Scale Decomposition Research Articles

A novel intrinsic time–scale decomposition–graph signal processing–based characteristic extraction method for rotor–stator rubbing of aeroengine

Intrinsic time-scale decomposition and graph signal processing are combined to effectively identify a rotor–stator rubbing fault. The vibration signal is decomposed into mutually independent rotational components, and then, the Laplacian energy index is obtained by the graph signal of the autocorrelation function of rotational components, and the signal is reconstructed by an autocorrelation function of each proper rotation (PR) component relative to smaller Laplacian energy index (less complexity). Finally, characteristics are extracted from rotor–stator rubbing faults in an aeroengine according to square demodulation spectrum of a reconstructed signal. To validate the effectiveness of the algorithm, a comparative analysis is made among traditional intrinsic time-scale decomposition algorithm, combination of intrinsic time-scale decomposition and autocorrelation function, and the proposed intrinsic time-scale decomposition–graph signal processing algorithm. Comparative result shows that the proposed intrinsic time-scale decomposition–graph signal processing algorithm is more precise and effective than the traditional intrinsic time-scale decomposition and intrinsic time-scale decomposition and autocorrelation function algorithms in extracting characteristic frequency and frequency multiplication of rotor–stator rubbing faults and can greatly reduce the number of noise components irrelevant to faults.

Analysis of scale-specific factors controlling soil erodibility in southeastern China using multivariate empirical mode decomposition

Soil erodibility (K) is a key factor in predicting the erosion of soil by water. K is spatially influenced by many environmental factors, but studies of the scales of factors influencing K are rare. The objective of this study was to identify the scale-dependent relationships between K and various environmental factors using multivariate empirical mode decomposition (MEMD). K and nine environmental factors were investigated at 101 locations along a 4200-km sinuous transect in southeastern China and then they were decomposed and analyzed using MEMD. Mean K was 0.043 ± 0.008 t ha h MJ−1 mm−1 ha−1 and had a weakly moderate spatial variability. Six intrinsic mode functions (IMFs) and a residue were obtained for K and the environmental factors after decomposition. The mean scales for IMF1 to IMF6 were 123.4, 192.4, 347.0, 598.9, 1268.3 and 1559.2 km, respectively. IMF1 for each variable explained most of the spatial variability for the variable, and IMF1 and IMF2 or IMF1 and IMF3 explained half of the spatial variability of the variable. K was mainly associated with soil-particle composition and soil organic-matter content at small scales (IMF1-IMF2), with soil-particle composition and pH at moderate scales (IMF3-IMF4) and with elevation, saturated hydraulic conductivity and clay content at large scales (IMF5-IMF6). Modeled prediction functions of K at each scale of decomposition were accurate, but the total prediction accuracy at the sampling scale was slightly lower than for multiple linear regression. The MEMD method was nevertheless valuable and provided detailed information about the factors influencing K at different spatial scales.

Robust singular perturbation control for 3D path following of underactuated AUVs

This paper presents a novel control scheme for the three-dimensional (3D) path following of underactuated Autonomous Underwater Vehicle (AUVs) subject to unknown internal and external disturbances, in term of the time scale decomposition method. As illustration, two-time scale motions are first artificially forced into the closed-loop control system, by appropriately selecting the control gain of the integrator. Using the singular perturbation theory, the integrator is considered as a fast dynamical control law that designed to shape the space configuration of fast variable. And then the stabilizing controller is designed in the reduced model independently, based on the time scale decomposition method, leading to a relatively simple control law. The stability of the resultant closed-loop system is demonstrated by constructing a composite Lyapunov function. Finally, simulation results are provided to prove the efficacy of the proposed controller for path following of underactuated AUVs under internal and external disturbances.

