the severity of damages that occur under loading are essential in practical applications of civil infrastructure. In this article, we analyze failure using a smart aggregate sensor–based approach. The signals captured by smart aggregate sensors mounted on the structure under loading are de-noised using wavelet de-noising technique to prevent misdirection of the event interpretation of what is happening in the material. The performance of different mother wavelets on the de-noising process was investigated and analyzed. The objective is to identify the opti

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9

Yang, Yan Mei, Ze Gen Wang, Yu Yun Gao, and Fa Peng Gao. "Deformation Monitoring Data De-Noising Processing Based on Wavelet Packet." Applied Mechanics and Materials 166-169 (May 2012): 1180–86. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1180.

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Wavelet packet coefficients carrying real signals have large amplitude but are in minority, while those carrying noise has lower amplitude but is of large number. In this case, the Basic principle of de-noising wavelet packet is to process signals carrying noise. A suitable threshold is chosen in different decomposition level. Wavelet packet coefficient of less than this threshold is set to equal zero, while wavelet packet coefficients of greater than this threshold is reserved and reconstructed into de-noising signals. MSE, SNR, PSNR are regarded as the standards of de-noising evaluation, som

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10

Zhang, Mingming, Pan Kong, Anping Hou, Aiguo Xia, Wei Tuo, and Yongzhao Lv. "Identification Strategy Design with the Solution of Wavelet Singular Spectral Entropy Algorithm for the Aerodynamic System Instability." Aerospace 9, no. 6 (2022): 320. http://dx.doi.org/10.3390/aerospace9060320.

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In order to effectively identify the signs of instability in the aerodynamic system of an axial compressor, a wavelet singular spectral entropy algorithm incorporated within the wavelet transform, singular value decomposition and information entropy is proposed to describe the distribution complexity of the spatial modalities in the flow field. This kind of identification design can accurately distinguish the boundary between the stable and unstable states of the internal flow field from the view of a dynamic system. On the basis of the information entropy algorithm, the wavelet singular spect

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11

Wang, Ying Qiang, Ji Ping Xu, Yan Shi, Xiao Yi Wang, Jia Bin Yu, and Yang Liu. "Research on the Application of Wavelet Entropy Theory in Detecting Metal Magnetic Memory." Applied Mechanics and Materials 401-403 (September 2013): 1212–17. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1212.

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A recognition method based on metal magnetic memory and wavelet entropy theory is proposed to detect cracks in metal workpiece. First, wavelet decomposition is performed to magnetic induction signal, and wavelet entropy is introduced based on soft threshold in wavelet transform to reflect the distribution feature of signal energy. Then the threshold of high-frequency component is determined self-adaptively due to different wavelet entropies of the signal at different decomposition scales, thus extracting useful information effectively and determining the position of the defect. Experiment resu

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12

YAN, RUQIANG, and ROBERT X. GAO. "BASE WAVELET SELECTION FOR BEARING VIBRATION SIGNAL ANALYSIS." International Journal of Wavelets, Multiresolution and Information Processing 07, no. 04 (2009): 411–26. http://dx.doi.org/10.1142/s0219691309002994.

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A critical issue to ensuring the effectiveness of wavelet transform in machine condition monitoring and health diagnosis is the choice of the most suited base wavelet for signal decomposition and feature extraction. This paper addresses this issue by introducing a quantitative measure to select an appropriate base wavelet for analyzing vibration signals measured on rotary mechanical systems. Specifically, the measure based on energy-to-Shannon entropy ratio has been investigated. Both the simulated Gaussian-modulated sinusoidal signal and an actual ball bearing vibration signal have been used

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13

Yan, Jian Guo, Dong Li Yuan, Si Yuan Li, and Xiao Jun Xing. "Study on Sensor Signal Filtering Based on Wavelet Energy Entropy." Applied Mechanics and Materials 63-64 (June 2011): 573–78. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.573.

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In order to increase the fuel level measurement accuracy in aircraft fuel system, the method of sensor signal filtering based on the wavelet energy entropy was put forward. Using the maximum entropy principle the wavelet energy entropy of high-frequency coefficient vector in each level was calculated while the output signal of sensor was analyzed in wavelet multi-resolution mode. Once the sum of wavelet energy entropy for filtered signal and noise signal is maximum, the filtering effect is much better. At the same time, the result of tests which use simulation signal and fuel level sensor data

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14

Sun, Hanqing, Xiaohui Zhang, Zhou Yu, and Gang Xi. "Feature Recognition of Crop Growth Information in Precision Farming." Complexity 2018 (October 15, 2018): 1–10. http://dx.doi.org/10.1155/2018/9250832.

