Relevant bibliographies by topics / Wavelet energy entropy

Academic literature on the topic 'Wavelet energy entropy'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Wavelet energy entropy"

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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 of the wavelet energy entropy residual of the measured signal and the estimated one to the preset threshold. Testing results show that the wavelet energy entropy is a sensitive indictor to diagnose the sensor faults. The deviations of wavelet energy entropy of sensors under fault-free conditions and faulty ones all exceed the threshold. The severer the fault is, the larger the residuals of the wavelet energy entropy will be. The results prove that the proposed method is valid and effective for the fault detection and diagnosis of the sensors.
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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 (January 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. In all such applications appropriate selection of feature parameter plays an important role in discriminating normal EEG from abnormal. In the recent past one can find the importance of wavelet and wavelet packet towards EEG analysis. This proposed research work investigates the effect of wavelet packet log energy entropy on EEG signals. Entropy being the measure of relative information, the proposed study attempts to discriminate the normal EEGs from abnormal EEG's by employing the log energy entropy features. For better brevity, this study restricts to the analysis of epileptic seizure from normal EEGs. Different decomposition levels from 2 to 5 were considered for wavelet packets with application of Haar, rbio3.1, sym7, dmey wavelets. A one second windowing was introduced for the data segmentation and Shannon's log energy entropy was estimated. Then the statistical non-parametric Wilcoxon model was employed. The result shows that the application of wavelet packet log energy entropy found to be a potential indicator for discriminating epileptic seizure from normal.
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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 the quality of ECG signal. Experimental results show that we can achieve an accuracyof 95.2%.
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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 collected from fuel tank oscillation test are all satisfied, it is show that this method is available.
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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 established. The new method and damage indexes are verified in numerical simulation and laboratory test of simple beam. The analysis results show that lifting wavelet time entropy can identify structural damage moment and the relative lifting wavelet entropy can identify structural damage location. The feasibility of method proposed is instructed.
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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 (November 3, 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 wavelet transform, based on sym8 wavelet basis function; it divides the long time sequence into equal-length small time sequences, calculates each feature component obtained from wavelet transform decomposition separately, then borrows the concept of information entropy to find its energy entropy. After obtaining the energy entropy sequence of each feature component, it then borrows the concept of kurtosis to weighted differentiation of each energy entropy sequence to highlight the feature information and finally, uses the LSTM neural network to classify the power quality information of different renewable energy sources to determine to which interval segment they belong. By building a simulation model to simulate the actual data in the field, the percentage of renewable energy can be quantitatively analyzed efficiently and accurately.
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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 (September 2012): 147–57. http://dx.doi.org/10.1007/s13534-012-0066-7.

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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 achieve higher diagnostic accuracy.
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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 (April 1, 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 entropy as an ideal indicator of the gas-liquid flow patterns. The overall identification rate of the map is 92.86%, which can satisfy most engineering applications. This method provides a simple, practical, and robust solution to the problem of gas-liquid flow pattern recognition.
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Göksu, Hüseyin. "Engine Speed–Independent Acoustic Signature for Vehicles." Measurement and Control 51, no. 3-4 (April 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 analysis rather than traditional time- or frequency-domain methods. Wavelet packet analysis, by providing arbitrary time–frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. Under varying engine speed, sound emissions are recorded from four cars and analyzed by wavelet packet analysis. Wavelet packet analysis subimages are further analyzed to obtain feature vectors in the form of log energy entropy, norm entropy, and energy. These feature vectors are fed into a classifier, multilayer perceptron, for evaluation. While norm entropy achieves a classification rate of 100%, log energy entropy and energy achieves classification rates of 99.26% and 97.79%, respectively. These results indicate that, wavelet packet analysis along with norm entropy and multilayer perceptron provides an accurate vehicle-specific acoustic signature independent of the engine speed.
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Dissertations / Theses on the topic "Wavelet energy entropy"

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Santos, Mikaelle Oliveira. "An?lise Ac?stica de Desvios Vocais Infantis utilizando a Transformada Wavelet." reponame:Repositório Institucional do IFPB, 2016. http://repositorio.ifpb.edu.br/jspui/handle/177683/258.

