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- 摘要
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- 实验方案
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Analysis of the topographic roughness of the Moon using the Wavelet Leaders Method and the Lunar Digital Elevation Model from the Lunar Orbiter Laser Altimeter and SELENE Terrain Camera
摘要： The Wavelet Leaders Method (WLM) is a wavelet-based multifractal formalism that allows the identification of scale breaks (thus scaling regimes), the definition of scaling properties (mono versus multi fractality of the surface) and the calculation of the H?lder exponent that characterizes each pixel, based on the comparison between a theoretical wavelet and topographic values. Here we use the WLM and the SLDEM2015 digital elevation model to provide a near-global and a local isotropic characterization of the lunar roughness. The near-global study of baselines between 330 m and 1,350 km reveals scale breaks at ~1.3, 42.2 and 337.6 km. Scaling properties and H?lder exponent values were calculated for the three corresponding scaling regimes: 330–659 m, 1.3–21.1 km, and 42.2–168.8 km. We find that the dichotomy between the highlands and the maria is present at all scales. Between 330–659 m, the H?lder exponent map shows the unique signature of Orientale basin, rilles and a correlation with the age of mare units. Between 1.3–21.1 km, it shows the unique signature of the Orientale basin and a relationship with the density of 5-20 km diameter craters. Scaling properties and H?lder exponent values were also calculated locally for complex craters, basins, rilles and light plains, for two scaling regimes: 165–659 m, 1.3–21.1 km. Relationships between the H?lder exponent values at 165–659 m , the density of <500 m diameter craters and different geologic units were found and a potential scale break near 165 m was identified.
关键词： scaling regimes,Wavelet Leaders Method,SLDEM2015,lunar roughness,H?lder exponent
更新于2025-09-16 10:30:52
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Laser Doppler Signal Denoising Based on Wavelet Packet Thresholding Method
摘要： In laser Doppler velocimeter (LDV), calculation precision of Doppler shift is affected by noise contained in Doppler signal. In order to restrain the noise interference and improve the precision of signal processing, wavelet packet threshold denoising methods are proposed. Based on the analysis of Doppler signal, appropriate threshold function and decomposition layer number are selected. Heursure, sqtwolog, rigrsure, and minimaxi rules are adopted to get the thresholds. Processing results indicate that signal-to-noise ratio (SNR) and root mean square error (RMSE) of simulated signals with original SNR of 0 dB, 5 dB, and 10 dB in both low- and high-frequency ranges are significantly improved by wavelet packet threshold denoising. A double-beam and double-scattering LDV system is built in our laboratory. For measured signals obtained from the experimental system, the minimum relative error of denoised signal is only 0.079% (using minimaxi rule). The denoised waveforms of simulated and experimental signals are much more smooth and clear than that of original signals. Generally speaking, denoising effects of minimaxi and saqtwolog rules are better than those of heursure and rigrsure rules. As shown in the processing and analysis of simulated and experimental signals, denoising methods based on wavelet packet threshold have ability to depress the noise in laser Doppler signal and improve the precision of signal processing. Owing to its effectiveness and practicability, wavelet packet threshold denoising is a practical method for LDV signal processing.
关键词： wavelet packet threshold denoising,laser Doppler velocimeter,signal-to-noise ratio,double-beam and double-scattering LDV system,root mean square error
更新于2025-09-16 10:30:52
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400 Gb/s Silicon Photonic Transmitter and Routing WDM technologies for glueless 8-socket Chip-to-Chip interconnects
摘要： A novel frequency-based classification framework and new wavelet algorithm (Wave-CLASS) is proposed using an overcomplete decomposition procedure. This approach omits the downsampling procedure and produces four-texture information with the same dimension of the original image or window at infinite scale. Three image subsets of QuickBird data (i.e., park, commercial, and rural) over a central region in the city of Phoenix were used to examine the effectiveness of the new wavelet overcomplete algorithm in comparison with a widely used classical approach (i.e., maximum likelihood). While the maximum-likelihood classifier produced < 78.29% overall accuracies for all three image subsets, the Wave-CLASS algorithm achieved high overall accuracies—95.05% for the commercial subset (Kappa = 0.94), 93.71% for the park subset (Kappa = 0.93), and 89.33% for the rural subset (Kappa = 0.86). Results from this study demonstrate that the proposed method is effective in identifying detailed urban land cover types in high spatial resolution data.