Financial Time Series Image Algorithm Based on Wavelet Analysis and Data Fusion

In recent years, people are more and more interested in time series modeling and its application in prediction. This paper mainly discusses a financial time series image algorithm based on wavelet analysis and data fusion. In this research, we conducted an in‐depth study on the scale decomposition sequence and wavelet transform sequence in different scale domains of wavelet transform according to the scale change rule based on wavelet transform. We use wavelet neural network with different input neurons and hidden neurons to predict, respectively. Finally, the prediction results are integrated into the final prediction results based on the original time series by using wavelet reconstruction technology. Using RBF algorithm in neural network and SPSS Clementine, the wavelet transform sequences on five scales are modeled. Each network model has three layers: one input layer, one hidden layer, and one output layer, and each output layer has only one output element. In order to compare the prediction effect of the model proposed in this study, the ordinary RBF network is used to model and predict the log yield itself. When the input sample is 5, the minimum mean square error is obtained when the hidden layer is 6, and the mean square error is 1.6349. The mean square error of the training phase is 0.0209, and the validation error is 1.6141. The results show that the prediction results of the wavelet prediction method combined with the RBF network prediction method are better than those of wavelet prediction or RBF network prediction.

结合滤波算子与双尺度分解的多聚焦图像融合

结合滤波算子与双尺度分解的多聚焦图像融合

Design of Sanda Action Reconstruction Model Based on 3D Images

In order to improve the training level of Sanda movement, this article uses an image analysis method to reconstruct the detailed characteristics of the movement and apply them to the actual training process. Since the traditional wavelet reconstruction method is affected by the accuracy of the decomposition scale, this paper proposes an improved method of Sanda action based on 3D image reconstruction. First, the method relies on frame adjacent phase compensation and digital image stabilization techniques to perform digital frame operations on the image. Then, scanning and corner detection are used for image reconstruction, where adjacent phase compensation methods are used to match feature points and gray pixels. Image extraction is performed by extracting key feature points of the action 3D image, and a fast frame detection method is used to stabilize the image of the digital image, thereby improving the image quality of image reconstruction. The experimental results show that the method has good image output effect and has a high application value in Sanda guidance and optimization.

Predicting bulk density in deep unsaturated soils based on multiple scale decomposition

Information on bulk density (BD) in the unsaturated zone (USZ) is critical to the development of Earth’s critical zone science. However, models for predicting deep soil BD are limited due to poor understanding of the variability of deep soil BD. This study aimed to examine the dominant scales of variations in BD, identifying the scale-specific controls of BD and developing a new multi-scale physically based conceptual model for predicting BD in the deep USZ. Three undisturbed soil cores (90.0, 47.6, and 59.2 m in depth) were collected from the Changwu, Haiyuan, and Weinan regions within the Chinese Loess Plateau and a range of soil properties were measured at 0.2 m intervals. Scale specific variations were identified using empirical mode decomposition and the multi-scale model was compared against traditional pedo-transfer functions. The USZ BD distribution with depth exhibited two types of dominant variations: (a) large-scale logarithmic growth, due mainly to compaction and (b) medium-scale oscillations from variation of soil inherent properties. A multi-scale model accounted for 82% of the total variation in BD, from soil depth, magnetic susceptibility, and clay content. Compared with conventional pedo-transfer functions at the measurement scale, the multi-scale model improved prediction (adjusted R2) of BD by around 15% and provided an interpretable and rational form. This study provided new insights and a model to predict USZ BD, which are valuable for improving our understanding of the deep USZ.

Ultra-High Frequency Partial Discharge Signal Recognition in GIS based on Fisher Linear Discriminant Theory

In this paper, dual tree complex wavelet transform (DT-CWT) is used to decompose UHF partial discharge (PD) signal in multi-scale. The optimal decomposition level of complex wavelet is solved. The wavelet energy features of real part and imaginary part of UHF-PD signal under the optimal decomposition scale are extracted. Fisher linear discriminant method is used to select the features of energy features and to identify the PD types. The results show that the optimized real part and imaginary part high frequency wavelet energy features can effectively identify four kinds of typical insulation defects, and the recognition rate can reach to 92.5%. Meanwhile, the optimal complex wavelet energy (OCWEF) feature has better sensitivity and recognition effect in PD type identification.