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Approved for entry into archive by Alex Sandro R?go (alex@ifpb.edu.br) on 2016-11-23T14:10:37Z (GMT) No. of bitstreams: 1 An?lise_Ac?stica_de_Desvios_Vocais_Infantis_utilizando_a_Transformada_Wavelet.pdf: 1701525 bytes, checksum: 950821553f2e8377cd6696b783b657d7 (MD5)
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Dist?rbios da voz podem atingir diferentes faixas et?rias, afetando a qualidade vocal, prejudicando a comunica??o por meio da voz. T?cnicas de processamento digital de sinais de voz podem ser empregadas para auxiliar outros m?todos de avalia??o de dist?rbios da voz, tais como an?lise otorrinolaringol?gica e an?lise perceptivo-auditiva. Crian?as com dist?rbios de voz podem apresentar efeitos negativos no seu desenvolvimento social, educacional e f?sico. A investiga??o e o diagn?stico precoce do desvio vocal infantil permite maior efic?cia no tratamento. Entretanto, a avalia??o de desordens vocais em crian?as apresenta alguns desafios relacionados ?s dificuldades de coopera??o das mesmas durante os exames tradicionais. Nesta pesquisa, as medidas de energia e entropia dos coeficientes de detalhe da transformada wavelet s?o empregadas na avalia??o da qualidade vocal em crian?as. Dois estudos de caso s?o abordados nesta pesquisa: 1) An?lise ac?stica do grau da intensidade do desvio vocal; e 2) An?lise ac?stica da qualidade vocal predominante (rugosidade e soprosidade). As medidas de energia e entropia dos coeficientes de detalhe da transformada wavelet s?o utilizadas de maneira individual e combinada a fim de se obter uma maior efic?cia na classifica??o dos sinais. Para o primeiro estudo de caso, utilizando-se de um vetor h?brido de medidas combinadas, foram obtidas acur?cias acima de 95% e, para o segundo, utilizando-se tamb?m do vetor de medidas combinadas, as medidas de acur?cia foram superiores a 90%. Os sinais das vozes desviadas apresentaram eleva??o em suas frequ?ncias dos formantes, comparados ?s vozes sem desvio. Os resultados obtidos nesta pesquisa indicam que o uso das medidas de energia e entropia dos coeficientes de detalhe da transformada wavelet mostra-se como uma t?cnica promissora, que pode ser considerada para ser empregada como uma ferramenta para an?lise ac?stica da qualidade vocal em crian?as.
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Li, Feng_Yu, and 李豐裕. "Wavelet Energy Entropy Applied to Detection Enhancement of Islanding Operation of Distributed Generation Systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/23956821646444188290.

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碩士
國立成功大學
電機工程學系碩博士班
96
The islanding operation of distributed generation systems happens when the distributed generators are disconnected from the mains power such that the load is purely supplied by those connected distributed generators. Under such scenarios, if the occurred event is not informed in time, it may damage the electric power equipment and affect the safety of operation personnel. In view of this importance, the thesis has devoted to the study of islanding operation detection via the wavelet energy entropy method. It is found that by employing the proposed method, it would help quantify the waveform such that energy distribution of voltage as well as frequency is better grasped, thus assisting the forewarning of the occurrence of islanding events. To confirm the feasibility of the method, the simulated model has been tested by the proposed approach and FPGA validation. Test results help support the method for the islanding detection enhancement of distributed generation systems.
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Book chapters on the topic "Wavelet energy entropy"

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Fernandez, Jincy J., and Nithyanandam Pandian. "Iris Recognition Using Integer Wavelet Transform and Log Energy Entropy." In Lecture Notes in Electrical Engineering, 15–29. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6987-0_2.

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Yan, Yan. "Gingivitis Detection by Wavelet Energy Entropy and Linear Regression Classifier." In Intelligent Computing Theories and Application, 185–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84532-2_17.

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Lang, Kuan, Zongyi Xing, Wei Dong, and Xudong Gao. "A Rail Corrugation Detection Method Based on Wavelet Packet Energy Entropy." In Lecture Notes in Electrical Engineering, 205–14. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7989-4_21.

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Muszkats, Juan P., Miguel E. Zitto, Miryam Sassano, and Rosa Piotrkowski. "Application of Wavelet Transform to Damage Detection in Brittle Materials via Energy and Entropy Evaluation of Acoustic Emission Signals." In Applications of Wavelet Multiresolution Analysis, 75–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61713-4_5.

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Ding, Kai, Xinghua Li, Hao Li, Huayuan Ma, Lei Fan, and Xiaogang Qi. "Wavelet Packet Energy Entropy Based Feature Analysis of Seismic Signals from Vehicle Targets." In Advances in Intelligent Systems and Computing, 153–58. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5887-0_22.

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Nowak, W., E. Szul-Pietrzak, and A. Hachol. "Wavelet Energy and Wavelet Entropy as a New Analysis Approach in Spontaneous Fluctuations of Pupil Size Study – Preliminary Research." In IFMBE Proceedings, 807–10. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_200.

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Zhang, Yuanyuan, Qingwu Gong, and Xi Shi. "A Novel Adaptive Reclosure Criterion for HV Transmission Lines Based on Wavelet Packet Energy Entropy." In Advances in Neural Networks – ISNN 2009, 874–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01513-7_95.

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Zhang, Bowen, Bingdie Huang, Qun Wu, Guowei Lu, and Yao Wu. "Research on the Analysis of Muscle Fatigue Based on the Algorithm of Wavelet Packet Entropy in sEMG." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220040.

