Journal articles: '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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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 result shows that the crack could be pinpointed based on characteristics from wavelet entropy theory, thus having an excellent application prospect in the life evaluation of metal defect.

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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 decomposed into different frequency bands, and its energy entropy feature is extracted. As for comparisons, the center frequency and permutation entropy are also used as features to be extracted, then the k-nearest neighbor is applied to classify and recognize the extracted results. Based on the comparisons of wavelet packet decomposition, the center frequency, permutation entropy, and the k-nearest neighbor are used for classification and recognition. The experimental results present that, when comparing with center frequency and permutation entropy, the method based on energy entropy has the best availability, with the highest average recognition rate for four types of ship radiation signals, up to 98%.

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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 used to classify the obtained feature vectors. The experimental results show that this method can acquire comparatively high recognition rate, which proposed a new efficient solution for the identification of plant electrical signals.

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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 (July 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 to evaluate the effectiveness of the developed measure on base wavelet selection. Experimental results demonstrate that the wavelet selected using the developed measure is better suited than other wavelets in diagnosing structural defects in the bearing. The method developed provides systematic guidance in wavelet selection.

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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, some mathematical methods such as Shannon entropy, norm entropy, logarithm energy entropy, threshold entropy, Stein Unbiased Risk Estimate entropy are adopted to measure whether the wavelet packet basis is optimal , minimum Entropy function D value is the best base. Selecting threshold and threshold quantitative is the key to wavelet packet de-noising. And selection of threshold value abides standards such as Sqtwolog, Rigrsure, Heursure, Manimaxi, or Birge-massart. Wavelet packet de-noising method has been applied to tunnel vault sink and landslide monitoring data de-noising processing, which manifests itself being a more elaborate, flexible method compared to wavelet de-noising, since wavelet packet de-noising can even subdivided the low-frequency part and the high-frequency part of upper layer, thus entertains a more precise local analysis capabilities.

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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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Yang, Hongyi, and Han Yang. "Evolution of Entropy in Art Painting Based on the Wavelet Transform." Entropy 23, no. 7 (July 11, 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 process of art painting. Further, the experimental results demonstrate an important change in the evolution of art painting, and since the rise of modern art in the twentieth century, the entropy values in painting have started to become diverse. In comparison with Western paintings, Eastern paintings have distinct low entropy characteristics in which the wavelet entropy feature of the images has better results in the machine learning classification task of Eastern and Western paintings (i.e., the F1 score can reach 97%). Our study can be the basis for future quantitative analysis and comparative research in the context of Western and Eastern art aesthetics.

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Liu, Shuang, Minpeng Xu, Jiajia Yang, Hongzhi Qi, Feng He, Xin Zhao, Peng Zhou, Lixin Zhang, and Dong Ming. "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 feasibility of dynamic features of 24-hour pH recording. Wavelet energy, information entropy, and wavelet entropy were estimated for three groups (severe, mild-to-moderate, and normal). The results suggest that wavelet energy and entropy are physiologically meaningful since they differentiated patients with varying degrees of GERD.K-means clustering algorithm was employed to obtain the sensitivity and specificity of new parameters. It is obvious that information entropy goes with the highest sensitivity of 87.3% and wavelet energy has the highest specificity of 97.1%. This would allow a more accurate definition of the best indicators to detect and quantify GERD as well as provide an alternative insight into the early diagnosis of GERD.

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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 practicality of the new algorithm, the paper carried out not only the simulation test for transient faults in transmission system model, but also the processing of practical harmonics and lighting signal based on DSP, which showed that in comparison with Shannon WE the new algorithm can ensure the accuracy of feature extraction for transient signals , but its runtime has been partially reduced.

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Wan, Shuting, and Xiong Zhang. "Bearing fault diagnosis based on teager energy entropy and mean-shift fuzzy C-means." Structural Health Monitoring 19, no. 6 (April 14, 2020): 1976–88. http://dx.doi.org/10.1177/1475921720910710.