A Wavelet Transform-Assisted Convolutional Neural Network Multi-Model Framework for Monitoring Large-Scale Fluorochemical Engineering Processes

The barely satisfactory monitoring situation of the hypertoxic fluorochemical engineering processes requires the application of advanced strategies. In order to deal with the non-linear mechanism of the processes and the highly complicated correlation among variables, a wavelet transform-assisted convolutional neural network (CNN) based multi-model dynamic monitoring method was proposed. A preliminary CNN model was first trained to detect faults and to diagnose part of them with minimum computational burden and time delay. Then, a wavelet assisted secondary CNN model was trained to diagnose the remaining faults with the highest possible accuracy. In this step, benefitting from the scale decomposition capabilities of the wavelet transform function, the inherent noise and redundant information could be filtered out and the useful signal was transformed into a higher compact space. In this space, a well-designed secondary CNN model was trained to further improve the fault diagnosis performance. The application on a refrigerant-producing process located in East China showed that not only regular faults but also hard to diagnose faults were successfully detected and diagnosed. More importantly, the unique online queue assembly updating strategy proposed remarkably reduced the inherent time delay of the deep-learning methods. Additionally, the application of it on the widely used Tennessee Eastman process benchmark strongly proved the superiority of it in fault detection and diagnosis over other deep-learning methods.

Extreme Low-Temperature Combustion Chemistry: Ozone-Initiated Oxidation of Methyl Hexanoate.

The accelerating chemical effect of ozone addition on the oxidation chemistry of methyl hexanoate [CH3(CH2)4C(═O)OCH3] was investigated over a temperature range from 460 to 940 K. Using an externally heated jet-stirred reactor at p = 700 Torr (residence time τ = 1.3 s, stoichiometry φ = 0.5, 80% argon dilution), we explored the relevant chemical pathways by employing molecular-beam mass spectrometry with electron and single-photon ionization to trace the temperature dependencies of key intermediates, including many hydroperoxides. In the absence of ozone, reactivity is observed in the so-called low-temperature chemistry (LTC) regime between 550 and 700 K, which is governed by hydroperoxides formed from sequential O2 addition and isomerization reactions. At temperatures above 700 K, we observed the negative temperature coefficient (NTC) regime, in which the reactivity decreases with increasing temperatures, until near 800 K, where the reactivity increases again. Upon addition of ozone (1000 ppm), the overall reactivity of the system is dramatically changed due to the time scale of ozone decomposition in comparison to fuel oxidation time scales of the mixtures at different temperatures. While the LTC regime seems to be only slightly affected by the addition of ozone with respect to the identity and quantity of the observed intermediates, we observed an increased reactivity in the intermediate NTC temperature range. Furthermore, we observed experimental evidence for an additional oxidation regime in the range near 500 K, herein referred to as the extreme low-temperature chemistry (ELTC) regime. Experimental evidence and theoretical rate constant calculations indicate that this ELTC regime is likely to be initiated by H abstraction from methyl hexanoate via O atoms, which originate from thermal O3 decomposition. The theoretical calculations show that the rate constants for methyl ester initiation via abstraction by O atoms increase dramatically with the size of the methyl ester, suggesting that ELTC is likely not important for the smaller methyl esters. Experimental evidence is provided indicating that, similar to the LTC regime, the chemistry in the ELTC regime is dominated by hydroperoxide chemistry. However, mass spectra recorded at various reactor temperatures and at different photon energies provide experimental evidence of some differences in chemical species between the ELTC and the LTC temperature ranges.

Turbulent pipe flow response to a step change in surface roughness

Abstract

Failure Analysis of Coke Dry Quenching Car Liners in Coke Plant

Coke dry quenching (CDQ) bucket is used to carry hot coke to CDQ chamber for cooling. The liners provided inside the bucket were getting buckled and cracked after 6–7 months of service. The thermography was carried out, and results showed that maximum temperature inside bucket is 600–650 °C. Chemical analysis revealed the failed liners were made of 600-QT spheroidized cast iron. Metallographic study showed decarburization, nodule decomposition, and thick scale in the hot(inner) side of liner plate. Pearlite graphite nodules decompose to cementite and ferrite, and hardness taken on the cementite globules (280 HV) confirms decomposition. Type IV and V graphites were observed in the heat affected zone, whereas in cold side of the liner plate VI graphite nodules were observed. Hardness variation was observed in the cracked sample along the thickness, and it showed lower hardness at hot surface due to ferritic structure, but at cold zone specified higher hardness was achieved. This nodules bursting, decarburization, and scale formation indicated microstructural degradation due to prolong use at high temperature (above 600 °C) in S.G iron. The microstructural deterioration resulted in lower hardness and decreased strength causing buckling and cracking. To enhance the service life of liner, austenitic heat-resistant casting steel with added Ni & Mo was recommended and it enhanced the service life by at least 150%.