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Surface Electromyography (sEMG) has been widely applied in different fields, such as human-computer interaction and medical rehabilitation. This paper deeply studies the theoretical research of muscle fatigue analysis and scientific experiments of sEMG, especially related to muscle fatigue. It provides a theoretical basis for not only building an assessment method of muscle fatigue but also testing the relationship between the value of wavelet packet entropy and the complexity of signal frequency. Additionally, the field value of muscle fatigue has been uncovered. In this research, the experiment tests the energy variation of wavelet packet entropy when testing the muscle contraction by the sEMG signals of brachioradialis. It means that the experiment decomposes frequency band, and then sEMG is analyzed by the entropy and distribution of energy. The result of experiment indicated that the index of wavelet packet entropy has an efficient and quick performance on analyzing complexity of signal system. In the experiment of muscle fatigue, wavelet packet entropy can present high accuracy, instant reaction, stronger consistency and reliability, which is significant for the achievement of the real-time monitoring and clinical research of bioelectrical signals.
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Liu, Xiao-Ming, Yang Zhao, Yun-Dong Cao, and Chun-Guang Hou. "Modeling research of low voltage AC arc fault based on the wavelet energy entropy." In Environment, Energy and Sustainable Development, 1187–91. CRC Press, 2013. http://dx.doi.org/10.1201/b16320-245.

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Kumar, Himanshu, Nagarajan Ganapathy, Subha D. Puthankattil, and Ramakrishnan Swaminathan. "Classification of Emotional States Using EEG Signals and Wavelet Packet Transform Features." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220632.

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In this work, an attempt has been made to classify arousal and valence states of emotion using time-domain features extracted from the Wavelet Packet Transform. For this, Electroencephalogram (EEG) signals from the publicly available DEAP database are considered. EEG signals are first decomposed using wavelet packet decomposition into θ, α, β, and γ bands. Then featural, namely band energy, sub-band energy ratio, root mean of energy, and information entropy of band energy is estimated. These features are fed into various machine learning classifiers such as support vector machines, linear discriminant analysis, K-nearest neighbor, and random forest. Results indicate that features extracted from wavelet packet transform can predict the arousal and valence emotional states. It is also seen that Support Vector Machines perform the best for both arousal (f-m = 75.68%) and valence(f-m=57.53%). This method can be used for the recognition of emotional states for various clinical purposes in emotion-related psychological disorders like major depressive disorder.
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Conference papers on the topic "Wavelet energy entropy"

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Vora, Rita A., V. A. Bharadi, and H. B. Kekre. "Retinal scan recognition using wavelet energy entropy." In 2012 International Conference on Communication, Information & Computing Technology (ICCICT). IEEE, 2012. http://dx.doi.org/10.1109/iccict.2012.6398120.

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Li, Rui, Nan-hua Yu, Ming Gong, Yi-dan Su, Bin-bin Zhang, Bing-kun Xu, and Chun-xiang Li. "Fault diagnosis algorithm for distribution line based on wavelet singular entropy and wavelet energy entropy." In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2017. http://dx.doi.org/10.1109/iaeac.2017.8054451.

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Yang, He, Xiubing Jing, Zehui Zheng, and Peng Shang. "Chatter Detection Based on Wavelet Packet Energy Entropy." In 2022 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO). IEEE, 2022. http://dx.doi.org/10.1109/3m-nano56083.2022.9941603.

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Yan, Yan, and Dimas Lima. "Alcoholism via wavelet energy entropy and support vector machine." In UCC '21: 2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3492323.3495617.

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Hui Liu, GuoHai Liu, and Yue Shen. "Lifting wavelet scheme and wavelet energy entropy theory for transient power quality detection." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4592853.

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Shuai Wang, Tianshu Bi, and Ke Jia. "Wavelet entropy based fault detection approach for MMC-HVDC lines." In 2015 IEEE Power & Energy Society General Meeting. IEEE, 2015. http://dx.doi.org/10.1109/pesgm.2015.7286582.

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Xuewei, Wang, Zhao Yong, and Wang Lin. "Best Wavelet Packet Power Measurement Based on Energy Threshold Entropy." In 2011 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2011. http://dx.doi.org/10.1109/icicta.2011.100.

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Wang, Zhicheng, Yan Tian, Jian Liu, and Jinwen Tian. "Wavelet fusion method based on local energy and local entropy." In Visual Communications and Image Processing 2005. SPIE, 2005. http://dx.doi.org/10.1117/12.633205.

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Liu, Cuicui, Feng Wang, Fang Zhuo, and Ziqian Zhang. "Fault Diagnosis of HVDC Transmission System Using Wavelet Energy Entropy and the Wavelet Neural Network." In 2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe). IEEE, 2020. http://dx.doi.org/10.23919/epe20ecceeurope43536.2020.9215964.

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Besbes, Salsabil, and Zied Lachiri. "Wavelet packet energy and entropy features for classification of stressed speech." In 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA). IEEE, 2016. http://dx.doi.org/10.1109/sta.2016.7952076.

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