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Feature extraction and fault recognition of vibration signals are two important parts of bearing fault diagnosis. In this article, a fault diagnosis method based on teager energy entropy of each wavelet subband and improved fuzzy C-means is proposed. First, bearing vibration signal is decomposed into wavelet packet and normalized teager energy entropy feature matrix is constructed as clustering index. Principal component analysis is applied to the high-dimensional teager energy entropy feature matrix, and the principal components are determined by cumulative contribution rate to construct feature vectors. Then, the mean-shift method is used to search for the high probability density region of principal components so as to determine the cluster number and cluster center. Finally, fuzzy C-means is used to update the clustering center and membership value, and confirm the optimal clustering center and the type of clustering. Through simulated and experimental analysis, the proposed method has two advantages. The feature vector constructed by this method has better specificity than wavelet energy entropy. The initial clustering center of fuzzy C-means is confirmed by the mean-shift method, which can improve the clustering performance of fuzzy C-means and solve the misclassification without preknowing the number of categories.

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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 (March 31, 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 classified using these statistical features as input to least square support vector machine (LSSVM) and wavelet support vector machine (WSVM). Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of gearboxes. The results of fault classification demonstrate that the WSVM identified the fault categories of gearbox more accurately and has a better diagnosis performance as compared to the LSSVM.

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Chen, Yinsheng, Tinghao Zhang, Zhongming Luo, and Kun Sun. "A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method." Applied Sciences 9, no. 11 (June 8, 2019): 2356. http://dx.doi.org/10.3390/app9112356.

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To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%.

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Mingliang, Liu, Wang Keqi, Sun Laijun, and Zhang Jianfeng. "Applying Energy-Equal Entropy of Wavelet Packet to Diagnose Circuit Breaker Faults." Open Electrical & Electronic Engineering Journal 8, no. 1 (December 31, 2014): 445–52. http://dx.doi.org/10.2174/1874129001408010445.

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Aiming to better reflect features of machinery vibration signals of high-voltage (HV) circuit breaker (CB), a new method is proposed on the basis of energy-equal entropy of wavelet packet(WP). First of all, three-layer wavelet packet decomposes vibration signal, reconstructing 8 nodes of signals in the 3rd layer. Then, the vector is extracted with energy-equal entropy of reconstructed signals. At last, the simple back-propagation (BP) neural network for fault diagnosis contributes to classification of the characteristic parameter. This technology is the basis of a number of patents and patents pending, which is experimentally demonstrated by the significant improvement of diagnose faults.

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Harlalka, Vasudha, Viraj Pradip Puntambekar, Kalugotla Raviteja, and P. Mahalakshmi. "Detection of Epileptic Seizure Using Wavelet Analysis based Shannon Entropy, Logarithmic Energy Entropy and Support Vector Machine." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 935. http://dx.doi.org/10.14419/ijet.v7i4.10.26630.

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Epilepsy is a prevalent condition, mainly affecting the nervous system of the human body. Electroencephalogram (EEG) is used to evaluate and examine the seizures caused due to epilepsy. The issue of low precision and poor comprehensiveness is worked upon using dual tree- complex wavelet transform (DT-CWT), rather than discrete wavelet transform (DWT). Here, Logarithmic energy entropy (LogEn) and Shannon entropy (ShanEn) are taken as input features. These features are fed to Linear Support Vector Machine (L-SVM) Classifier. For LogEn, accuracy of 100% for A-E, 99.34% for AB-E, and 98.67% for AC-E is achieved. While ShanEn combinations give accuracy of 96.67% for AB-E and 95.5% for ABC-E. These results showcase that our methodology is suitable for overcoming the problem and can become an alternate option for clinical diagnosis.

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Chen, Xianqing, and Yan Yan. "Alcoholism Detection by Wavelet Energy Entropy and Linear Regression Classifier." Computer Modeling in Engineering & Sciences 127, no. 1 (2021): 325–43. http://dx.doi.org/10.32604/cmes.2021.014489.

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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 wavelet analysis process was carried out using Discrete Wavelet Transform (DWT) and Wavelet Package Decomposition (WPD) analysis and entropy calculation as feature extraction. In the DWT process, the highest accuracy obtained was 97.98% using Permutation Entropy (PE), Renyi Entropy (RE), and Spectral Entropy (SEN). In WPD, the best accuracy achieved is 98.99 % when 8 sub-bands and RE are used. These results are relatively competitive compared with previous studies using the wavelet method with the same datasets.

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Kováč, Szabolcs, German Micha’čonok, Igor Halenár, and Pavel Važan. "Comparison of Heat Demand Prediction Using Wavelet Analysis and Neural Network for a District Heating Network." Energies 14, no. 6 (March 11, 2021): 1545. http://dx.doi.org/10.3390/en14061545.