Is hillslope-based catchment decomposition approach superior to hydrologic response unit (HRU) for stream-aquifer interaction modelling: Inference from two process-based coupled models

To quantify the surface water-groundwater (SW-GW) exchange flux in a river basin, the inclusion of SW-GW interaction (SGI) process in the next generation earth system models is of growing concern among the hydrologic community that requires a trade-off among model complexity linking streamflow and groundwater flow domains, spatial heterogeneity, and computational demand. Although semi-distributed SGI models seem to be a plausible solution, the question is ‘what could be the effective way to decompose a river basin to assess the sub-surface flux’? Therefore, this study intends to search for a better alternative approach for catchment decomposition between the hillslope- and hydrologic response unit (HRU)-based SGI models; the inherent relationship between the decomposition level and model efficiency; and the appropriate spatial scale of decomposition for hillslopes and HRUs. The typical hydrological models chosen for this study are: the hillslope-based coupled Variable Parameter McCarthy-Muskingum–enhanced hillslope storage Boussinesq (VPMM-ehsB), and the HRU-based coupled Soil and Water Assessment Tool–MODular three-dimensional groundwater FLOW (SWAT-MODFLOW). These semi-distributed models are field-tested to model the streamflow accounting for lateral contributions along the river reach, baseflow, depth to water table, and the SGI flux. The results reveal that the VPMM-ehsB model with hillslope-grid based decomposition outperformed the SWAT-MODFLOW with the HRU-grid based hybrid decomposition in explaining the variance of the entire annual and low (non-monsoon) streamflow time series by 12% and 25%, respectively. The VPMM-ehsB approach has a faster (36%) simulation time than that of the SWAT-MODFLOW showing the potential of the hillslope decomposition approach in the VPMM-ehsB model over the HRU-based approach in the SWAT-MODFLOW for large-scale application in future earth system models.

Multi‐GPU performance optimization of a computational fluid dynamics code using OpenACC

SummaryThis article investigates the multi‐GPU performance of a 3D buoyancy driven cavity solver using MPI and OpenACC directives on multiple platforms. The article shows that decomposing the total problem in different dimensions affects the strong scaling performance significantly for the GPU. Without proper performance optimizations, it is shown that 1D domain decomposition scales poorly on multiple GPUs due to the noncontiguous memory access. The performance using whatever decompositions can be benefited from a series of performance optimizations in the article. Since the buoyancy driven cavity code is communication‐bounded on the clusters examined, a series of optimizations both agnostic and tailored to the platforms are designed to reduce the communication cost and improve memory throughput between hosts and devices efficiently. First, the parallel message packing/unpacking strategy developed for noncontiguous data movement between hosts and devices improves the overall performance by about a factor of 2. Second, transferring different data based on the stencil sizes for different variables further reduces the communication overhead. These two optimizations are general enough to be beneficial to stencil computations having ghost exchanges. Third, GPUDirect is used to improve the communication on clusters which have the hardware and software support for direct communication between GPUs without staging the memory of CPU. Finally, overlapping the communication and computations is shown to be not efficient on multi‐GPUs if only using MPI or MPI+OpenACC. Although we believe our implementation has revealed enough communication and computation overlap, the actual running does not utilize the overlap well due to a lack of enough asynchronous progression.

