Multi-scale Frequency Research Articles

Dual-layer optimized selective information fusion using multi-source multi-component mechanical signals for mill load parameters forecasting

Abstract Ball mill is a heavy mechanical device necessary for grinding. Mill load parameters (MLP) relate to production economic indices and process safety. Mechanical signals of the ball mill are used to estimate MLP by domain experts. However, they can only estimate familiar mills effectively in certain time because of human limitation. A new dual-layer optimized selective information fusion is proposed based on the analysis of the characteristics of mill mechanical signals and cognitive behavior of the domain expert for MLP forecasting (MLPF). An ensemble construction strategy based on multi-component mechanical signals adaptive decomposition is employed to build candidate sub-models by using kernel partial least squares (KPLS). The dual-layer optimization strategy is proposed to build selective ensemble (SEN) KPLS (SENKPLS) with optimized ensemble sub-models and their coefficients, thus realizing the trade-off between prediction accuracies and diversity implicitly. The MLPF models based on SENKPLS are constructed by selective fusion multi-source multi-scale frequency spectral information in terms of the auditory perception process of the simulation domain experts. Results show that the proposed strategy can obtain better forecasting results than other state-of-the-art methods.

Multiscale analysis of flow patterns in the dense‐phase pneumatic conveying of pulverized coal

AbstractFour typical flow patterns in the vertical pipe of the dense‐phase pneumatic conveying of pulverized coal were obtained, namely packed bed flow, plug flow, churn flow and less dense phase flow. The electrical capacitance tomography (ECT) was used for space–time multiscale analysis. Firstly, Daubechies second order wavelet transform and rescaled range analysis(R/S) were applied for the scientific multiscale decompositions. The multiscale frequency range is that the microscale signal is d1–d5 (2.1875, 70.0000) Hz, the mesoscale signal is d6 and d7 (0.5469, 2.1875) Hz and the macroscale signal is d8–d11 (0.0342, 0.5469) Hz. The multiscale ECT signal characteristics of pure gas flow, particle–wall friction, and particle–particle collision were determined by experiments. The multiscale ECT signal energy matrix is proposed, based on the multiscale energy structure characteristics. Control mechanisms and multiscale statistical rules of four typical flow patterns are obtained. This study provides theoretical support for the study of flow pattern transformation of dense‐phase pneumatic conveying.

Multi-Scale Frequency Reconstruction for Guided Depth Map Super-Resolution via Deep Residual Network

The depth maps obtained by the consumer-level sensors are always noisy in the low-resolution (LR) domain. Existing methods for the guided depth super-resolution, which are based on the pre-defined local and global models, perform well in general cases (e.g., joint bilateral filter and Markov random field). However, such model-based methods may fail to describe the potential relationship between RGB-D image pairs. To solve this problem, this paper proposes a data-driven approach based on the deep convolutional neural network with global and local residual learning. It progressively upsamples the LR depth map guided by the high-resolution intensity image in multiple scales. A global residual learning is adopted to learn the difference between the ground truth and the coarsely upsampled depth map, and the local residual learning is introduced in each scale-dependent reconstruction sub-network. This scheme can restore the depth structure from coarse to fine via multi-scale frequency synthesis. In addition, batch normalization layers are used to improve the performance of depth map denoising. Our method is evaluated in noise-free and noisy cases. A comprehensive comparison against 17 state-of-the-art methods is carried out. The experimental results show that the proposed method has faster convergence speed as well as improved performances based on the qualitative and quantitative evaluations.

Evaluation of Multiscale Frequency Approach for Visualizing Rotors in Patients with Atrial Fibrillation.

Atrial Fibrillation (AF) is most common cardiac arrhythmia. It is associated with increased risk of stroke, heart failure and sudden cardiac death. Catheter ablation is a treatment used to control AF and has had suboptimal success for patients with persistent AF, which is primarily maintained by rotors outside of the pulmonary veins (PV) region. The pivot point (core) of the rotor is considered an efficient target for ablation. Currently available electro-anatomical mapping systems cannot accurately predict the exact location of the pivot point of rotors outside of the PV region, so there is a need for novel approaches to accurately identify and distinguish sites for ablation. Recently, a multiscale frequency (MSF) technique was developed for accurate identification of the pivot point of rotors and validated using optical mapping experiments in exvivo rabbit hearts, where electrical activity can be directly visualized. However, the nature of optical signals and its spatial resolution are very different from clinical intracardiac electrograms (iEGM). Here we extend the MSF approach to 3D iEGM and compare its prediction with the traditional dominant frequency (DF) approach, using Pearson's correlation and earth mover's distance methods. Our results demonstrate that the similarity between MSF and DF are high in some regions, but very low in other spatial regions of the human atria. This indicates the inconsistency in the traditional DF approach in identifying pivot points and identifying such low similarity regions can be used to find sites for successful ablation.