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Short-Term Load Prediction (STLP) is an important part of energy planning. STLP is based on the analysis of historical data such as outdoor temperature, heat load, heat consumer configuration, and the seasons. This research aims to forecast heat consumption during the winter heating season. By preprocessing and analyzing the data, we can determine the patterns in the data. The results of the data analysis make it possible to form learning algorithms for an artificial neural network (ANN). The biggest disadvantage of an ANN is the lack of precise guidelines for architectural design. Another disadvantage is the presence of false information in the analyzed training data. False information is the result of errors in measuring, collecting, and transferring data. Usually, trial error techniques are used to determine the number of hidden nodes. To compare prediction accuracy, several models have been proposed, including a conventional ANN and a wavelet ANN. In this research, the influence of different learning algorithms was also examined. The main differences were the training time and number of epochs. To improve the quality of the raw data and remove false information, the research uses the technology of normalizing raw data. The basis of normalization was the technology of the Z-score of the data and determination of the energy‒entropy ratio. The purpose of this research was to compare the accuracy of various data processing and neural network training algorithms suitable for use in data-driven (black box) modeling. For this research, we used a software application created in the MATLAB environment. The app uses wavelet transforms to compare different heat demand prediction methods. The use of several wavelet transforms for various wavelet functions in the research allowed us to determine the best algorithm and method for predicting heat production. The results of the research show the need to normalize the raw data using wavelet transforms. The sequence of steps involves following milestones: normalization of initial data, wavelet analysis employing quantitative criteria (energy, entropy, and energy‒entropy ratio), optimization of ANN training with information energy–entropy ratio, ANN training with different training algorithms, and evaluation of obtained outputs using statistical methods. The developed application can serve as a control tool for dispatchers during planning.

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Sun, Tiankai, Xingyuan Wang, Kejun Zhang, Daihong Jiang, Da Lin, Xunguang Jv, Bin Ding, and Weidong Zhu. "Medical Image Authentication Method Based on the Wavelet Packet and Energy Entropy." Entropy 24, no. 6 (June 8, 2022): 798. http://dx.doi.org/10.3390/e24060798.

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The transmission of digital medical information is affected by data compression, noise, scaling, labeling, and other factors. At the same time, medical data may be illegally copied and maliciously tampered with without authorization. Therefore, the copyright protection and integrity authentication of medical information are worthy of attention. In this paper, based on the wavelet packet and energy entropy, a new method of medical image authentication is designed. The proposed method uses the sliding window to measure the energy of the detail information. In the time–frequency data distribution, the local details of the data are mined. The complexity of energy is quantitatively described to highlight the valuable information. Based on the energy weight, the local energy entropy is constructed and normalized. The adjusted entropy value is used as the feature vector of the authentication information. A series of experiments show that the authentication method has good robustness against shearing attacks, median filtering, contrast enhancement, brightness enhancement, salt-and-pepper noise, Gaussian noise, multiplicative noise, image rotation, scaling attacks, sharpening, JPEG compression, and other attacks.

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Mawlood, Zhyar Q., and Azhin T. Sabir. "Human gait-based gender classification using neutral and non-neutral gait sequences." Innovaciencia Facultad de Ciencias Exactas Físicas y Naturales 7, no. 1 (October 25, 2019): 1–13. http://dx.doi.org/10.15649/2346075x.689.

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A biometric system offers automatic identification of an individual basedon characteristic possessed by the individual. Biometric identification systems are often categorized as physiological or behavioural characteristics.Gait as one of the behavioural biometric recognition aims to recognizean individual by the way he/she walk. In this paper we propose genderclassification based on human gait features using wavelet transform andinvestigates the problem of non-neutral gait sequences; Coat Wearing andcarrying bag condition as addition to the neutral gait sequences. We shallinvestigate a new set of feature that generated based on the Gait Energy Image and Gait Entropy Image called Gait Entropy Energy Image(GEnEI). Three different feature sets constructed from GEnEI basedon wavelet transform called, Approximation coefficient Gait EntropyEnergy Image, Vertical coefficient Gait Entropy Energy Image and Approximation & Vertical coefficients Gait Entropy Energy Image Finallytwo different classification methods are used to test the performance ofthe proposed method separately, called k-nearest-neighbour and SupportVector Machine. Our tests are based on a large number of experimentsusing a well-known gait database called CASIA B gait database, includes124 subjects (93 males and 31 females). The experimental result indicatesthat the proposed method provides significant results and outperform thestate of the art.