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To identify plant electrical signals effectively, a new feature extraction method based on multiwavelet entropy and principal component analysis is proposed. The wavelet energy entropy, wavelet singular entropy, and the wavelet variance entropy of plants’ electrical signals are extracted by a wavelet transformation to construct the combined features. Principal component analysis (PCA) is applied to treat the constructed features and eliminate redundant information among those features and extract features which can reflect signal type. Finally, the classification method of BP neural network is

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15

ČASTOVÁ, NINA, DAVID HORÁK, and ZDENĚK KALÁB. "DESCRIPTION OF SEISMIC EVENTS USING WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 04, no. 03 (2006): 405–14. http://dx.doi.org/10.1142/s0219691306001336.

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This paper deals with engineering application of wavelet transform for processing of real seismological signals. Methodology for processing of these slight signals using wavelet transform is presented in this paper. Briefly, three basic aims are connected with this procedure:. 1. Selection of optimal wavelet and optimal wavelet basis B opt for selected data set based on minimal entropy: B opt = arg min B E(X,B). The best results were reached by symmetric complex wavelets with scaling coefficients SCD-6. 2. Wavelet packet decomposition and filtration of data using universal criterion of thresho

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16

Rizal, Achmad, and Attika Puspitasari. "Lung sound classification using wavelet transform and entropy to detect lung abnormality." Serbian Journal of Electrical Engineering 19, no. 1 (2022): 79–98. http://dx.doi.org/10.2298/sjee2201079r.

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Lung sounds provide essential information about the health of the lungs and respiratory tract. They have unique and distinguishable patterns associated with the abnormalities in these organs. Many studies attempted to develop various methods to classify lung sounds automatically. Wavelet transform is one of the approaches widely utilized for physiological signal analysis. Commonly, wavelet in feature extraction is used to break down the lung sounds into several sub-bands before calculating some parameters. This study used five lung sound classes obtained from various sources. Furthermore, the

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17

Roushangar, Kiyoumars, Vahid Nourani, and Farhad Alizadeh. "A multiscale time-space approach to analyze and categorize the precipitation fluctuation based on the wavelet transform and information theory concept." Hydrology Research 49, no. 3 (2018): 724–43. http://dx.doi.org/10.2166/nh.2018.143.

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AbstractThe present study proposed a time-space framework using discrete wavelet transform-based multiscale entropy (DWE) approach to analyze and spatially categorize the precipitation variation in Iran. To this end, historical monthly precipitation time series during 1960–2010 from 31 rain gauges were used in this study. First, wavelet-based de-noising approach was applied to diminish the effect of noise in precipitation time series which may affect the entropy values. Next, Daubechies (db) mother wavelets (db5–db10) were used to decompose the precipitation time series. Subsequently, entropy

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18

Zhou, Yujuan, Lei Wang, Jintai Jia, and Gema Monasterio. "Application of Back Propagation Neural Network and Information Entropy in Deep Detection of Anesthesia." Journal of Medical Imaging and Health Informatics 10, no. 8 (2020): 1875–79. http://dx.doi.org/10.1166/jmihi.2020.3103.

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In order to study the monitoring of anesthesia depth during general anesthesia, the EEG (electroencephalogram) signals of 30 patients with laparoscopic general anesthesia were taken as the research objects. The approximate entropy, sample entropy, ranking entropy, and wavelet entropy of EEG signals under different anesthesia conditions were compared by BP (Back Propagation) neural network. The results showed that with the deepening of anesthesia, the four kinds of information entropies of EEG signal showed a downward trend. Among them, the sample entropy algorithm, ranking entropy algorithm, a

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19

Li, Dan, Kevin Sze Chiang Kuang, and Chan Ghee Koh. "Rail crack monitoring based on Tsallis synchrosqueezed wavelet entropy of acoustic emission signals: A field study." Structural Health Monitoring 17, no. 6 (2017): 1410–24. http://dx.doi.org/10.1177/1475921717742339.