Aerial Image Super Resolution via Wavelet Multiscale Convolutional Neural Networks

We develop an aerial image super-resolution method by training convolutional neural networks (CNNs) with respect to wavelet analysis. To this end, we commence by performing wavelet decomposition to aerial images for multiscale representations. We then train multiple CNNs for approximating the wavelet multiscale representations, separately. The multiple CNNs thus trained characterize aerial images in multiple directions and multiscale frequency bands, and thus enable image restoration subject to sophisticated culture variability. For inference, the trained CNNs regress wavelet multiscale representations from a low-resolution aerial image, followed by wavelet synthesis that forms a restored high-resolution aerial image. Experimental results validate the effectiveness of our method for restoring complicated aerial images.

On the role of density and attenuation in three-dimensional multiparameter viscoacoustic VTI frequency-domain FWI: an OBC case study from the North Sea

3-D frequency-domain full waveform inversion (FWI) is applied on North Sea wide-azimuth ocean-bottom cable data at low frequencies (≤10 Hz) to jointly update vertical wave speed, density and quality factor Q in the viscoacoustic VTI approximation. We assess whether density and Q should be viewed as proxy to absorb artefacts resulting from approximate wave physics or are valuable for interpretation in the presence of soft sediments and gas cloud. FWI is performed in the frequency domain to account for attenuation easily. Multiparameter frequency-domain FWI is efficiently performed with a few discrete frequencies following a multiscale frequency continuation. However, grouping a few frequencies during each multiscale step is necessary to mitigate acquisition footprint and match dispersive shallow guided waves. Q and density absorb a significant part of the acquisition footprint hence cleaning the velocity model from this pollution. Low Q perturbations correlate with low-velocity zones associated with soft sediments and gas cloud. However, the amplitudes of the Q perturbations show significant variations when the inversion tuning is modified. This dispersion in the Q reconstructions is however not passed on the velocity parameter suggesting that cross-talks between first-order kinematic and second-order dynamic parameters are limited. The density model shows a good match with a well log at shallow depths. Moreover, the impedance built a posteriori from the FWI velocity and density models shows a well-focused image with however local differences with the velocity model near the sea bed where density might have absorbed elastic effects. The FWI models are finally assessed against time-domain synthetic seismogram modelling performed with the same frequency-domain modelling engine used for FWI.

Novel approaches for quantitative electrogram analysis for intraprocedural guidance for catheter ablation: A case of a patient with persistent atrial fibrillation.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia that causes stroke affecting more than 2.3 million people in the US and is increasing in prevalence due to ageing population causing a new global epidemic. Catheter ablation with pulmonary vein isolation (PVI) to terminate AF is successful for paroxysmal AF but suffers limitations with persistent AF patients as current mapping methods cannot identify AF active substrates outside of PVI region. Recent evidences in the mechanistic understating of AF pathophysiology suggest that ectopic activity, localized re-entrant circuit with fibrillatory propagation and multiple circuit re-entries may all be involved in human AF. The authors developed novel electrogram analysis methods and validated using optical mapping data from isolated rabbit hearts to accurately identify rotor pivot points. The purpose of this study was to assess the feasibility of generating patient-specific 3D maps for intraprocedural guidance for catheter ablation using intracardiac electrograms from a persistent AF patient using novel electrogram analysis methods. A persistent AF patient with clinical appointment for AF ablation was recruited for this study with IRB approval. 1055 electrograms throughout the left and right atrium were obtained for offline analysis with the novel approaches such as multiscale entropy, multiscale frequency, recurrence period density entropy, kurtosis and empirical mode decomposition to generate patient specific 3D maps. 3D Shannon Entropy, Renyi Entropy and Dominant frequency maps were also generated for comparison purposes along with local activation time and complex fractionated electrogram analysis maps. Patient specific 3D maps were obtained for each of the different approach. The 3D maps indicate potential active sites outside the PVI region. However, presence of rotors cannot be confirmed and validation of these approaches is required on a larger dataset. Conventional catheter mapping system can be used for generating patient specific 3D maps with short time series analysis using the novel approaches.

