Transform Analysis Method Research Articles

Evaluation of Single-Event Effect Current-Carrier Mapping Based on Experimental Data.

For single-event radiation damage of power MOSFET devices, this paper aims to establish a statistical analysis method based on external observation (gate/drain current characteristics in irradiation environment) to recognize and evaluate the radiation evolution process and damage mechanism of microscopic physical quantities inside the devices, namely current-carrier (CC) mapping. Firstly, a special data fluctuate-collapse transform analysis method is proposed according to the temporal characteristics of the gate/drain current. Secondly, a carrier dynamic balance ratio based on current data is defined to evaluate the radiation damage degree of the device. TCAD is used to deeply study the relationship between the external current characteristics and the evolution process of internal physical quantities and the damage mechanism. The results show that the current data timing analysis based on fluctuate-collapse transformation can better peer into the evolution process of irradiation events inside the device, and the statistical analysis based on the dynamic balance ratio of carriers can evaluate the severity of irradiation damage to a certain extent.

Mechanism-driven improved SVMD: an indirect approach for rail corrugation detection using axle box acceleration

Abstract Addressing the challenge of the current inability to qualitatively identify rail corrugation damage accurately from axle box acceleration data, this study proposes a novel approach. To indirectly identify rail corrugation from axle box acceleration, we introduce an improved successive variational mode decomposition (SVMD) algorithm, coupled with a deep learning model for corrugation recognition. First, a numerical model of the vehicle-rail-track slab system is established, considering rail corrugation. Indicator analysis is integrated into the SVMD. The improved SVMD is employed to decompose and reconstruct axle box acceleration, achieving noise reduction and extraction of useful components. Next, we apply the fast Continuous Wavelet Transform analysis method to effectively transform one-dimensional data into two-dimensional images. Finally, the You Only Look Once (YOLO) model serves as a classifier for the classification and recognition of corrugation with different wavelengths. The results demonstrate that the mechanism-driven improved SVMD effectively extracts corrugation components from axle box acceleration, while the YOLO model achieves rapid and efficient identification and classification of corrugation with different wavelengths. The results show that compared with other traditional models, the training time of the YOLO model is 60%–90% of the training time of the traditional algorithm, and the Recall rate is 1.15–1.45 times that of the traditional algorithm. In terms of wavelength identification, the YOLO model has a recognition rate of 98% for different wavelengths. The proposed approach offers an innovative and efficient solution for identifying rail corrugation damage in axle box acceleration data.

Modification method and impact analysis of aerodynamic loads for fixed wind-wave hybrid devices

Aerodynamic load is one of the important loads that must be considered for wind-wave energy hybrid devices. In this study, the effects of blade angle and foundation structure vibration on aerodynamic loads are considered, and the traditional blade element momentum (BEM) is dynamically modified by the method of coordinate matrix transformation and vibration function analysis, starting from the relative inflow angle and the instantaneous wind speed of the wind turbine, and the aerodynamic loads of the wind turbine were simulated by using the improved BEM theory. The correctness of the proposed method in this study is verified by comparing the data of a large number of researchers and the computational results of OpenFast. A simulation study has been carried out using AQWA, combining the modified aerodynamic load of the wind turbine and the hydrodynamic load of the wave energy device, to analyze the variation trend of the base structure response of the wind-wave hybrid device under general and extreme conditions. The results show that the method developed in this paper can provide theoretical reference for the development and stable operation of wind-wave hybrid device.

The application of trajectory analysis method and Fast Fourier Transform analysis method in the division of flow instability influence regions under ocean conditions

For offshore nuclear power platforms, complex ocean conditions may lead to flow instability and decrease the safety of the reactor. In this study, the trajectory analysis method is used to assess the stability of the system. The Fast Fourier Transform analysis method is applied to analyze the evolution process of flow fluctuations, and an analysis process is developed for the division of affected regions by flow instability under ocean conditions. The diagram of system operating condition can be quantitatively divided into different regions. The research results indicate that as the power increases, the system trajectory diagram composed of inlet and outlet flows undergoes an evolution process from a convergence loop to a self-sustained loop, and ultimately to a divergence loop. According to the flow fluctuation characteristics of flow instability evolution under ocean conditions, the diagram of system operating condition can be divided into ocean condition control region, joint control region, and flow instability control region. Additionally, the impact of maximum heaving acceleration on the region division is investigated. When the maximum heaving acceleration is relatively small, the ocean condition control region is smaller while the joint control region is larger. This study shows that the triggering mechanism of flow fluctuations under ocean conditions and provides guidance for the design and safe operation of systems in such environments.