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This article focuses on the rail crack monitoring using acoustic emission technique in the field typically with complex cracking conditions and high operational noise. A novel crack monitoring strategy based on Tsallis synchrosqueezed wavelet entropy was developed, where synchrosqueezed wavelet transform was introduced to explore the time–frequency characteristics of acoustic emission signals and Tsallis entropy was adopted to quantify the local variation of acoustic emission wavelet coefficients more accurately. The mother wavelet of synchrosqueezed wavelet transform and three key parameters

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20

Meng, Rui, Junpeng Zhang, Ming Chen, and Liangliang Chen. "Fault Diagnosis Method of Planetary Gearboxes Based on Multi-Scale Wavelet Packet Energy Entropy and Extreme Learning Machine." Entropy 27, no. 8 (2025): 782. https://doi.org/10.3390/e27080782.

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As critical components of planetary gearboxes, gears directly affect mechanical system reliability when faults occur. Traditional feature extraction methods exhibit limitations in accurately identifying fault characteristics and achieving satisfactory diagnostic accuracy. This research is concerned with the gear of the planetary gearbox and proposes a new approach termed multi-scale wavelet packet energy entropy (MSWPEE) for extracting gear fault features. The signal is split into sub-signals at three different scale factors. Following decomposition and reconstruction using the wavelet packet

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21

Li, Xin, Xue Jun Li, and Guang Bin Wang. "De-Noising Method of Acoustic Emission Signal for Rolling Bearing Based on Adaptive Wavelet Correlation Analysis." Applied Mechanics and Materials 273 (January 2013): 188–92. http://dx.doi.org/10.4028/www.scientific.net/amm.273.188.

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In acoustic emission (AE) detection technique, to avoid the serious noise disturbance in the fault diagnosis of rotary machine, a de-noising method based on adaptive wavelet correlation analysis to be applied to the AE signal is proposed. First, AE signals are decomposed by dyadic wavelet transform and at the same time the AE signal is divided into available coefficients and noise coefficients. Secondly, the available coefficients are reconstructed to restore the original real signal after de-noising process. Finally, the de-noising threshold is set by adaptive threshold method based on wavele

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22

Zunino, L., D. G. Pérez, M. Garavaglia, and O. A. Rosso. "Wavelet entropy of stochastic processes." Physica A: Statistical Mechanics and its Applications 379, no. 2 (2007): 503–12. http://dx.doi.org/10.1016/j.physa.2006.12.057.

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23

Bing, Hankun, Yuzhu Zhao, Le Pang, and Minmin Zhao. "Research on Fault Diagnosis Model of Rotating Machinery Vibration Based on Information Entropy and Improved SVM." E3S Web of Conferences 118 (2019): 02036. http://dx.doi.org/10.1051/e3sconf/201911802036.

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Based on the concept of information entropy, this paper analyzes typical nonlinear vibration fault signals of steam turbine based on spectrum, wavelet and HHT theory methods, and extracts wavelet energy spectrum entropy, IMF energy spectrum entropy, time domain singular value entropy and frequency domain power spectrum entropy as faults. The feature is supported by a support vector machine (SVM) as a learning platform. The research results show that the fusion information entropy describes the vibration fault more comprehensively, and the support vector machine fault diagnosis model can achiev

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24

Zhang, Ning Ning, and Qiang Zhang. "Characteristic Extraction of Fatigue Driver's EEG Signals Based on Wavelet Entropy." Advanced Materials Research 779-780 (September 2013): 1019–22. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.1019.

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This study aims to develop a method to detect drivers fatigue using the EEG signals. Experiments have been designed to test the subjects under simulated driving and actual driving, and the fatigue drivers Electroencephalogram (EEG) signals were collected. Wavelet transform method was applied to de-noise the raw EEG data. The H, R (H=α/β; R= (α+θ)/β) wavelet entropy were calculated. The results show that the fatigue drivers H, R wavelet entropy decreased after rest (P<0.05). It is concluded that there are significant difference in brain function between fatigue states and recovered after res

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25

Sychev, Sergey, and Andre D. L. Batako. "A Study of Sliding Friction Using an Acoustic Emission and Wavelet-Based Energy Approach." Machines 12, no. 4 (2024): 265. http://dx.doi.org/10.3390/machines12040265.