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Zhao, Fengkui, Jian Wang, and Aimin Wang. "An Improved Spectral Background Subtraction Method Based on Wavelet Energy." Applied Spectroscopy 70, no. 12 (November 13, 2016): 1994–2004. http://dx.doi.org/10.1177/0003702816665530.

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Most spectral background subtraction methods rely on the difference in frequency response of background compared with characteristic peaks. It is difficult to extract accurately the background components from the spectrum when characteristic peaks and background have overlaps in frequency domain. An improved background estimation algorithm based on iterative wavelet transform (IWT) is presented. The wavelet entropy principle is used to select the best wavelet basis. A criterion based on wavelet energy theory to determine the optimal iteration times is proposed. The case of energy dispersive X-ray spectroscopy is discussed for illustration. A simulated spectrum with a prior known background and an experimental spectrum are tested. The processing results of the simulated spectrum is compared with non-IWT and it demonstrates the superiority of the IWT. It has great significance to improve the accuracy for spectral analysis.

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He, Can, Jianchun Xing, Juelong Li, Wei Qian, and Xun Zhang. "A New Structural Damage Identification Method Based on Wavelet Packet Energy Entropy of Impulse Response." Open Civil Engineering Journal 9, no. 1 (August 24, 2015): 570–76. http://dx.doi.org/10.2174/1874149501509010570.

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Excitation makes a great influence on the wavelet energy distribution of the response signal, this deficiency leads that the traditional structural damage identification method based on wavelet energy has a low precision. In order to solve this problem, a new structural damage identification method based on wavelet packet energy entropy (WPEE) of impulse response is presented in this paper. Firstly, natural excitation technique (NExT) is adopted to extract structural impulse response. Then, WPEE of the impulse response is computed, and the change rate of WPEE is used to construct the structural damage index. An experiment of damage identification on a pile structure is provided to verify the effectiveness of the proposed method. Experiment results show that this method can accurately identify the single damage and multi-damage.

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Li, Bin, Shuang Li, and Xu Chen Lu. "Research on Classification Performance of Circuit Breaker Vibration Signal Based on Fuzzy C-Means Clustering Analysis." Advanced Materials Research 960-961 (June 2014): 1352–55. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.1352.

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With permanent magnetic actuator monostable circuit breaker as the research object, it gets three different running status signal data, normal circuit breaker closing state , break-brake spring single loss and Institutions unsmooth Through constructing data acquisition platform. The performance of the three methods of feature extraction is compared, short-time Fourier transform, wavelet packet energy entropy and Hilbert huang transform, through fuzzy c-means clustering analysis algorithm. Through the comparative study, it is concluded that adopt wavelet packet transform method to get the best classify performance of time-frequency entropy vector .

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Zhang, Min, Hong Qi Liu, and Bin Li. "Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy." Applied Mechanics and Materials 541-542 (March 2014): 1419–23. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.1419.

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Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

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Yan, Ru Qiang, and Robert X. Gao. "Wavelet Selection for Bearing Defect Diagnosis." Key Engineering Materials 413-414 (June 2009): 575–82. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.575.

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This paper introduces a quantitative measure based on the energy-to-Shannon entropy ratio for base wavelet selection in vibration signal analysis. The Gaussian-modulated sinusoidal signal and a realistic vibration signal measured from a ball bearing have been used to evaluate the effectiveness of the measure. Experimental results demonstrate that the wavelet selected using the developed measure is effective in diagnosing structural defects in the bearing and the method developed provides systematic guidance in wavelet selection.

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Dongchao, Liu, Xiong Hui, Zhu Xiaotong, and Xu Lei. "Pattern Recognition of Partial Discharge by Using Scale parameters-Energy Entropy Characteristic Pairs." E3S Web of Conferences 136 (2019): 01026. http://dx.doi.org/10.1051/e3sconf/201913601026.

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In this paper, the complex wavelet transform (CWT) was used to process the ultra-high frequency partial discharge (UHF PD) signal in gas insulated switchgear (GIS) at different scales. The trend curves of complex wavelet transform energy entropy (CWT-EE) under different decomposition scale were analyzed, and it was found that the PD feature information mainly distributed in the scales, in which the gradient of CWT-EE is big. Besides, The CWT-EE characteristics and their scales were extracted to the structure characteristic pairs for PD type identification. The recognition results show that the characteristic pair could effectively identify four typical defects in GIS and obviously reduce the feature dimension.