Investigating population-specific epilepsy detection from noisy EEG signals using deep-learning models

“Epilepsy is a chronic brain disorder that affects people of all ages. The cause of epilepsy is often unknown and its effect in different age groups is not yet investigated. The main objective of this study is to introduce a novel approach that successfully detects epilepsy even from noisy EEG signals. In addition, this study also investigates population specific epilepsy detection for providing novel insights. Correspondingly, we utilized the TUH EEG corpus database, publicly available challenging multi-channel EEG database containing detailed patient information. We applied a band-pass filter and manual noise rejection to remove noise and artifacts from EEG signals. We then utilized statistical features and correlation to select channels, and applied different transform analysis methods such as continuous wavelet transform, spectrogram, and Wigner-Ville distribution, with and without ensemble averaging, to construct an image dataset. Afterwards, we used various deep-learning models for general analysis. Our findings suggest that different models such as DenseNet201, DenseNet169, DenseNet121, VGG16, VGG19, Xception, InceptionV3, and MobileNetV2 performed better while using images generated from different approaches in general analysis. Furthermore, we split the dataset into two sections according to age for population analysis. All the models that performed well in the general analysis were used for population analysis, which provided novel insights in epilepsy detection from EEG. Our proposed framework for epilepsy detection achieved 100% accuracy, which outperforms other concurrent methods."

Identification and Analysis of Multi-Station Atmospheric Electric Field Anomalies before the Yangbi Ms 6.4 Earthquake on 21 May 2021

This study reports the atmospheric electric field (AEF) anomalies associated with seismic-geological activity recorded by the monitoring network in the Sichuan–Yunnan region of China during the 15–30 days prior to the Yangbi earthquake in Yunnan Province, China, on 21 May 2021. Based on the real-time AEF data from continuous observation, this study summarized the characteristics of the anomalous interference of different meteorological factors on the AEF, compared the simultaneous meteorological data of the AEF anomalies, and ruled out the influence of precipitation, wind, fog, and other weather factors on the AEF anomalies in Yangbi County prior to the Yangbi Ms 6.4 earthquake. The AEF anomalies were identified and extracted from the two-month data from 1 April to 1 June, which were from multiple days, stations, and rupture zones near the 100 km radius from the epicenter of the Yangbi Ms 6.4 main earthquake. Using time series and wavelet transform analysis methods, the obvious common features of the anomalies were summarized, and the homology of the anomalies was verified. The main outcome of the investigation in this study will be used to distinguish and characterize the AEF anomalies associated with pre-seismic geologic activity of non-meteorological elements in the near future.

Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform

As the continuous growth of the machinery industry, the importance of rolling bearings as key connecting parts in machinery movement is also increasing. However, the extraction and diagnosis of rolling bearing fault signals are difficult, and how to use modern transform analysis methods to raise the extraction efficiency and diagnostic accuracy becomes the focus. For this, a rolling bearing fault signal extraction and diagnosis model is designed based on empirical wavelet transform. The diagnostic model is optimized by using support vector machine and quantum genetic algorithm to design a rolling bearing fault signal extraction and diagnosis model based on improved empirical wavelet transform-support vector machine. The test results show that the research method can obtain four component signals showing different anomalies when generating time domain diagrams. Only five component peaks are generated and one group is extracted as output when generating component peaks. The abnormal amplitude of envelope spectrum basically reaches 0.40×10-6 or above. The judgment accuracy of component diagnosis reaches 98.12%. The above results show that the research method has better fault signal extraction ability and better diagnostic accuracy when performing fault signal diagnosis, which can provide new technical support for rolling bearing fault signal extraction and diagnosis.

