Spurious Spikes Research Articles

Error‐based grid adaptation methods for plasma edge simulations with SOLPS‐ITER

AbstractOnly recently, plasma edge simulations up to the wall have been enabled with SOLPS‐ITER. This requires dedicated gridding techniques to reconcile grid alignment with the magnetic field and refinement toward the wall as grid quality is primordial to ensure fast and reliable convergence. Therefore, the gridding approach for the grids up to the wall is analyzed and improved. A truncation error analysis is performed on the discrete operators of the discretization scheme in SOLPS‐ITER, resulting in indicators of grid properties that are undesired. Based on these indicators, grid adaptation and grid smoothing algorithms are developed to reduce truncation errors and improve the overall grid quality. The resulting methods are applied on an AUG single‐null case. Here, the impact of the new gridding strategy is examined on the divertor heat load, a typical quantity of interest for plasma edge simulations. The new gridding methods allow to mitigate spurious numerical spikes in the target heat load profiles, reduce the convergence time with a factor 30, and improve the accuracy of the heat load with a factor 3 compared to original grids with similar total number of cells.

Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1

Abstract. The Canadian Earth System Model version 5.0 (CanESM5.0), the most recent major version of the global climate model developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) at Environment and Climate Change Canada (ECCC), has been used extensively in climate research and for providing future climate projections in the context of climate services. Previous studies have shown that CanESM5.0 performs well compared to other models and have revealed several model biases. To address these biases, the CCCma has recently initiated the “Analysis for Development” (A4D) activity, a coordinated analysis activity in support of CanESM development. Here we describe the goals and organization of this effort and introduce two variants (“p1” and “p2”) of a new CanESM version, CanESM5.1, which features important improvements as a result of the A4D activity. These improvements include the elimination of spurious stratospheric temperature spikes and an improved simulation of tropospheric dust. Other climate aspects of the p1 variant of CanESM5.1 are similar to those of CanESM5.0, while the p2 variant of CanESM5.1 features reduced equilibrium climate sensitivity and improved El Niño–Southern Oscillation (ENSO) variability as a result of intentional tuning of the atmospheric component. The A4D activity has also led to the improved understanding of other notable CanESM5.0 and CanESM5.1 biases, including the overestimation of North Atlantic sea ice, a cold bias over sea ice, biases in the stratospheric circulation and a cold bias over the Himalayas. It provides a potential framework for the broader climate community to contribute to CanESM development, which will facilitate further model improvements and ultimately lead to improved climate change information.

Study of a numerical integration method using the compact scheme for electromagnetic transient simulations

This paper proposes a one-stage and oscillation free numerical integration method using the compact scheme for electromagnetic transient simulations. Since the compact scheme becomes L-stable at a moment when a circuit suddenly changes to a stiff system, the method is capable of suppressing the spurious numerical oscillations. Moreover, the compact scheme, which is a one-stage method, does not produce spurious spikes due to nonlinear elements. The compact scheme is compared with the trapezoidal method, the two-stage diagonally implicit Runge-Kutta (2S-DIRK) and the trapezoidal method with the second order backward difference formula (TR-BDF2). It follows from the comparison that the compact scheme does not produce the spurious numerical oscillations and spikes.

Wavelet sparsity enhancement for extracting transient vibration signatures of bearing structural damages

Wavelet methods are widely used in mechanical transient vibration signature detection and fault diagnosis. Undesirable artifacts (e.g., spurious noise spikes and pseudo-Gibbs components) are serious issues for the mainstream methods, probably resulting in inaccurate analysis results. For this reason, a new wavelet sparsity enhancement methodology is proposed to achieve artifact-free extraction of bearing transient vibration signatures. The wavelet sparsity is enhanced through two aspects—that is, wavelet basis design and wavelet coefficient processing. Specifically, we use a new family of wavelets called fractional B-spline wavelets on bearing fault signal analysis and propose the adaptive undecimated fractional spline wavelet transform which addresses the shift-invariant problem and also permits to customize an optimal wavelet basis according to the signal itself adaptively. Meanwhile, we introduce a two-step wavelet processing method including a nonlinear operator and a generalized hard-thresholding (with symmetric or asymmetric thresholds determined automatically by wavelet coefficients at each level). Moreover, unlike the most existing wavelet methods, the approximation coefficients are also processed along with the detailed coefficients to remove the possible low-frequency noise. The final transient vibration signatures are reconstructed with the processed coefficients and would be sparser, more accurate, and almost free of artifacts. The validity of the methodology is verified with the analysis results of vibration signals measured from fault-injection experiment and industrial wind turbine transmission system. The comparisons highlight the advantages of the methodology over several common methods in suppressing artifacts and extracting the sparse transient vibration signatures of bearing structural damages.

