Simplified Spectral Model Research Articles

Automatic Coal-Rock Recognition by Laser-Induced Breakdown Spectroscopy Combined with an Artificial Neural Network

Automatic coal-rock recognition (ACRR) is of considerable theoretical and practical significance for unmanned coal mining. To the best of our knowledge, this is the first study to assess laser-induced breakdown spectroscopy (LIBS) combined with an artificial neural network (ANN) for automatic coal-rock recognition. Each sample in this study was subjected to LIBS testing and spectrum collection 20 times in the air, and the average value was taken as the LIBS data. Spectral data were optimized and dimensionality reduction was performed using partial least-squares discriminant analysis (PLS-DA). The 10 selected wavelength lines were used to construct a simplified spectral model (SSM). The ANN based on SSM was designed to classify the coal and rock. The results demonstrated that LIBS combined with an ANN has a high recognition accuracy rate, providing a rapid and accurate coal-rock recognition method for unmanned coal mining.

Laser-Induced Breakdown Spectroscopy-Based Coal-Rock Recognition: An in Situ Sampling and Recognition Method

Coal and rock recognition (CRR) has important theoretical and practical significance in unmanned coal mining. Laser-induced breakdown spectroscopy (LIBS) is considered a cutting-edge technology in the field of material analysis due to its real-time analysis capability, minimal to no sample preparation scheme, high sensitivity to low atomic weight elements, and ability to perform nearby and distant detection. In this research, a new fast and accurate coal-rock recognition method for unmanned coal mining based on LIBS is presented. The LIBS in situ sampling method of the coal mining face and the LIBS-based CRR method are discussed. Partial least squares discriminant analysis (PLS-DA) is used to analyze the LIBS spectrum and to construct a simplified spectral model (SSM). Finally, SSM and neural network classifiers are used to recognize coal and rock, and the results are presented and discussed. These results show that the CRR method proposed in this research has a high recognition accuracy rate.

Model-based material decomposition with a penalized nonlinear least-squares CT reconstruction algorithm

Spectral information in CT may be used for material decomposition to produce accurate reconstructions of material density and to separate materials with similar overall attenuation. Traditional methods separate the reconstruction and decomposition steps, often resulting in undesirable trade-offs (e.g. sampling constraints, a simplified spectral model). In this work, we present a model-based material decomposition algorithm which performs the reconstruction and decomposition simultaneously using a multienergy forward model. In a kV-switching simulation study, the presented method is capable of reconstructing iodine at 0.5 mg ml−1 with a contrast-to-noise ratio greater than two, as compared to 3.0 mg ml−1 for image domain decomposition. The presented method also enables novel acquisition methods, which was demonstrated in this work with a combined kV-switching/split-filter acquisition explored in simulation and physical test bench studies. This novel design used four spectral channels to decompose three materials: water, iodine, and gadolinium. In simulation, the presented method accurately reconstructed concentration value estimates with RMSE values of 4.86 mg ml−1 for water, 0.108 mg ml−1 for iodine and 0.170 mg ml−1 for gadolinium. In test-bench data, the RMSE values were 134 mg ml−1, 5.26 mg ml−1 and 1.85 mg ml−1, respectively. These studies demonstrate the ability of model-based material decomposition to produce accurate concentration estimates in challenging spatial/spectral sampling acquisitions.