Impact of land use on Histosols properties in urban agriculture ecosystems of Rio de Janeiro, Brazil

ABSTRACT Histosols provide several ecosystem services, related mainly to their reserves of carbon and nitrogen. Management practices in these soils can increase the mineralization of organic matter and contribute to the emission of greenhouse gases. This study aimed to investigate the effect of tillage with plowing and drainage on Histosol properties in three land use systems located in the municipality of Rio de Janeiro, Brazil. Three areas subjected to different land use systems over the last twenty years were chosen: Area 1, secondary forest with natural regeneration; Area 2, conventional tillage of cassava with plowing; and Area 3, intercropped coconut + cassava with an artificial drainage system. The chemical characterization, von Post scale of organic matter decomposition, percentage of rubbed fiber, organic matter, percentage of mineral material, bulk density, electrical conductivity, soluble phosphate, total organic carbon (TOC) and nitrogen (NT), organic carbon fractions, and C and N stocks were analyzed. Our results showed the critical, nearly irreversible effects of agricultural practices comprising drainage and plowing of the soil. Over twenty years, in Area 2, the TOC and NT values decreased by 33 and 20 %, respectively in the histic horizon, and by about 17 and 8 %, respectively in the gley horizon. In Area 3, the TOC and NT values decreased by 31 and 18 %, respectively, in the histic horizon, and by 27 and 21 % in the gley horizon. Our findings also showed that the loss of C is related to the labile organic carbon, which is more sensitive to environmental changes, even at deeper depths. The plowing of the soil decreases the organic matter content due to the accelerated oxidation of organic matter, increasing the bulk density. Drainage, besides the loss of organic matter by subsidence, promotes the sulfidization of the soil with a high content of SO42-, due to the oxidation of soil materials containing sulfides.

Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization

The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could improve the stability and accuracy of chlorophyll analysis. To develop such a method, we collected the modelling data by conducting field experiments at the tillering, tuber-formation, tuber-bulking, and tuber-maturity stages in 2018. A chlorophyll analysis model was established using the partial least-square (PLS) algorithm based on original reflectance, standard normal variate reflectance, and wavelet features (WFs) under different decomposition scales (21–210, Scales 1–10), which were optimized by the competitive adaptive reweighted sampling (CARS) algorithm. The performances of various models were compared. The WFs under Scale 3 had the strongest correlation with chlorophyll concentration with a correlation coefficient of −0.82. In the model calibration process, the optimal model was the Scale3-CARS-PLS, which was established based on the sensitive WFs under Scale 3 selected by CARS, with the largest coefficient of determination of calibration set (Rc2) of 0.93 and the smallest Rc2−Rcv2 value of 0.14. In the model validation process, the Scale3-CARS-PLS model had the largest coefficient of determination of validation set (Rv2) of 0.85 and the smallest root–mean–square error of cross-validation (RMSEV) value of 2.77 mg/L, demonstrating good prediction capability of chlorophyll concentration. Finally, the analysis performance of the Scale3-CARS-PLS model was measured using the testing data collected in 2020; the R2 and RMSE values were 0.69 and 3.36 mg/L, showing excellent applicability. Therefore, the Scale3-CARS-PLS model could be used to analyze chlorophyll concentration. This study indicated the best decomposition scale of continuous wavelet transform and provided an important support method for chlorophyll analysis in the potato crops.

Wavelet-Based Kalman Smoothing Method for Uncertain Parameters Processing: Applications in Oil Well-Testing Data Denoising and Prediction.

The low-distortion processing of well-testing geological parameters is a key way to provide decision-making support for oil and gas field development. However, the classical processing methods face many problems, such as the stochastic nature of the data, the randomness of initial parameters, poor denoising ability, and the lack of data compression and prediction mechanisms. These problems result in poor real-time predictability of oil operation status and difficulty in offline interpreting the played back data. Given these, we propose a wavelet-based Kalman smoothing method for processing uncertain oil well-testing data. First, we use correlation and reconstruction errors as analysis indicators and determine the optimal combination of decomposition scale and vanishing moments suitable for wavelet analysis of oil data. Second, we build a ground pressure measuring platform and use the pressure gauge equipped with the optimal combination parameters to complete the downhole online wavelet decomposition, filtering, Kalman prediction, and data storage. After the storage data are played back, the optimal Kalman parameters obtained by particle swarm optimization are used to complete the data smoothing for each sample. The experiments compare the signal-to-noise ratio and the root mean square error before and after using different classical processing models. In addition, robustness analysis is added. The proposed method, on the one hand, has the features of decorrelation and compressing data, which provide technical support for real-time uploading of downhole data; on the other hand, it can perform minimal variance unbiased estimates of the data, filter out the interference and noise, reduce the reconstruction error, and make the data have a high resolution and strong robustness.