A Reliable Combination of Double Laplace Transform and Homotopy Analysis Method for Solving a Singular Nonlocal Problem with Bessel Operator

In this article, we present a numerical iterative scheme for solving a non-local singular initial-boundary value problem by combining two well known efficient methods. Namely, the homotopy analysis method and the double Laplace transform method. The resulting scheme is tested on a set of test examples to illustrate its efficiency, it generates the exact analytical solution for each one of these examples. The convergence of the resulting numerical solutions of these examples is tested both graphically and numerically.

Comparative analysis of ANN performance of four feature extraction methods used in the detection of epileptic seizures

Epilepsy, a prevalent neurological disorder characterized by disrupted brain activity, affects over 70 million individuals worldwide, as reported by the World Health Organization (WHO). The development of computer-aided diagnosis systems has become vital in assessing epilepsy severity promptly and initiating timely treatment. These systems enable the detection of epileptic seizures by analyzing the electrical activity in the EEG recordings of the patients. In addition, it helps doctors to choose suitable treatment by quickly determining the type, duration, and characteristics of seizures and increases the patient's quality of life. The proposed computer-aided diagnosis system in this study comprises three modules: preprocessing, feature extraction, and classification. The initial module employs a low-pass Chebyshev II filter to eliminate noise artifacts from signal recordings. The second module involves deriving feature vectors using Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis. The third module employs the Artificial Neural Networks method for epileptic seizure detection. This study not only enables the comparison of feature extraction efficacy among Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis techniques, but it also reveals that Bispectrum Analysis and Empirical Mode Decomposition yield the highest accuracy rate. The method achieves 100% accuracy in detecting epileptic seizures. Additionally, sensitivity analysis has been conducted to enhance the success of Discrete Wavelet Transform and Wavelet Packet Analysis methods and to identify significant features.

Identifying the Bridge Natural Frequency Pattern Under Operational Condition

This study investigates variations in the Humber Bridge’s natural frequency and presents waveforms of individual vibration modes using acceleration response data collected on February 1, 2012. Three synchrosqueezed transform analysis methods were primarily employed to process the numerical multi-component and closely-spaced signal to identify the most suitable techniques for real-world applications. The improved multisynchrosqueezing transform and Fourier synchrosuqeezed transform were chosen for analyzing the bridge’s monitored vertical acceleration at Humber. The time-frequency representations and instantaneous frequencies (IFs) generated by these methods are well-aligned, revealing less than a 5% change in the bridge’s vertical natural frequency, with mode 1 exhibiting an 11% variation. The monitored vertical acceleration was then reconstructed, and the results demonstrated the high accuracy of the recovered data, with an L-Infinite norm of 0.0013. Finally, the reconstructed waveforms of each mode, along with the corresponding IFs and spectra, provided a convenient means for further insight into the acceleration data. This study presents new findings, such as the waveform of each vibration mode from the monitored acceleration of the bridge, previously unreported in bridge engineering, and validates the feasibility of cutting-edge synchrosqueezed transform methods for accurately tracking the bridge’s frequency variations under operational conditions. It lays a foundation for further analysis of the bridge’s monitored signals.

Algorithm study of three-dimensional light gauge steel structures thermal–mechanical coupling based on improved static-dynamic transformation analysis