关键词： overcomplete decomposition,high spatial resolution,infinite scale,urban land cover,Classification,wavelet transforms
更新于2025-09-16 10:30:52
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[IEEE 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) - Delhi, India (2018.10.22-2018.10.24)] 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) - Condition Monitoring of Photovoltaic Systems Using Machine Leaming Techniques
摘要： Condition monitoring of any system is essential to maintain its healthy operation as it results in getting maximum revenue with minimum maintenance and operation costs. The main objective of this paper is to develop a fault detection algorithm capable of classifying different faults that can be occur in a Photovoltaic (PV) systems. Output characteristics of the PV system are used as valuable information to observe various types of faults and their locations. Wavelet transforms and neural network systems were adapted to filter the non-significant anomalies and make it easier to detect faults that are to be taken care of in a timely manner. The neural network (NN) classification adapts Multilayer perceptron (MLP) to identify the type and location of occurring faults. Wavelet transform (WT) based signal processing technique is utilized in the feature extraction process to provide inputs to the NN. The developed detection algorithm is adapted for 24/7 automated surveillance. The developed algorithm achieved 98.2% accuracy when tested on a predetermined fault data set.
关键词： Neural Network,Fault Detection,Wavelet Transform,Machine Learning,Photovoltaic Systems
更新于2025-09-12 10:27:22
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EMD- PNN based welding defects detection using laser-induced plasma electrical signals
摘要： The plasma electrical signal has gained extensive attention for characterizing the behavior of the laser-induced plasma due to the advantages of easy acquisition and feedback control. In this paper, the electrical signals were measured by a passive probe based on the principle of plasma sheath effect. To explore the mutation characteristics of plasma electrical signals during defect generation in laser deep penetration welding, wavelet packet transform (WPT) and empirical mode decomposition (EMD) were used to compress data and extract features, respectively. Based on the analysis of the time-frequency spectrum of a typical plasma electrical signal, the approximate coefficients of 0?390 Hz frequency range were reconstructed. The residual term which characterizes the change trend of electrical signal was obtained by the further adaptive decomposition. For better identifying weld defects, another two statistical features, mean value and standard deviation, were extracted by carrying out statistical analysis in the time domain. The feature database is built with above features and used as inputs of the predictive model based on the probabilistic neural network (PNN). The result showed the average prediction accuracy was as high as 90.16% when recognizing five statuses of weld seam, including sound weld and four kinds of weld defects.
关键词： Wavelet packet transformation,Empirical mode decomposition,Laser welding,Plasma electrical signal,Probabilistic neural network
更新于2025-09-12 10:27:22
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Photovoltaic active power control based on BESS smoothing
摘要： The power fluctuation of photovoltaic (PV) is harmful to power systems, so the battery energy storage system (BESS) was applied to smooth power fluctuation in PV. At present, the main ways to get configuration of BESS are low-pass filter and spectrum compensation, which have some drawbacks. In this paper, a method that combines empirical mode decomposition (EMD) with wavelet analysis (WA) is proposed to get grid-connected active power expectation of PV properly. Based on simulation of PV output, the minimum sizing of BESS is determined by different batteries’ state-of-charging (SOC) and efficiency. Comparing traditional low-pass filter and spectrum compensation, this method not only acquires the capacity of BESS accurately, but also improves the effect to smoothing power fluctuation of PV effectively. Finally, a case is proposed to verify correctness of the theory.
关键词： Sizing energy storage capacity,Empirical mode decomposition (EMD),Wavelet analysis,Photovoltaic output
更新于2025-09-12 10:27:22
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Effective Defect Features Extraction for Laser Ultrasonic Signal Processing by Using Time–Frequency Analysis
摘要： The time-frequency analysis (TFA) by wavelet transform is adopted for the laser ultrasonic signal processing, and the effective features extraction of the material defect is obtained. The TFA is adopted here to analyze the laser-generated surface acoustic wave (SAW) signal which contains the defect features, the echo wave features are extracted signi?cantly, especially under the condition of low signal-to-noise rate (SNR). The simulation model by using ?nite element method (FEM) is set up in an aluminum plate with different surface defect depths in detail, and the defect depths prediction with TFA is also considered. It shows that, without extra denoising process, the echo SAW is extracted signi?cantly in case of defect depths ranging from 0.1mm to 0.9mm at SNR of ?3dB by TFA. The TFA for processing the laser ultrasonic signal provides a promising way to get the defect information, with the accuracy increased by 7.9dB in this work, which is extremely meaningful for the ultrasonic signal processing and material evaluation.