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The purpose of this work is to study the mechanism of running-in during friction and to determine the informative parameters characterizing the degree of its completion. During friction, contact interaction of rough surfaces causes various wave phenomena covering a wide range of frequencies, the subsequent frequency analysis can provide information about the sizes of wave sources and thereby clarify the mechanism of interaction between surface roughness. The using of the wavelet transform for processing the signals of audible acoustic emission made it possible to determine the beginning and th

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26

Chen, Ji Kai, Hao Yu Li, Shi Yan Yang, and Bao Quan Kou. "A New Method for Extracting Transient Signal Feature in Transmission System Based on Tsallis Wavelet Entropy." Advanced Materials Research 433-440 (January 2012): 2417–22. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2417.

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To reduce the computing complexity of Shannon wavelet entropy(WE), Tsallis WE algorithm was proposed and implemented by combining Tsallis entropy with lifting wavelet transform(LWT), which provided a new method to extract features of transient signals in transmission system. By adjusting the nonextension index, the property of Tsallis entropy was analyzed, and the relations between Tsallis entropy and Shannon entropy were discussed. Taking for instance Tsallis wavelet energy entropy(WEE), the computing complexity of Tsallis WE was analyzed and compared with Shannon WE. In order to verify the p

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27

Wang, Tingzhong, Lingli Zhu, Miaomiao Fu, Tingting Zhu, and Ping He. "Repetitive Transient Extraction Using the Optimized SES Entropy Wavelet for Fault Diagnosis of Rotating Machinery." Shock and Vibration 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/8290717.

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Repetitive transients are usually generated in the monitoring data when a fault occurs on the machinery. As a result, many methods such as kurtogram and optimized Morlet wavelet and kurtosis method are proposed to extract the repetitive transients for fault diagnosis. However, one shortcoming of these methods is that they are constructed based on the index of kurtosis and are sensitive to the impulsive noise, leading to failure in accurately diagnosing the fault of the machinery operating under harsh environment. To address this issue, an optimized SES entropy wavelet method is proposed. In th

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28

Garcia-Alvarez, J., H. Führ, and G. Castellanos-Domínguez. "Wavelet-based Entropy Measure for Rate-Distortion Optimization in Image Coding." International Journal of Electronics and Telecommunications 56, no. 1 (2010): 25–32. http://dx.doi.org/10.2478/v10177-010-0003-6.

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Wavelet-based Entropy Measure for Rate-Distortion Optimization in Image CodingA novel method for calculation of the entropy measure in wavelet space is proposed. This perceived-based entropy measure uses a Second Order Model entropy estimator, in which the occurrence of neighbors is considered in formulation. It has the intention to allow the implementation of a more suitable measure in coding processes and a relationship between the metric and the description of perceptual features. This method is used for the Rate-Distortion optimization in order to improve the bit-allocation coding algorith

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29

Göksu, Hüseyin. "Engine Speed–Independent Acoustic Signature for Vehicles." Measurement and Control 51, no. 3-4 (2018): 94–103. http://dx.doi.org/10.1177/0020294018769080.

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A vehicle, when running, makes a complex sound emission from the engine, the exhaust, the air conditioner, and other mechanical parts. Analysis of this sound for the purpose of vehicle identification is an interesting practice which has security- and transportation-related applications. Engine speed variation, which causes shifts in the frequency content of the emissions, makes Fourier-based methods ineffective in terms of providing a stable signature for the vehicle. We search for an engine speed–independent acoustic signature for the vehicle, and for this purpose, we propose wavelet packet a

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30

Zhao, Wei Guo, and Li Ying Wang. "Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM." Advanced Materials Research 121-122 (June 2010): 813–18. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.813.

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On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from th

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31

Wang, Li Ying, Wei Guo Zhao, and Ying Liu. "Rolling Bearing Fault Diagnosis Based on Wavelet Packet- Neural Network Characteristic Entropy." Advanced Materials Research 108-111 (May 2010): 1075–79. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.1075.