Non-linearity and low-computational efficiency make the thermal–mechanical coupling simulation of the overall structure difficult to achieve with a single static or dynamic analysis. This manuscript combined the efficiency of implicit static analysis and explicit dynamic analysis for calculating large deformations, and realized the transformation of static and dynamic analysis in ABAQUS software. Then, the transformation of static and dynamic analysis applied to the thermal–mechanical coupling numerical simulation of the overall structure. For structural forms with numerous members, significant non-linearity and complex connection contacts, an improved static-dynamic transformation analysis (ISDTA) method was proposed. In the ISDTA method, the criteria for determining and removing members that reach the fire resistance temperature were set to improve the efficiency and convergence of the calculation. A three-dimensional light gauge steel structure calculation model was established. Then, based on the proposed ISDTA method, numerical simulation of thermal–mechanical coupling was carried out to compare the failure model, member deformation and fire resistance temperature of the experimental and numerical simulation models. It was concluded that the proposed ISDTA method could simulate the actual failure model of light gauge steel structures effectively and predict the moment of structural collapse accurately, with an error of 4.1% between the calculated and measured fire resistance times. The method could apply to the thermal–mechanical numerical simulation of the overall structural process from small deformation to collapse in a structural system characterised by “large number of components, significant non-linearity, and complex connection contacts”.

Atomic structure of intermetallic compound Nb5Si3 by new cluster transformation analysis method

The structure of Nb5Si3 at the atomic level is fundamental for identifying its complicated structure in atomic simulations and for further understanding the phase selection behaviors during the solidification of Nb-Si alloys. In this study, the structure of Nb5Si3 was investigated using deep-learning molecular dynamic simulations. The ideal βNb5Si3 is characterized by Nb-centered Voronoi polyhedrons (VPs) <0,0,12,3>, <0,0,12,2>, and Si-centered VPs <0,2,8,2>, <0,2,8,0>. Most initial VPs are distorted at high temperatures due to intense thermal perturbation. A new cluster transformation analysis (CTA) method was proposed to evaluate the stability of ideal VPs against perturbation and predict the possible transformations of the initial VPs in atomic simulations. Most transformations of the initial VPs in βNb5Si3 originate from distortions at the edges of the Nb-centered VPs and the faces/vertices of the Si-centered VPs. The distorted VPs in βNb5Si3 at high temperatures are dominated by <0,1,10,4>, <0,1,10,5>, <0,2,8,1> and <1,2,5,3> VPs, which are predicted as the primary transformations by the CTA.

Shock Train Leading Edge Detection Method under the Condition of Steady Back Pressure and Fluctuating Back Pressure

In this paper, the shock train in the isolator is taken as the research object, and the method of detecting the leading edge position of the shock train during self-excited oscillation and forced oscillation is studied by conducting the wind tunnel test. A steady-state throttling system and a fluctuating throttling system are designed to apply the required steady-state and fluctuating back pressure downstream of the isolator. Then, the four methods of pressure ratio method, pressure difference method, standard deviation method, and Fast Fourier Transform (FFT) analysis method are used to detect and study the position of the shock train leading edge under steady and fluctuating back pressure to verify their applicability. The results show that under the condition of steady-state back pressure, the four methods can get effective results, and the results are consistent. Under the condition of fluctuating back pressure, the pressure difference method could not give effective results, but the other three methods could give effective and consistent results. And the results of the standard deviation method and the Fast Fourier Transform analysis method are similar in image and principle.

Microseismic Dynamic Response and Multi-Source Warning during Rockburst Monitoring Based on Weight Decision Analysis

To prevent rockburst disasters and improve the accuracy of warnings for rockburst, based on the microseismic data of the 1366 working face of Hengda Coal Mine collected by the microseismic monitoring system, Fourier transform, wavelet packet transform, and Hilbert-Huang transform analysis methods are used for time-frequency domain joint analysis. The time-frequency differences of the main frequency, amplitude, frequency band percentage, and instantaneous energy of the high-energy microseismic event and the events before high-energy microseismic event are obtained. The analysis shows that the high-energy event has obvious low frequency characteristics, and when the high-energy event occurs, the instantaneous energy shows an obvious "inverted V" trend. At the same time, it is found that the acoustoelectric indexes show a trend of "rising" or "inverted V" when the high-energy event occurs. On this basis, the unascertained measure comprehensive evaluation model of rock burst hazard is established by analytic hierarchy process (AHP). Based on the analysis of microseismic data and the acoustoelectric index of the 1366 working face in Hengda coal mine, it is of great significance to determine the warning indicators for rockburst, improve the accuracy of uncertainty quantitative analysis for rockburst, and improve the discrimination accuracy of rockburst risk.