Bearing Fault Diagnosis Based on an Enhanced Image Representation Method of Vibration Signal and Conditional Super Token Transformer.

Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is an advanced deconvolution method, which can effectively inhibit the interference of background noise and distinguish the fault period by calculating the multipoint kurtosis values. However, multipoint kurtosis (MKurt) could lead to misjudgment since it is sensitive to spurious noise spikes. Considering that L-kurtosis has good robustness with noise, this paper proposes a multipoint envelope L-kurtosis (MELkurt) method for establishing the temporal features. Then, an enhanced image representation method of vibration signals is proposed by employing the Gramian Angular Difference Field (GADF) method to convert the MELkurt series into images. Furthermore, to effectively learn and extract the features of GADF images, this paper develops a deep learning method named Conditional Super Token Transformer (CSTT) by incorporating the Super Token Transformer block, Super Token Mixer module, and Conditional Positional Encoding mechanism into Vision Transformer appropriately. Transfer learning is introduced to enhance the diagnostic accuracy and generalization capability of the designed CSTT. Consequently, a novel bearing fault diagnosis framework is established based on the presented enhanced image representation and CSTT. The proposed method is compared with Vision Transformer and some CNN-based models to verify the recognition effect by two experimental datasets. The results show that MELkurt significantly improves the fault feature enhancement ability with superior noise robustness to kurtosis, and the proposed CSTT achieves the highest diagnostic accuracy and stability.

Countering the Deleterious Effects of Electromagnetic Pulse

Robot systems like automated shipping swinging robots, wire transducer sensors and even computer indigenous time sensors (amongst others) often use oscillating circuits such as the famous van der Pol system, while this manuscript investigates protection of such sensor circuitry to spurious voltage spikes accompanying an electromagnetic pulse. These spurious voltages can lead to uncontrolled robot motion and even debilitation. A very brief discussion of electromagnetic pulses yields design parameters to evaluate circuit responses to realistic disturbing pulses. Recent research in nonlinear-adaptive methods to protect circuits are described to highlight the proposed novelty: utilization of feedback rules as adaptive mechanisms to modify the otherwise nonlinear feedforwards systems improving the results in recent literature. Feedback is iterated to select adaption parameters that simultaneously produce favorable circuit performance in addition to effective parameter identification inherent in the adaption (to provide meaningful parameter estimates to unspecified future applications). Spurious voltages were rapidly rejected with a mere 0.3% trajectory deviation, stabilizing quickly with a final (steady state) deviation of 0.01%. The demonstrated abilities to reject the deleterious spurious effects are compared to nominal figures of merit for timing accuracy of various computer systems to conclude the proposed methods are effective for some applications, but insufficient for others.

Improving Victimization Risk Estimation: A Geographically Weighted Regression Approach

Standardized crime rates (e.g., “homicides per 100,000 people”) are commonly used in crime analysis as indicators of victimization risk but are prone to several issues that can lead to bias and error. In this study, a more robust approach (GWRisk) is proposed for tackling the problem of estimating victimization risk. After formally defining victimization risk and modeling its sources of uncertainty, a new method is presented: GWRisk uses geographically weighted regression to model the relation between crime counts and population size, and the geographically varying coefficient generated can be interpreted as the victimization risk. A simulation study shows how GWRisk outperforms naïve standardization and Empirical Bayesian Estimators in estimating risk. In addition, to illustrate its use, GWRisk is applied to the case of residential burglaries in Belo Horizonte, Brazil. This new approach allows more robust estimates of victimization risk than other traditional methods. Spurious spikes of victimization risk, commonly found in areas with small populations when other methods are used, are filtered out by GWRisk. Finally, GWRisk allows separating a reference population into segments (e.g., houses, apartments), estimating the risk for each segment even if crime counts were not provided per segment.

Calcium Imaging and the Curse of Negativity.

