A Novel Design of Morlet Wavelet to Solve the Dynamics of Nervous Stomach Nonlinear Model

The present study introduces a novel design of Morlet wavelet neural network (MWNN) models to solve a class of a nonlinear nervous stomach system represented with governing ODEs systems via three categories, tension, food and medicine, i.e., TFM model. The comprehensive detail of each category is designated together with the sleep factor, food rate, tension rate, medicine factor and death rate are also provided. The computational structure of MWNNs along with the global search ability of genetic algorithm (GA) and local search competence of active-set algorithms (ASAs), i.e., MWNN-GA-ASAs is applied to solve the TFM model. The optimization of an error function, for nonlinear TFM model and its related boundary conditions, is performed using the hybrid heuristics of GA-ASAs. The performance of the obtained outcomes through MWNN-GA-ASAs for solving the nonlinear TFM model is compared with the results of state of the article numerical computing paradigm via Adams methods to validate the precision of the MWNN-GA-ASAs. Moreover, statistical assessments studies for 50 independent trials with 10 neuron-based networks further authenticate the efficacy, reliability and consistent convergence of the proposed MWNN-GA-ASAs.

Characterization of Monsoonal Rainfall in the Sudano-Sahelian Zone of Northwestern Nigeria

Wavelet transform has been underutilized in characterization of rainfall (Real Onset Dates and Real Cessation Dates) in the study area. This study aims at the characterization of monsoonal rainfall. Daily rainfall data of four stations for the period 1981-2018 were collected from Nigerian Meteorological Agency. The Intra-seasonal Rainfall Monitoring Index (IRMI) was generated and used in determining the RODs and RCDs. The Mann–Kendall test was used to detect trends of the rainfall characteristics. Wavelet transform was used in modelling RODs and RCDs. Findings revealed that RODs vary between stations. There is low (0.3 Spearman’s Rank r) correlation between latitudes and Early Cessations (ECs) of rains. The Morlet wavelet analysis revealed that from 1999 to 2018, there were more of EOs and NOs especially in Kano station. We conclude that from 1981 to 2018 there has been a minimal increase in the retreat dates of rainfall in the study area.

Repetitive Transient Extraction Using the Optimized SES Entropy Wavelet for Fault Diagnosis of Rotating Machinery

Repetitive transients are usually generated in the monitoring data when a fault occurs on the machinery. As a result, many methods such as kurtogram and optimized Morlet wavelet and kurtosis method are proposed to extract the repetitive transients for fault diagnosis. However, one shortcoming of these methods is that they are constructed based on the index of kurtosis and are sensitive to the impulsive noise, leading to failure in accurately diagnosing the fault of the machinery operating under harsh environment. To address this issue, an optimized SES entropy wavelet method is proposed. In the proposed method, the optimized parameters including bandwidth and central frequency of Morlet wavelets are selected. Then, based on the wavelet coefficients decomposed using the optimized Morlet wavelet, the SES entropy is calculated to select the scales of wavelet coefficients. Finally, the repetitive transients are reconstructed based on the denoising wavelet coefficients of the selected scales. One simulation case and vibration data collected from the experimental setup are used to verify the effectiveness of the proposed method. The simulated and experimental analyses showed that the signal-to-noise ratio (SNR) of the proposed method has the largest value. Specifically, the SNR in the experimental analysis of the proposed method is 0.6, while that of the other three methods is 0.043, 0.0065, and 0.0045, respectively. Therefore, the result shows that the proposed method is superior to the traditional methods for repetitive transient extraction from the vibration data suffered from impulsive noise.

Research On Arc Fault Detection Using Morlet Wavelet Features And ResNet

Series arc fault is the main cause of electrical fire in low-voltage distribution system. A fast and accurate detection system can reduce the risk of fire effectively. In this paper, series arc experiment is carried out for different kinds of electrical load. The time-domain current is analyzed by Morlet wavelet. Then, the multiscale wavelet coefficients are expressed as the coefficient matrix. We use HSV color index to map the coefficient matrix to the phase space image. Random gamma transform and random rotation are applied to data enhancement. Finally, typical deep residual network (ResNet) is established for image recognition. Training results show that this method can detect faults in real time. The accuracy of ResNet50 is 96.53% by using the data set in this paper.

Designing of Morlet wavelet as a neural network for a novel prevention category in the HIV system

The aim of this work is to present a design of Morlet wavelet neural network (MWNN) for solving a novel prevention category (P) in the HIV system, known as HIPV mathematical model. The numerical performance of the novel HIPV mathematical model will be observed by exploiting the MWNN that works through the optimization procedures of global/local via “genetic algorithm (GA)” and local search “interior-point algorithm (IPA)”, i.e. MWNN-GA-IPA. An error function using the differential HIPV mathematical model and its initial conditions is presented and optimized by the MWNN-GA-IPA. The obtained results have been compared with the Adams method to check the competence of the MWNN-GA-IPA. For the reliability and stability of the scheme, the performance using different statistical operators has been performed based on the multiple independent trials to solve the novel HIPV mathematical model.

Himalayan Tectonic Belt: Morlet Wavelet Variation and Seismic Harmony

Morlet wavelet analysis is a method of studying the periodic spectrum of non-stationary physical signals and is applied to the Himalayan Tectonic Belt to explore whether there is any seismic periodicity, and to explore the possibility of harmony or commonality of properties among the seismic activities of different zones. The earthquake sequence during 1951–2016 with magnitudes M ≥ 6.0 is analysed. Wavelet non-periodicity for the Centre zone suggests a non-uniform spatial–temporal distribution of earthquake movement between plates which may relate with the rare great earthquakes, while the periodicities for the west and east zones may suggest the concurrence with the adjustment of the tectonic movement of the east- and west-end regions of the Himalayan Tectonic Belt relative to its central core. These three zones collectively form the Himalayan Tectonic Belt. This contains a periodicity of about five years of seismic activity that tests successfully with a 95% confidence statistic. Borrowing from the concept of musical harmony, this is the significant seismic harmonic which reflects the Belt’s pervasive tectonic stress and an overall harmony of continent–continent plate convergence. Morlet wavelet analysis also reveals the Himalayan Tectonic Belt and the Pamir–Hindu Kush Tectonic Zone to be engaged as a big new family: the Himalayan Tectonic Belt Plus. It is demonstrated that this new whole also has seismic harmony with the common property again being the 5-year periodicity. This indicates a unified structure of pervading active stress and seismic harmony permeating the overall seismicity.