The influence of infragravity waves, wind, and basin resonance on vessel movements and related downtime at the Outer Port of Punta Langosteira, Spain

The coupling of low-frequency oscillatory modes of a harbor with horizontal motions of moored ships can amplify the latter, causing operational and safety problems. The objectives of this study were to analyze infragravity waves and their effect on moored ship motions; to validate the Wavelet Transform Analysis (WTA) method for studying port-ship resonance in the Outer Port of Punta Langosteira, Spain; and to study the climatic forcing of downtimes. The excited resonant modes of the basin (102 and 132 s) were determined using a numerical model and high-frequency water level measurements at different points of the harbor. Yaw and sway motions of three bulk carriers were analyzed in the frequency-time domain using WTA. The results showed that the oscillation periods of the studied vessel motions are directly related to the water depth to vessel draft ratio. In addition, when the wind speeds exceeds 20 km/h, the ship moves away from the fenders, which lead to a lengthening of these periods. Based on our findings, yaw may induce oscillations of the same period in sway. Additionally, operational thresholds were proposed based on outer and inner waves data that will lead to optimize ship stays, avoiding downtimes, and to advance the safety of loading and unloading operations.

Experiment Analysis of Drilling Feedback Signal from Simulation of Roadway Roof

To simulate the condition of unstable rock formation in the stable rock formation of the roadway roof, the complete and broken rock formations on the roadway roof were simulated by superimposing and combining complete specimens and broken specimens, hence providing an appropriate way to obtain the drilling vibration signals to find regular patterns which can be used to judge the state of the rock layers. The Fourier transform analysis method and the wavelet transform analysis method are, respectively, used for the vibration signals to extract the eigenvalues of the vibration signals that are quite different in the complete layer and broken layers. Effective values, kurtosis factor, pulse factors, and gravity amplitude were used to construct the eigenvectors, which were most suitable as the basis for judging the integrity of rock formation after analysis, providing an effective way to deal with drilling signal. The wavelet threshold denoising could adaptively reduce the noise of the signals, and the unbiased likelihood estimation threshold rule had the best denoising effect. Well, through the extraction and comparison of the wavelet feature-scale entropy values, it is concluded that the wavelet analysis can be used as a reference rather than a decisive factor to determine the complete and broken layers. And more research is needed to determine the selection method of wavelet.

Multiple transformation analysis for interference separation in TDCS

Various types of interference signals limit the practical application of transform domain communication systems (TDCSs) in the severe electromagnetic field, an orthogonal basis learning method of transformation analysis (OBL-TA) is proposed to effectively address the problem of obtaining an optimal transform domain based on sparse representation. Then, the sparse availability is utilized to obtain the optimal transformation analysis by the iterative methods, which yields the sparse representation for transform domain (SRTD) in unrestricted form. In addition, the iterative version of SRTD (I-SRTD) in unrestricted form is obtained by decomposing the SRTD problem into three sub-problems and each sub-problem is iteratively solved by learning the best orthogonal basis. Furthermore, orthogonal basis learning via cost function minimization process is conducted by stochastic descent, which is assured to converge to a local minimum at least. Finally, the optimal transformation analysis is developed by the effectiveness of different transform domains according to the accuracy of the sparse representation and an optimal transformation analysis separately (OPTAS) is applied to the synthesized signal forms with conic alternatives, dualization, and smoothing. Simulation results demonstrate that the superiorities of the proposed methods achieve the optimal recovery and separation more rapidly and accurately than conventional methods.

New Analytical Method and Application for Solve the Nonlinear Wave Equation

:The proposed analytical approach is a sophisticated grouping of Elzaki Transform and Homotopy Analysis Method (HAM), called Homotopy Analysis Elzaki Transform (HAET) method, for solving Non-linear Wave System which is combination of two wave equations (Telegraph and Klein Gordon (K-G) Type Equations). This study will be exemplifying to well-becoming character of HAET method. The obtained solution is graphically sketched.