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Göksu, H. "Ground moving target recognition using log energy entropy of wavelet packets." Electronics Letters 54, no. 4 (February 2018): 233–35. http://dx.doi.org/10.1049/el.2017.4267.

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Göksu, Hüseyin. "BCI oriented EEG analysis using log energy entropy of wavelet packets." Biomedical Signal Processing and Control 44 (July 2018): 101–9. http://dx.doi.org/10.1016/j.bspc.2018.04.002.

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Chen, Yi, Yin Zhang, Hui-Min Lu, Xian-Qing Chen, Jian-Wu Li, and Shui-Hua Wang. "Wavelet energy entropy and linear regression classifier for detecting abnormal breasts." Multimedia Tools and Applications 77, no. 3 (November 22, 2016): 3813–32. http://dx.doi.org/10.1007/s11042-016-4161-0.

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Ananda Babu, T., and Dr P. Rajesh Kumar. "Comparison of different feature extraction methods for the analysis of uterine magnetomyography signals to predict term labor." International Journal of Engineering & Technology 7, no. 3.29 (August 24, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i3.29.18449.

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The prediction of term labor by analyzing the uterine magnetomyographic signals attempted in this research. The existing works did not focus on the classification of the signals. Publicly available MIT-BIH database records were divided into term-labor and term-nonlabor groups. This research presents two methods for feature extraction, discrete wavelet transform and wavelet packet transform. Energy, standard deviation, variance, entropy and waveform length of transform coefficients used in the first method. The normalized logarithmic energy of wavelet coefficients from each packet of the total wavelet packet tree used as the feature space for the second method. The labor assessment done through the classification of the features by using five different classifiers for different mother wavelet families. Discrete wavelet transform features extracted using coif5 wavelet with random subspace classification gives the accuracy, precision and FPrates of 93.9286%, 94.2014% and 5.7986% respectively. Using sym8 wavelet for wavelet packet transform features classified with SVM classifier performed well with 95.8763% accuracy, 95.9719% precision and 4.0281% FPrate. The results obtained from the research will be helpful in term labor assessment and understanding the parturition process.

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Ma, Jun, Jiande Wu, and Xiaodong Wang. "Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine." Advances in Mechanical Engineering 10, no. 1 (January 2018): 168781401775144. http://dx.doi.org/10.1177/1687814017751446.

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Aiming at connatural limitations of extreme learning machine in practice, a new fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine is proposed. On one hand, the presented method can extract the more efficient features using the wavelet packet-energy entropy method, and on the other hand, the sample fuzzy membership degree matrix U, weight matrix W which is used to describe the sample imbalance, and the kernel function are introduced to construct the fuzzy kernel extreme learning machine model with high accuracy and reliability. The experimental results of rolling bearing and check valve are obtained and analyzed in MATLAB 2010b. The results show that the proposed fuzzy kernel extreme learning machine method can obtain fairly or slightly better classification performance than the traditional extreme learning machine, kernel extreme learning machine, back propagation, support vector machine, and fuzzy support vector machine.

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Ahmadipour, Masoud, Hashim Hizam, Mohammad Lutfi Othman, and Mohd Amran Mohd Radzi. "An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network." Energies 11, no. 10 (October 11, 2018): 2701. http://dx.doi.org/10.3390/en11102701.

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This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.

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Palaniappan, Rajkumar, Kenneth Sundaraj, Sebastian Sundaraj, N. Huliraj, and S. S. Revadi. "Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine." Biomedical Engineering / Biomedizinische Technik 63, no. 4 (July 26, 2018): 383–94. http://dx.doi.org/10.1515/bmt-2016-0097.

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Abstract Background: Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. Methods: Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier. Results: The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively. Conclusion: The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

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43

Diwalkar, Shraddha P. "Development & Analysis of 2D Medical Image Fusion Using Wavelets." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 792–99. http://dx.doi.org/10.22214/ijraset.2021.38516.