关键词： Time-frequency analysis,feature extraction,wavelet transform,signal-to-noise rate,laser ultrasonic signal
更新于2025-09-11 14:15:04
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Quantitative diagnosis method of beam defects based on laser Doppler non-contact random vibration measurement
摘要： The beam structure is prone to defect damage during its use, and the rapid quantitative diagnosis of the beam structure can detect the defects of the beam in real time and quantitatively. In this article, the method of obtaining the vibration time-domain signal under random excitation of beam structure is proposed by using random vibration excitation and Laser Doppler principle. Based on this, the defect quantitative identification algorithm of beam structure is proposed based on fast Fourier, continuous wavelet transform and convolutional neural network. The random vibration of different parts of steel beams with artificial defects is measured by Laser Doppler method. The experimental results show that the defect size of the beam structure can be effectively identified only by the random vibration signal of the finite point. The method is expected to help to develop an online real-time assessment instrument for beam structure defects in service state.
关键词： Continuous wavelet transform,Beam defects,Laser Doppler,Non-contact random excitation,Quantitative diagnosis,Convolutional neural network
更新于2025-09-11 14:15:04
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Different lasers reveal different skin microcirculatory flowmotion - data from the wavelet transform analysis of human hindlimb perfusion
摘要： Laser Doppler flowmetry (LDF) and reflection photoplethysmography (PPG) are standard technologies to access microcirculatory function in vivo. However, different light frequencies mean different interaction with tissues, such that LDF and PPG flowmotion curves might have distinct meanings, particularly during adaptative (homeostatic) processes. Therefore, we analyzed LDF and PPG perfusion signals obtained in response to opposite challenges. Young healthy volunteers, both sexes, were assigned to Group 1 (n = 29), submitted to a normalized Swedish massage procedure in one lower limb, increasing perfusion, or Group 2 (n = 14), submitted to a hyperoxia challenge test, decreasing perfusion. LDF (Periflux 5000) and PPG (PLUX-Biosignals) green light sensors applied distally on both lower limbs recorded perfusion changes for each experimental protocol. Both techniques detected the perfusion increase with massage, and the perfusion decrease with hyperoxia, in both limbs. Further analysis with the wavelet transform (WT) revealed better depth-related discriminative ability for PPG (more superficial, less blood sampling) compared with LDF in both challenges. Spectral amplitude profiles consistently demonstrated better sensitivity for LDF, especially regarding the lowest frequency components. Strong correlations between components were not found. Therefore, LDF and PPG flowmotion curves are not equivalent, a relevant finding to better study microcirculatory physiology.
关键词： Laser Doppler flowmetry,perfusion,photoplethysmography,wavelet transform,microcirculatory function
更新于2025-09-11 14:15:04
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Analysis of velocity calculation methods of laser-induced surface acoustic wave
摘要： The key to measuring residual stress by surface acoustic wave method is the accurate measurement of velocity. In this paper, the velocity of laser-induced broadband surface acoustic wave is studied, and three velocity calculation methods of surface acoustic wave, time domain method, phase method and wavelet method are compared. The calculation error of the time domain method under the condition of dispersion is analyzed. A recursive method for calculating phase difference is proposed to improve the efficiency of phase method. The simulated surface acoustic waves are used to compare the phase method and wavelet method under the conditions of attenuation and dispersion. Compared with the wavelet method, the phase method cannot distinguish the time when the frequency band appears, and the velocity calculation of adjacent frequency points is related, while the wavelet method is independent of each other. The wavelet method can improve the calculation accuracy of the velocity curve by interpolating the original data. After interpolation, the trend of curve is more obvious, and the fitting error is greatly reduced.
关键词： Wavelet,Dispersion,Comparative analysis,Surface acoustic wave,Wave velocity
更新于2025-09-11 14:15:04