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On the basis of neural network based on wavelet packet-characteristic entropy(WP-CE) the author proposes a new fault diagnosis method of vibrating of hearings, in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted, the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample the three layers BP neural network is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective

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32

Zhang, Ai Hua, Ming Chun Kou, Chen Diao, and Dong Mei Lin. "Quality Assessment of ECG Signal Based on Wavelet Energy Ratio and Wavelet Energy Entropy." Applied Mechanics and Materials 530-531 (February 2014): 577–80. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.577.

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ECG signal is affected by many factors such as noise and interference in the process of acquisition, which make it difficult for clinicians to interpret the ECG signal precisely and effectively. In order to detect whether an ECG signal is worthy to be interpreted by clinicians, an algorithm was proposed to assess the quality of ECG signal based on wavelet energy ratio and wavelet energy entropy. After wavelet decomposition, the ECG signals wavelet energy ratio and wavelet energy entropy were calculated in three different frequency bands, and we defined them as the quality indices to evaluate t

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Sun, Zhiqiang, Shuai Shao, and Hui Gong. "Gas–liquid Flow Pattern Recognition Based on Wavelet Packet Energy Entropy of Vortex-induced Pressure Fluctuation." Measurement Science Review 13, no. 2 (2013): 83–88. http://dx.doi.org/10.2478/msr-2013-0016.

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Here we report a novel flow-pattern map to distinguish the gas-liquid flow patterns in horizontal pipes at ambient temperature and atmospheric pressure. The map is constructed using the coordinate system of wavelet packet energy entropy versus total mass flow rate. The wavelet packet energy entropy is obtained from the coefficients of vortex-induced pressure fluctuation decomposed by the wavelet packet transform. A triangular bluff body perpendicular to the flow direction is employed to generate the pressure fluctuation. Experimental tests confirm the suitability of the wavelet packet energy e

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Safara, Fatemeh, Shyamala Doraisamy, Azreen Azman, Azrul Jantan, and Sri Ranga. "Wavelet Packet Entropy for Heart Murmurs Classification." Advances in Bioinformatics 2012 (November 25, 2012): 1–6. http://dx.doi.org/10.1155/2012/327269.

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Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered includ

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Qin, Na, Wei Dong Jin, Jin Huang, Peng Jiang, and Zhi Min Li. "High Speed Train Bogie Fault Signal Analysis Based on Wavelet Entropy Feature." Advanced Materials Research 753-755 (August 2013): 2286–89. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2286.

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Mechanical behavior of high speed trains bogie seriously impact the reliability of the train system. Performance monitoring and fault diagnosis for the critical component on bogie are very important. Simulation data of high speed train bogie fault signal is selected in data experiment. Based on multiresolution analysis, wavelet entropy features are extracted to reflect the uncertainty level of the vibration signal on scales. In the high dimension space composed by several wavelet entropy features, the dates from four fault patterns are classified and the result is satisfactory. Result show tha

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Wang, Qingjun, Yibo Li, and Xueping Liu. "Analysis of Feature Fatigue EEG Signals Based on Wavelet Entropy." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 08 (2018): 1854023. http://dx.doi.org/10.1142/s021800141854023x.

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Fatigue driving is bringing more and more serious harm, but there are various reasons for fatigue driving, it is still difficult to test the driver’s fatigue. This paper defines a method to test driver’s fatigue based on the EEG, and different from other researches into fatigue driving, this paper mainly takes the fatigue features of EEG signals in fatigue state and uses wavelet entropy as the feature extraction method to analyze the features of wavelet entropy and spectral entropy features as well as the classification accuracy under the same classifier. The SVM is used to show the classifier

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Rodriguez, Nibaldo, Pablo Alvarez, Lida Barba, and Guillermo Cabrera-Guerrero. "Combining Multi-Scale Wavelet Entropy and Kernelized Classification for Bearing Multi-Fault Diagnosis." Entropy 21, no. 2 (2019): 152. http://dx.doi.org/10.3390/e21020152.

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Discriminative feature extraction and rolling element bearing failure diagnostics are very important to ensure the reliability of rotating machines. Therefore, in this paper, we propose multi-scale wavelet Shannon entropy as a discriminative fault feature to improve the diagnosis accuracy of bearing fault under variable work conditions. To compute the multi-scale wavelet entropy, we consider integrating stationary wavelet packet transform with both dispersion (SWPDE) and permutation (SWPPE) entropies. The multi-scale entropy features extracted by our proposed methods are then passed on to the

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38

Yang, Hongyi, and Han Yang. "Evolution of Entropy in Art Painting Based on the Wavelet Transform." Entropy 23, no. 7 (2021): 883. http://dx.doi.org/10.3390/e23070883.