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Abstract: Medical image fusion is the technique of integrating two or more images from various imaging modalities/scans to get a fused image with information having the details of anatomical information combined from all the modalities for accurate diagnosis and further treatment. This paper performs the analysis of various wavelet functions for decomposition and synthesis. PET (Positron Emission Tomography) and MRI (Magnetic Resonance Imaging) scans of Brain and chest are used and compared using Stationary Wavelet Transform (SWT) and Discrete wavelet Transform (DWT). Entropy is calculated which is a measure of information acquired after the fusion process. Keywords: Wavelet transform, Fusion, Stationary Wavelet Transform, Discrete, Medical image

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Deák, Krisztián. "Design of Discrete Wavelet by Using Transient Model for Exact Measurement of Manufacturing Faults of Tapered Roller Bearings." Periodica Polytechnica Mechanical Engineering 63, no. 2 (March 6, 2019): 113–22. http://dx.doi.org/10.3311/ppme.13034.

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This paper considers a comparison of six wavelets for bearing fault diagnosis. Five wavelets Symlet_05, Symlet_08, Daubechies_04, Daubechies_06, Daubechies_08 are typical ones which are used for fault diagnosis due to several researches. The purpose is to design a new discrete wavelet which has higher efficiency to reveal minor defects on the bearing rings. Defects derive from either manufacturing or operational problems. Detecting of tiny manufacturing defects, especially manufacturing grinding marks is quite difficult due to their special geometrical shapes, however they can cause serious problems in machines during operation. Therefore, it is an important task to diagnose these marks with the most adequate methods. The transient vibration signal model of the defect is established for signals generated by tapered roller bearing on the outer race. The wavelet creation used the sub-optimal algorithm devised by Chapa and Rao. The applicability of the matched wavelet is tested for identifying this kind of bearing failure. The new wavelet analysis and synthesis filter coefficients are determined which define the designed wavelet. To determine the efficiency of the designed wavelet and to establish comparison with the other wavelets, a test-rig was constructed with high-precision measuring sensors and devices. By using the Maximum Energy-to-Shannon Entropy criteria the efficiency of the wavelets is determined. The designed wavelet is found to be the most effective to detect the manufacturing fault compared to the others in this article. The final purpose is not only to detect the faults but to determine their sizes. By analyzing the entry points of the rollers into the defects, the de-stressing point and the exit points of the rollers from the defects the width of the grinding marks is calculated. It is proved that the new-designed wavelet obtains the most precise way for fault width measurement. Finally, the size of the failure is measured by a contact type Mahr Perthometer to compare the results to the calculated parameters and validate them. The width deviation is only 1.18 % in the case of the new-designed wavelet which is remarkable precision level for bearing fault analysis.

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Wang, Chang Gang, Lin Chuan Wang, Jia Liu, and Bin Liu. "A New Method to Detect Voltage Sag Based on Wavelet Energy Entropy." Applied Mechanics and Materials 448-453 (October 2013): 2254–58. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.2254.

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Voltage sags was a common power system disturbance, usually associated with power system faults. Nowadays, there were more and more methods to detect voltage sag, involving with RMS, peak voltage detect, FFT. Owing to their defections, they were too slow when they came to being applied to detect voltages sags. This paper presents a method based on wavelet energy entropy detection voltage sags, this method can detect the drop time quickly and sensitively. In order to verify the feasibility of the proposed method, a numerical example simulation has been done based on matlab / simulink , results show that there is a significant effect.

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Deng, Yujia, Sheng Lin, Ling Fu, Kai Liao, Lei Liu, Zhengyou He, Shan Gao, and Yilu Liu. "New Criterion of Converter Transformer Differential Protection Based on Wavelet Energy Entropy." IEEE Transactions on Power Delivery 34, no. 3 (June 2019): 980–90. http://dx.doi.org/10.1109/tpwrd.2019.2893431.

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Yu, Chen, Jian Lan Li, and Shu Hong Huang. "Vibration Fault Diagnosis of Rotating Machine Based on the Principle of Entropy Increase." Advanced Materials Research 530 (June 2012): 109–14. http://dx.doi.org/10.4028/www.scientific.net/amr.530.109.

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Information fusion is the main method to improve diagnosis accuracy. In this paper, fusion information entropy is defined by fusing speed with three kinds of information entropy that are singular spectrum entropy in time domain, power spectrum entropy in frequency domain and wavelet energy spectrum entropy in time-frequency domain from multiple perspectives to describes vibration fault characteristics. According to the principle of entropy increase, condition information entropy increase of vibration fault are defined and used as new criterion for fault diagnosis, and the grey correlation between sample fault and typical fault is defined to realize the vibration fault diagnosis. Finally, the method’s effectiveness is verified by vibration simulation data from vibration fault simulator. The result shows that this method can identify 6 typical vibration faults correctly.