The imaging of neuronal activity using calcium indicators has become a staple of modern neuroscience. However, without ground truths, there is a real risk of missing a significant portion of the real responses. Here, we show that a common assumption, the non-negativity of the neuronal responses as detected by calcium indicators, biases all levels of the frequently used analytical methods for these data. From the extraction of meaningful fluorescence changes to spike inference and the analysis of inferred spikes, each step risks missing real responses because of the assumption of non-negativity. We first show that negative deviations from baseline can exist in calcium imaging of neuronal activity. Then, we use simulated data to test three popular algorithms for image analysis, CaImAn, suite2p, and CellSort, finding that suite2p may be the best suited to large datasets. We also tested the spike inference algorithms included in CaImAn, suite2p, and Cellsort, as well as the dedicated inference algorithms MLspike and CASCADE, and found each to have limitations in dealing with inhibited neurons. Among these spike inference algorithms, FOOPSI, from CaImAn, performed the best on inhibited neurons, but even this algorithm inferred spurious spikes upon the return of the fluorescence signal to baseline. As such, new approaches will be needed before spikes can be sensitively and accurately inferred from calcium data in inhibited neurons. We further suggest avoiding data analysis approaches that, by assuming non-negativity, ignore inhibited responses. Instead, we suggest a first exploratory step, using k-means or PCA for example, to detect whether meaningful negative deviations are present. Taking these steps will ensure that inhibition, as well as excitation, is detected in calcium imaging datasets.

An Autoregressive Exogenous Neural Network to Model Fire Behavior via a Naïve Bayes Filter

This work presents an artificial neural network-based linearly regressive technique for the prediction of a temperature rise event caused by a fire in enclosed building environments. The method predicts temperature range in a burning compartment based on the historic fire behavior data modelled via a neural network algorithm. The approach further extends the method by transforming the regression outcome as actionable information for firefighters via a self-organising feature map (SOM) fire-stage-clustering algorithm which categorises the predicted temperature range to provide warnings of any imminent and catastrophic temperature variations. The methodology implements a linear regression artificial neural model to model and predicts catastrophic temperature variations that are a threat to fire-and-rescue personnel in enclosed burning compartments via a ceiling gas-layer temperature acting as an exogenous variable. Based on the predicted temperature window, the SOM classifier maps the output in distinct clusters while isolating spurious prediction spikes and categorising the output to various self-organising threat/warning levels. The model was trained on temperature data captured with pole-mounted sensors in controlled fire training exercises. Tests carried out in actual fire-settings showed the capability of the SOM classifier to generate evacuation warnings for forecasted temperatures exceeding 300 C in 5 - 30 seconds ahead of the occurrences. Tests on these short-term and long-term prediction ranged from 78.31% to 96.27% in accuracy whereas the SOM classifier generated an overall accuracy of 76.92% to 100%. The techniques also showed a high resilience against both false-positives and false-negatives.

Cast surface texture characterisation via areal roughness

Areal surface texture characterisation leads to significantly enhanced surface information content. To obtain such an improved data set, several methodological steps are required. A suitable filter and nesting index have to be preselected based on surface topography and targeted structure content of the roughness. To select the nesting index, a filter study on synthetically generated surfaces has been performed firstly. Discrete Fourier transform features the selection of the nesting index on basis of manufacturing process affected surface topography. In terms of areal roughness calculation, the nesting index is proposed to be at least two times the present surface period length ls. In our investigations, a nesting index of 8 mm matched best for the analysed sand cast surfaces. As spurious spikes may occur by overexposure during image acquisition, a procedure is presented to minimize these by invoking Hampel filter based strategies. The newly introduced sub-area evaluation methodology further improves the roughness-based information content as distinctive, local surface structure characteristics can be traced back to corresponding areal roughness parameters. Finally, analysis of sub-area roughness parameters reveal that the arithmetical mean height Sa is log-logistic distributed.

Suppression of Switching Crosstalk and Voltage Oscillations in a SiC MOSFET Based Half-Bridge Converter

The silicon carbide (SiC) MOSFET is characterized by high operating voltage, temperature, switching frequency and efficiency which enables a converter to achieve high power density. However, at high switching frequency, the crosstalk phenomenon occurs when the gate voltage spike introduced by high dv/dt and voltage ringing forces false turn-on of SiC MOSFET which causes a crow-bar current thereby increasing switching losses. In order to increase the immunity against the crosstalk phenomenon in a half-bridge configuration, this paper presents a gate driver for SiC MOSFET capable of generating the negative turn-off voltage without using a negative power supply. In addition, the effect of parasitic inductances on the switching response is analyzed and an RC snubber is designed using high-frequency based circuit reduction technique to dampen the switching ringing. The performance of the proposed gate driver and the designed RC snubber is validated using simulation and experiment at the 1 MHz switching frequency. The results show that the proposed gate driver with RC snubber eliminates crosstalk by maintaining any spurious gate spike below the gate threshold voltage.