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Quantitative studies of art and aesthetics are representative of interdisciplinary research. In this work, we conducted a large-scale quantitative study of 36,000 paintings covering both Eastern and Western paintings. The information entropy and wavelet entropy of the images were calculated based on their complexity and energy. Wavelet energy entropy is a feature that can characterize rich information in images, and this is the first study to introduce this feature into aesthetic analysis of art paintings. This study shows that the process of entropy change coincides with the development proce

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Komasi, Mehdi, and Soroush Sharghi. "Recognizing factors affecting decline in groundwater level using wavelet-entropy measure (case study: Silakhor plain aquifer)." Journal of Hydroinformatics 21, no. 3 (2019): 510–22. http://dx.doi.org/10.2166/hydro.2019.111.

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Abstract The most important approach to identify the behavior of hydrological processes is time series analysis of this process. Wavelet-entropy measure has been considered as a criterion for the degree of time series fluctuations and consequently uncertainty. Wavelet-entropy measure reduction indicates the reduction in natural time series fluctuations and thus, the occurrence of an unfavorable trend in time series. In this way, to identify the main cause of declining aquifer water level in the Silakhor plain, monthly time series of rainfall, temperature and output discharge were divided into

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Rodriguez, Nibaldo, Lida Barba, Pablo Alvarez, and Guillermo Cabrera-Guerrero. "Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis." Entropy 21, no. 6 (2019): 540. http://dx.doi.org/10.3390/e21060540.

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Bearing fault diagnosis methods play an important role in rotating machine health monitoring. In recent years, various intelligent fault diagnosis methods have been proposed, which are mainly based on the features extraction method combined with either shallow or deep learning methods. During the last few years, Shannon entropy features have been widely used in machine health monitoring, improving the accuracy of the bearing fault diagnosis process. Therefore, in this paper, we consider the combination of multi-scale stationary wavelet packet analysis with the Fourier amplitude spectrum to obt

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Chen, Li, Jian Shen, Bin Zhou, Qingsong Wang, and Giuseppe Buja. "Quantitative Analysis on the Proportion of Renewable Energy Generation Based on Broadband Feature Extraction." Applied Sciences 12, no. 21 (2022): 11159. http://dx.doi.org/10.3390/app122111159.

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With the massive access of distributed renewable energy sources, many uncertain renewable energy power components have been added to the low-voltage lines in substations in addition to the loads of definite classification. From the perspective of economy and cleanliness, it is necessary to quantitatively analyze the renewable energy share among them and improve the power quality level of users. For the power quality information at low-voltage feeders, this paper proposes a quantitative analysis algorithm based on improved wavelet energy entropy and LSTM neural network. The method is based on w

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Wu, Zai Xin, Yong Wei Wang, and Tao Liu. "Analysis of Vibration Signals of Scroll Compressor Based on Information Entropy." Applied Mechanics and Materials 401-403 (September 2013): 1523–28. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1523.

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In order to make a quantitative description for the running status of the scroll compressor, a fault diagnosis method of information entropy is proposed on the basis of the theory of entropy. This method is based on singular spectrum entropy in time domain, power spectrum entropy in frequency domain, wavelet power spectrum entropy and wavelet space feature spectrum entropy in time-frequency domain, which is used as the comprehensive appraisal index of quantitative feature for vibration status of scroll compressor. Under condition of invariable speed operation, the identification of natural fre

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Li, Yuxing, Feiyue Ning, Xinru Jiang, and Yingmin Yi. "Feature Extraction of Ship Radiation Signals Based on Wavelet Packet Decomposition and Energy Entropy." Mathematical Problems in Engineering 2022 (January 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/8092706.

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The analysis of ship radiation signals to identify ships is an important research content of underwater acoustic signal processing. The traditional fast Fourier transform (FFT) is not suitable for analyzing non-stationary, non-Gaussian, and nonlinear signal processing. In order to realize the feature extraction and accurate classification of ship radiation signals with higher accuracy, a feature extraction method of ship radiation signals based on wavelet packet decomposition and energy entropy is proposed in this paper. According to wavelet packet decomposition, the ship radiation signal is d

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Cai, Zhi Yuan, and Tie Li. "Material Level Noise Measuring for Steel Ball Coal Mill Based on Energy Entropy of Wavelet Packet and Least Squares Support Vector." Advanced Materials Research 383-390 (November 2011): 7183–88. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.7183.