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PUTHANKATTIL, SUBHA D., and PAUL K. JOSEPH. "HALF-WAVE SEGMENT FEATURE EXTRACTION OF EEG SIGNALS OF PATIENTS WITH DEPRESSION AND PERFORMANCE EVALUATION OF NEURAL NETWORK CLASSIFIERS." Journal of Mechanics in Medicine and Biology 17, no. 01 (February 2017): 1750006. http://dx.doi.org/10.1142/s0219519417500063.

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A detailed understanding of key signal characteristics has enabled the use of artificial neural networks (ANN) for feature detection and classification of EEG signals in clinical research. The present study is performed to classify EEG signals of normal and depression patients with wavelet parameters as key input features. The characteristics of depression cannot be made out by visual inspection of EEG records unlike epilepsy which is well characterized by sudden recurrent and transient waveforms. In this study, a comparison is made between the performance of feedforward neural network (FFNN) and probabilistic neural network (PNN) while classifying the EEG signals of normal and depression patients. Classification capabilities of both the methods are validated with the EEG recordings from 30 normal controls and 30 depression patients. One-way ANOVA provided a statistical significant difference between the two classes of EEG signals recorded. Preprocessing for feature extraction is done using discrete wavelet transform (DWT). The time domain and relative wavelet energy (RWE) features calculated from the sub-bands are given as a set of input to the neural network. Another set of feature used independently for training the network is the wavelet entropy (WE). The FFNN achieved a classification accuracy of 100% and PNN gave an accuracy of 58.75% with time domain and wavelet energy as the input features. With wavelet entropy as the input feature, FFNN further showed 98.75% classification accuracy while PNN gave an accuracy of only 46.5%. The results indicate that FFNN with the given input features is more suitable for the classification of EEG signals with mood changing depressive disorders.

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Liang, Li. "A Method of Extracting and Identifying College Students’ Music Psychological Features Based on EEG Signals." Scientific Programming 2022 (September 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/1503757.

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With the development of information technology, music education in universities is also changing. Traditional music education can not effectively explore the feature of students, resulting in the quality of music education being restricted. The rapid development of Electroencephalogram (EEG) signals has brought a new educational model to music education. Through the extraction of students’ psychological features of music by EEG, psychological features can be identified and different educational programs can be formulated according to the results. Multifeature extraction and combination method can improve the accuracy of EEG feature extraction. Using empirical mode decomposition and wavelet packet decomposition of the two kinds of methods to analyze EEG data, respectively, then the average energy, volatility index, sample entropy, and approximate entropy and multiscale features such as permutation entropy and Hurst index, select features in combination, to classify the feature set after the combination, so as to find out the feature of the performance of the optimal combination. The experimental results show that the feature combination of sample entropy and approximate entropy can better represent the main features of EEG psychological characteristic signals after wavelet packet decomposition, and the recognition accuracy is more than 90%.

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Gao, Zhong-Ke, Dong-Mei Lv, Wei-Dong Dang, Ming-Xu Liu, and Xiao-Lin Hong. "Multilayer Network from Multiple Entropies for Characterizing Gas-Liquid Nonlinear Flow Behavior." International Journal of Bifurcation and Chaos 30, no. 01 (January 2020): 2050014. http://dx.doi.org/10.1142/s0218127420500145.

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Characterizing nonlinear dynamic behaviors underlying multiphase flow has attracted considerable attention from the nonlinear research field. In this paper, the authors develop a novel multiple entropy-based multilayer network (MEMN) for exploring the complex gas-liquid two-phase flow. At first, we carry out the gas-liquid flow experiments to get the multichannel measurements. Then, MEMN is constructed based on the fusion of three nonlinear entropies, namely weighted permutation entropy (WPE), wavelet packet energy entropy (WPEE), and amplitude entropy (AE). For each derived projection network of MEMN, spectral radius and global clustering coefficient are both calculated and they allow effectively uncovering the nonlinear flow behaviors in the transition of different gas-liquid flow patterns. In addition, we perform wavelet time-frequency representation for the two typical flow patterns and the results support our findings well. All these demonstrate that our MEMN framework can effectively characterize the nonlinear evolution of gas-liquid flow from the perspective of complex network theory. And this also provides a novel idea for studying nonlinear complex systems from the observed multivariate time series.

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Journal articles on the topic 'Wavelet energy entropy'

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