Novel Multiscale Frequency Approach to Identify the Pivot Point of the Rotor1

Novel Multiscale Frequency Approach to Identify the Pivot Point of the Rotor1

Selective ensemble modeling load parameters of ball mill based on multi-scale frequency spectral features and sphere criterion

It is difficult to model multi-frequency signal, such as mechanical vibration and acoustic signals of wet ball mill in the mineral grinding process. In this paper, these signals are decomposed into multi-scale intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD) technique. A new adaptive multi-scale spectral features selection approach based on sphere criterion (SC) is applied to these IMFs frequency spectra. The candidate sub-models are constructed by the partial least squares (PLS) with the selected features. Finally, the branch and bound based selective ensemble (BBSEN) algorithm is applied to select and combine these ensemble sub-models. This method can be easily extended to regression and classification problems with multi-time scale signal. We successfully apply this approach to a laboratory-scale ball mill. The shell vibration and acoustic signals are used to model mill load parameters. The experimental results demonstrate that this novel approach is more effective than the other modeling methods based on multi-scale frequency spectral features.

Spectro-temporal dynamics of Kerr combs with parametric seeding.

We report a joint theoretical and experimental investigation of the parametric seeding of a primary Kerr optical frequency comb. Electro-optic modulation sidebands matching multiple free-spectral ranges of an ultrahigh-Q millimeter-size magnesium fluoride disk resonator are used as seed signals. These seed signals interact through four-wave mixing with the spectral components of a stable primary comb and give rise to complex spectro-temporal patterns. We show that the new frequency combs feature multiscale frequency spacing, with major frequency gaps in the order of a few hundred gigahertz, and minor frequency spacing in the order of a few tens of gigahertz. The experimental results are in agreement with numerical simulations using the Lugiato-Lefever equation. We expect such versatile and coherent optical frequency combs to have potential applications in optical communications systems where frequency management assigns predefined spectral windows at the emitter stage.

Rhodium nanowires: Synthesis and nanostructure tailoring by controlling hydrogen evolution

We report the use of rhodium sulfate solution for electrochemical synthesis of straight separable cylindrical rhodium nanowires that can be used as nanoresonators. We studied the effect of pH on the length uniformity and the effect of EDTA and polyethylenimine as additives on the development of the wire nanostructure. Multiscale frequency hydrogen bubble trapping and release was observed in the special case of a sulfate-based solution with 0.1vol% polyethylenimine as an additive which results in formation of nanowires with ultrafine grain structure. The control over hydrogen co-reduction on the electrode surface and its bubble transport rate allowed for tailoring the nanostructure of the grown nanowires. Electrodeposition was performed at room temperature to avoid dissolution of the porous alumina template.

Multi-domain multi-scale problems in high frequency finite element methods

Purpose – This paper aims to present a domain decomposition method to overcome the challenges posed by multi-domain, multi-scale high frequency problems. Design/methodology/approach – A hybrid finite element and boundary integral procedure is also presented that allows for domains to employ different solution methods in different subdomains. Findings – By decomposing large electromagnetic regions into smaller domains, the finite element method can cope with the simulation of electrically large problems. Practical implications – Real life examples demonstrate the accuracy and efficiency of the new method. Originality/value – The Robin transmission condition (RTC) is applied to link the domains and preserve field continuity on interfaces.

Multi-scale fracture and fluid response characteristic in seismic data: Numerical simulation analysis

The exchange of fluid between fractures and rock matrix is considered to be a main factor influencing the calculated anisotropic elastic properties. Theoretical study shows that the fracture scales also have the same impact on it. To identify the multi-scale fracture, numerical analysis was introduced to analyze the variation of velocity and attenuation with the changes of the multi-scale fracture, frequency and fluid factor based on the equivalent medium model. The results show that the elastic parameters are frequency dependent, including the attenuation. This frequency dependence is related to the fracture scale and fluid properties of rocks. Within the various fracture scales with different velocity dispersion, the shear wave splitting depends on the frequency, and the scale of fracture is the leading factor, small scale is sensitive to high frequency, while low frequency to large scale. In the seismic frequency band, Thomsen parameters decreased with increasing frequency as well as the increasing of the fracture scale. Thus, seismic data may be an indication to distinguish the anisotropy caused by the fracture or micro-crack and also used to distinguish fracture scale.