Image Compression Technology Based on Wavelet Transform

Image compression based on wavelet transform is to apply wavelet algorithm to multi-resolution decomposition of the image, and to achieve image compression by encoding the obtained wavelet coefficients. First, multi-level wavelet decomposition is performed on the image to obtain the corresponding wavelet coefficients. Then, each layer of wavelet coefficients is quantized to obtain quantized coefficient objects. Finally, the quantized coefficient objects are encoded to obtain compression results. This paper uses Haar wavelet as the wavelet base, selects the 2-level decomposition scale, performs wavelet transform, compresses the reconstructed image by setting a threshold, and calculates the file size ratio and PSNR value before and after compression. The wavelet reconstruction function uses the coefficient matrix, dimensionality information and wavelet base type obtained by wavelet decomposition to perform wavelet reconstruction. The global threshold setting method is selected, and the wavelet high-frequency coefficients are threshold filtered to achieve compression before the reconstruction operation, and then the compressed image is obtained through wavelet reconstruction. After the image has undergone wavelet transformation with a specified scale, most of the energy is concentrated in the fractional part of the wavelet decomposition coefficients. The coefficients of other parts are set to constants by setting a threshold, and only a few decomposition coefficients are retained to represent the entire image.The experimental results show that the storage space of the compressed image is greatly saved and the compressed image does not change significantly from the original image visually.

Brain networks of rats under anesthesia using resting-state fMRI: comparison with dead rats, random noise and generative models of networks

Objective. Connectivity networks are crucial to understand the brain resting-state activity using functional magnetic resonance imaging (rs-fMRI). Alterations of these brain networks may highlight important findings concerning the resilience of the brain to different disorders. The focus of this paper is to evaluate the robustness of brain network estimations, discriminate them under anesthesia and compare them to generative models. Approach. The extraction of brain functional connectivity (FC) networks is difficult and biased due to the properties of the data: low signal to noise ratio, high dimension low sample size. We propose to use wavelet correlations to assess FC between brain areas under anesthesia using four anesthetics (isoflurane, etomidate, medetomidine, urethane). The networks are then deduced from the functional connectivity matrices by applying statistical thresholds computed using the number of samples at a given scale of wavelet decomposition. Graph measures are extracted and extensive comparisons with generative models of structured networks are conducted. Main results. The sample size and filtering are critical to obtain significant correlations values and thereby detect connections between regions. This is necessary to construct networks different from random ones as shown using rs-fMRI brain networks of dead rats. Brain networks under anesthesia on rats have topological features that are mixing small-world, scale-free and random networks. Betweenness centrality indicates that hubs are present in brain networks obtained from anesthetized rats but locations of these hubs are altered by anesthesia. Significance. Understanding the effects of anesthesia on brain areas is of particular importance in the context of animal research since animal models are commonly used to explore functions, evaluate lesions or illnesses, and test new drugs. More generally, results indicate that the use of correlations in the context of fMRI signals is robust but must be treated with caution. Solutions are proposed in order to control spurious correlations by setting them to zero.

Characteristics of fluctuating wall-shear stress in a turbulent boundary layer at low-to-moderate Reynolds number

Particle Image Velocimetry is used to measure the wall-shear stress (WSS) in a smooth-wall turbulent boundary layer at low-to-moderate Reynolds number. The fluctuating intensity of WSS is found to follow an empirical log-law with Re due to the effect of outer-layer large scale motions (LSMs) on the wall. A scale decomposition analysis suggests that the LSMs leave a mild footprint effect on the WSS in the present Re range. The amplitude modulation effect is inferred to play a more prominent role in affecting the characteristics of fluctuating WSS.