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A new material level noise measuring method of steel ball coal mill was proposed on the basis of energy entropy of wavelet packet and least squares support vector machines. First, four layers wavelet packet decomposition of the acquired noise signals was performed and the wavelet packet energy entropy was extracted; then the eigenvector of wave packet of the noise signals was constructed, the least squares support vector machines were trained to intelligent material level measuring by taking this eigenvector as sample. The simulation result from the proposed method is effective and feasible.

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Kaushal, Pauroosh, and Rohini Mudhalwadkar. "Stationary wavelet singular entropy based electronic tongue for classification of milk." Transactions of the Institute of Measurement and Control 42, no. 4 (2020): 870–79. http://dx.doi.org/10.1177/0142331219893895.

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Electronic tongue mimics human gustatory sensation and is used to characterize and discriminate beverages and foods. Feature extraction plays a key role in improving the classification accuracy by preserving the distinct characteristics while reducing high dimensionality of data generated from electronic tongue. This paper presents a new feature extraction method based on stationary wavelet singular entropy for a developed electronic tongue system to classify pasteurized cow milk. The electronic tongue consists of an array of five working electrodes along with a reference and a counter electro

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Heidari, Mohammad, Hadi Homaei, Hossein Golestanian, and Ali Heidari. "Fault diagnosis of gearboxes using wavelet support vector machine, least square support vector machine and wavelet packet transform." Journal of Vibroengineering 18, no. 2 (2016): 860–75. http://dx.doi.org/10.21595/jve.2015.16184.

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This work focuses on a method which experimentally recognizes faults of gearboxes using wavelet packet and two support vector machine models. Two wavelet selection criteria are used. Some statistical features of wavelet packet coefficients of vibration signals are selected. The optimal decomposition level of wavelet is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. Eventually, the gearbox faults are

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47

Liu, Shuang, Minpeng Xu, Jiajia Yang, et al. "Research on Gastroesophageal Reflux Disease Based on Dynamic Features of Ambulatory 24-Hour Esophageal pH Monitoring." Computational and Mathematical Methods in Medicine 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/9239074.

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Ambulatory 24-hour esophageal pH monitoring has been considered as the gold standard for diagnosing gastroesophageal reflux disease (GERD), and in clinical application, static parameters are widely used, such as DeMeester score. However, a shortcoming of these static variables is their relatively high false negative rate and long recording time required. They may be falsely labeled as nonrefluxers and not appropriately treated. Therefore, it is necessary to seek more accurate and objective parameters to detect and quantify GERD. This paper first describes a new effort that investigated the fea

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Chen, Jikai, Yanhui Dou, Yang Li, and Jiang Li. "Application of Shannon Wavelet Entropy and Shannon Wavelet Packet Entropy in Analysis of Power System Transient Signals." Entropy 18, no. 12 (2016): 437. http://dx.doi.org/10.3390/e18120437.

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Kumar, Yatindra, Mohan Lal Dewal, and Radhey Shyam Anand. "Relative wavelet energy and wavelet entropy based epileptic brain signals classification." Biomedical Engineering Letters 2, no. 3 (2012): 147–57. http://dx.doi.org/10.1007/s13534-012-0066-7.

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Dwivedi, Divyanshu, Ashutosh Chamoli, and Sandip Kumar Rana. "Wavelet Entropy: A New Tool for Edge Detection of Potential Field Data." Entropy 25, no. 2 (2023): 240. http://dx.doi.org/10.3390/e25020240.

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Subsurface source boundary identification is a major step in the interpretation of potential field anomalies in geophysical exploration. We investigated the behavior of wavelet space entropy over the boundaries of 2D potential field source edges. We tested the robustness of the method for complex source geometries with distinct source parameters of prismatic bodies. We further validated the behavior with two datasets by delineating the edges of (i) the magnetic anomalies due to the popular Bishop model and (ii) the gravity anomalies of the Delhi fold belt region, India. The results showed prom

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