Role of fault gouge dilatancy on aseismic deformation transients

In the vicinity of episodic aseismic transients in several subduction zones, the presence of interstitial fluids and near‐lithostatic pore pressure has been proposed to interpret seismic observations of high P to S wave speed ratio and high Poisson's ratio. Under such conditions, fault stabilization by dilatancy‐induced suction during increased shear strain rates becomes very efficient. We analyze the frictional and hydraulic conditions for spontaneous transients on a fluid‐infiltrated fault including dilatancy and pore compaction in the framework of rate and state friction with a “membrane diffusion” approximation. In both a simplified spectral model and a 2‐D Cascadia‐like subduction fault model, the fault response is mainly controlled by three nondimensional parameters: (1) W/h*, the along‐dip width of the high pore pressure, velocity‐weakening fault relative to a characteristic nucleation size, (2) a drainage parameter U, the relative time scales for fluid diffusion and friction evolution, and (3) a dilatancy parameter E, the relative contributions to stress drop from dilatancy and friction evolution. The incorporation of dilatancy enables aseismic transients at much larger values of W/h* than is possible under conditions of constant pore pressure. An analytic estimate of the maximum slip velocity as a function of W/h*, E, and U is derived and agrees reasonably well with the simulation results. The dependence of the properties of modeled transients on the drainage parameter U is similar to that on the dilatancy parameter E. For U (E) less than 1, maximum velocity decreases, while recurrence period remains relatively constant. For U (E) greater than 1, maximum velocity approaches the steady state velocity, and recurrence period approaches the period at neutral stability. In the subduction fault model using gabbro gouge friction properties, the slip per episode and the recurrence period increase with W/h*, generally following the trend defined without dilatancy. The maximum velocity with dilatancy can be several orders of magnitude smaller than that without, in particular for larger values of E and values of W/h* near the no‐dilatancy stability limit.

Spectral Short-circuiting and Wake Production within the Canopy Trunk Space of an Alpine Hardwood Forest

Using synchronous multi-level high frequency velocity measurements, the turbulence spectra within the trunk space of an alpine hardwood forest were analysed. The spectral short-circuiting of the energy cascade for each velocity component was well repro- duced by a simplified spectral model that retained return-to-isotropy and component-wise work done by turbulence against the drag and wake production. However, the use of an aniso- tropic drag coefficient was necessary to reproduce these measured component-wise spectra. The degree of anisotropy in the vertical drag was shown to vary with the element Reynolds number. The wake production frequency in the measured spectra was shown to be consistent with the vortex shedding frequency at constant Strouhal number given by fvs = 0.21 ¯ u/d, where d can be related to the stem diameter at breast height (dbh )a nd¯

The Dissipation Range of Wind-Wave Spectra Observed on a Lake

Based on an interpretation of a field experiment it is argued that, due to breaking of wind waves in deep water, the dissipation of energy is restricted to a range of frequencies ω > ωg, much higher than the frequency ωm of the dominant waves. In this dissipation range the spectrum has the form S(ω) = βg2ω−5 where g is the acceleration due to gravity and β = 0.025. For spectral wave components at ω ≤ ωg, only a local balance between energy input from the wind and the weak, third-order, nonlinear interaction is important. Asymptotically as ω ≫ ωm the wind input becomes unimportant, and the wave spectrum has the Kitaigorodskii form of a Kolgomorov analog S(ω) = 2aε0⅓ g4/3 ω−4 where ε0 is a constant flow of mean energy per unit surface area through the spectrum dissipated at high frequencies (when multiplied by g and water density ρw). From a method of M. S. Longuet-Higgins we estimate the magnitude of the dissipation (due to wave breaking) and find the Kolmogorov constant to be a ≈ 0.6. When a model, explained by Phillips, for wind energy input to the wave spectrum is applied to a simplified spectral model prescribing the scales of dissipation and growth of spectral wave components, good agreement is found with measurements by Donelan et al. of the coefficient 2aε0⅓ and its dependence on the frequency ωm of the dominant waves at the spectral peak.

Gravitational Adjustment in Phase Space

Abstract It is suggested that when one more properly views meteorological prediction as an initial value problem couched in random or stochastic terms, there is an additional way for the wind and the mass fields to adjust to a state of balance. A simplified spectral model is used to isolate the gravity wave energy and to show how it becomes dispersed through phase space. Calculations show that this non-Gaussian adjustment cannot be described without the inclusion of third moments. Monte Carlo and stochastic dynamic calculations show that the entire ensemble of points which describe a system in phase space tend to a state of balance which is consistent with a solution to the stochastic balance equation. Based upon the results, the implications of stochastic models in dealing with gravity waves in meteorological predictions is assessed.