Multiscale Sampling Geometries and Methods for Deterministic and Stochastic Reconstructions of Magnitude and Phase Spectra of Satellite Imagery

This paper presents new methods for phase and magnitude interpolation and demonstrates their usefulness in reconstructing images from a limited number of frequency samples. A collection of multiscale frequency domain sampling geometries are developed based on the partition of the spectrum into low-, medium-, and high-frequency blocks. A nonstationary statistical approach is introduced that is based on adaptively selecting the best stochastic model in each frequency block. To develop effective models, the magnitude spectrum is preprocessed using a logarithmic transformation. Phase interpolation requires preprocessing by an appropriate phase unwrapping method. The new stochastic interpolation method is compared against cubic spline, bilinear, and nearest neighbor interpolation methods. Image reconstruction results are presented for sampling rates that retain 6.01% to 28.91% of the 2-D fast Fourier transform (FFT) samples. Image interpolation methods are compared based on the peak signal-to-noise ratio and the mean structural similarity index for satellite images of rural, natural, and urban images. The results indicate that the stochastic (Kriging) interpolation approach provides the best rural image reconstructions using just 6.01% of the 2-D FFT samples. Bilinear interpolation also gave excellent reconstructions for natural and urban images. For natural and urban images, stochastic interpolation gave the best magnitude-only interpolation results.

Multi-scale frequency and absorption attributes of seismic signals

Seismic absorption is sensitive to gas saturation, and this can lead to a number of observable features, such as low dominant frequency, absence of high frequencies and dimming of reflections. We obtain the time–frequency distribution of seismic signals at different scales based on wavelet decomposition and try to analyse the response of high-frequency sections for the characterization of hydrocarbon reservoirs. We present a case study using multi-scale (dominant) frequency and multi-scale absorption, and demonstrate that the method is effective in recognizing a deep tight-sandstone gas reservoir. By this study, we can draw the conclusion that multi-scale frequency and absorption attributes may indicate the gassy areas in reservoirs, and we have obtained many good applications in the Sichuan basin of China.

FWT2D: A massively parallel program for frequency-domain full-waveform tomography of wide-aperture seismic data—Part 1: Algorithm

This is the first paper in a two-part series that describes a massively parallel code that performs 2D frequency-domain full-waveform inversion of wide-aperture seismic data for imaging complex structures. Full-waveform inversion methods, namely quantitative seismic imaging methods based on the resolution of the full wave equation, are computationally expensive. Therefore, designing efficient algorithms which take advantage of parallel computing facilities is critical for the appraisal of these approaches when applied to representative case studies and for further improvements. Full-waveform modelling requires the resolution of a large sparse system of linear equations which is performed with the massively parallel direct solver MUMPS for efficient multiple-shot simulations. Efficiency of the multiple-shot solution phase (forward/backward substitutions) is improved by using the BLAS3 library. The inverse problem relies on a classic local optimization approach implemented with a gradient method. The direct solver returns the multiple-shot wavefield solutions distributed over the processors according to a domain decomposition driven by the distribution of the LU factors. The domain decomposition of the wavefield solutions is used to compute in parallel the gradient of the objective function and the diagonal Hessian, this latter providing a suitable scaling of the gradient. The algorithm allows one to test different strategies for multiscale frequency inversion ranging from successive mono-frequency inversion to simultaneous multifrequency inversion. These different inversion strategies will be illustrated in the following companion paper. The parallel efficiency and the scalability of the code will also be quantified.

Analysis Study on Convective Boundy Layer Eddy Structure in Water Tank by Orthonormal Wavelet

AbstractUsing orthonormal wavelet transform, We made analysis of experimental data of convective boundary layer turbulence in water tank, including multi‐scale decomposition and high frequency noise removing. Spectrum analyses show that turbulence in mixed layer is very different from that in entrainment zone. This difference may be caused by various characteristics of large scale element in turbulence. While the small scale turbulence in both mixed layer and entrainment zone are similar, which are homogeneous and their spectra are satisfied with Kolmogorov's scaling law with –5/3 exponent.

Principles of image processing in digital chest radiography.

Image processing has a major impact on image quality and diagnostic performance of digital chest radiographs. Goals of processing are to reduce the dynamic range of the image data to capture the full range of attenuation differences between lungs and mediastinum, to improve the modulation transfer function to optimize spatial resolution, to enhance structural contrast, and to suppress image noise. Image processing comprises look-up table operations and spatial filtering. Look-up table operations allow for automated signal normalization and arbitrary choice of image gradation. The most simple and still widely applied spatial filtering algorithms are based on unsharp masking. Various modifications were introduced for dynamic range reduction and MTF restoration. More elaborate and more effective are multi-scale frequency processing algorithms. They are based on the subdivision of an image in multiple frequency bands according to its structural composition. This allows for a wide range of image manipulations including a size-independent enhancement of low-contrast structures. Principles of the various algorithms will be explained and their impact on image appearance will be illustrated by clinical examples. Optimum and sub-optimum parameter settings are discussed and pitfalls will be explained.