Coronary Artery Magnetic Resonance Angiography Combined with Computed Tomography Angiography in Diagnosis of Coronary Heart Disease by Reconstruction Algorithm.

This research aimed at discussing the diagnosis effect of coronary artery magnetic resonance angiography (MRA) combined with computed tomography (CT) angiography (CTA) based on the back-projection filter reconstruction (BPFR) algorithm in coronary heart disease (CHD), and its role in the diagnosis of coronary artery disease (CAD). Sixty patients with CHD were selected and randomly rolled into group A (undergone MRA examination), group B (undergone CTA examination), and group C (undergone MRA + CTA), with 20 cases in each group. Taking the diagnostic results of coronary angiography as the gold standard, the MRA and CTA images were reconstructed using a BPFR algorithm, and a filter function was added to solve the problem of image sharpness. In addition, the iterative reconstruction algorithm and the Fourier transform analysis method were introduced. As a result, the image clarity and resolution obtained by the BPFR algorithm were better than those obtained by the Fourier transform analytical method and the iterative reconstruction algorithm. The accuracy of group C for the diagnosis of mild coronary stenosis, moderate stenosis, and severe stenosis was 94.02%, 96.13%, and 98.01%, respectively, which was significantly higher than that of group B (87.5%, 90.2%, and 88.4%) and group C (83.4%, 89.1%, and 91.5%) (P < 0.05). The sensitivity and specificity for the diagnosis of noncalcified plaque in group C were 87.9% and 89.2%, respectively, and the sensitivity and specificity for the diagnosis of calcified plaque were 84.5% and 78.4%, respectively, which were significantly higher than those in groups B and C (P < 0.05). In summary, the BPFR algorithm had good denoising and artifact removal effects on coronary MRA and CTA images. The combined detection of reconstructed MRA and CTA images had a high diagnostic value for CHD.

Computed Tomography Image under Optimized Iterative Reconstruction Algorithm to Analyze the Characteristics of Blood Flow Field in Cerebral Aneurysm before and after Stent Implantation

In order to analyze the change characteristics of blood flow field in cerebral aneurysms before and after stent implantation, this study first constructed an optimized iterative reconstruction algorithm to reconstruct CT images of patients with cerebral aneurysms and used it to solve the problem of image sharpness. In addition, backprojection image reconstruction algorithm and Fourier transform analytic method were introduced. According to the CT images of cerebral arteries of patients, the lesions were presented in a three-dimensional and visual way through the reconstructed three-dimensional images, thus achieving the effects of simulation and simulation. The results showed that the sensitivity, specificity, and accuracy of the optimized iterative reconstruction algorithm were 90.78%, 83.27%, and 94.82%, which were significantly higher than those of the backprojection image reconstruction algorithm and Fourier transform analysis method, and the difference was statistically significant ( P &lt; 0.05 ). Before operation, the blood flow velocity in the neck of aneurysm was 7.35 × 10−2 m/s, the exit velocity was 1.51 × 10−1 m/s, and the maximum velocity appeared in the upstream part of the exit. After passing through the aneurysm, the blood flow velocity began to decrease gradually, forming a vortex at the top of the tumor. After stent implantation, the neck and outlet velocities of cerebral aneurysm were 9.352 × 10−2 m/s and 1.897 × 10−2 m/s, respectively. The velocity of blood flow decreased after entering the aneurysm, and there was no vortex at the top of the aneurysm. Among the outlet velocities of arterial blood vessels, the velocity before stent implantation was significantly lower than that after stent implantation, and the difference was statistically significant ( P &lt; 0.05 ). Compared with prestent, the shear force distribution on the wall of cerebral aneurysm showed a significant decrease, and the difference was statistically significant ( P &lt; 0.05 ). To sum up, pelvic floor ultrasound based on hybrid iterative reconstruction algorithm has high accuracy in diagnosing the changes of blood flow field in cerebral aneurysms. The application of CT images in the diagnosis of cerebral aneurysms can objectively provide imaging data for clinical practice and has high application value.