Estimating instantaneous concentration of suspended sediment using acoustic backscatter from an ADV

The measurement of instantaneous sediment concentration remains a challenging task. In this study, a three-step procedure is proposed to estimate instantaneous sediment concentration using acoustic backscatter from Acoustic Doppler Velocimeter (ADV). The influences of acoustic noise and particle diameter on an ADV׳s performance was first tested in the laboratory, then the three-step procedure was verified based on field measurements in the Zhongxian and Fengjie reaches in the Three Gorges Reservoir. The first step involves reconstructing the backscatter signal time series. Due to contamination from both the noise floor and spurious spikes, the denoising-despiking method was applied instead of the traditional velocity-despiking methods, and this approach performed well based on spectrum analysis. The second step involves calibrating the sediment concentration against the backscatter signal. A linear relation, whose slope and intercept were calibrated to be dependent on particle diameter, is proposed in double logarithmic coordinates. The third step involves calculating the instantaneous sediment concentration using reconstructed instantaneous backscatter based on the proposed relation. The accuracy of the proposed method was evaluated through consideration of the concentration spectrum and sediment flux, indicating that the proposed three-step procedure is effective for the measurement of instantaneous sediment concentration.

VADASE Reliability and Accuracy of Real-Time Displacement Estimation: Application to the Central Italy 2016 Earthquakes

The goal of this article is the illustration of the new functionalities of the VADASE (Variometric Approach for Displacements Analysis Stand-alone Engine) processing approach. VADASE was presented in previous works as an approach able to estimate in real time the velocities and displacements in a global reference frame (ITRF), using high-rate (1 Hz or more) carrier phase observations and broadcast products (orbits, clocks) collected by a stand-alone GNSS receiver, achieving a displacements accuracy within 1–2 cm (usually better) over intervals up to a few minutes. It has been well known since the very first implementation and testing of VADASE that the estimated displacements might be impacted by two different effects: spurious spikes in the velocities due to outliers (consequently, displacements, obtained through velocities integration, are severely corrupted) and trends in the displacements time series, mainly due to broadcast orbit and clock errors. Two strategies are herein introduced, respectively based on Leave-One-Out cross-validation (VADASE-LOO) for a receiver autonomous outlier detection, and on a network augmentation strategy to filter common trends out (A-VADASE); they are combined (first, VADASE-LOO; second, A-VADASE) for a complete solution. Moreover, starting from this VADASE improved solution, an additional strategy is proposed to estimate in real time the overall coseismic displacement occurring at each GNSS receiver. New VADASE advances are successfully applied to the GPS data collected during the recent three strong earthquakes that occurred in Central Italy on 24 August and 26 and 30 October 2016, and the results are herein presented and discussed. The VADASE real-time estimated coseismic displacements are compared to the static ones derived from the daily solutions obtained within the standard post-processing procedure by the Istituto Nazionale di Geofisica e Vulcanologia.

A simple algorithm for despiking Raman spectra

Raman Spectroscopy is a widely used analytical technique, favoured when molecular specificity with minimal sample preparation is required. The majority of Raman instruments use charge-coupled device (ccd) detectors, these are susceptible to cosmic rays and as such multiple spurious spikes can occur in the measurement. These spikes are problematic as they may hinder subsequent analysis, particularly if multivariate data analysis is required. In this work we present a new algorithm to remove these spikes from spectra after acquisition. Specifically, our algorithm uses modified Z scores calculated from the once-differenced detrended spectrum to locate the offending spikes, followed by a simple moving average to remove them. The algorithm is very simple and its execution is essentially instantaneous, resulting in spike-free spectra with minimal distortion of actual Raman data. The presented algorithm represents an improvement on existing spike removal methods by utilising simple, easy to understand mathematical concepts, making it ideal for experts and non-experts alike.

Separation of superparamagnetic magnetite nanoparticles by capillary zone electrophoresis using non-complexing and complexing electrolyte anions and tetramethylammonium as dispersing additive.

Separations of bare superparamagnetic magnetite nanoparticles (BSPMNPs, approx. 11 nm diameter) was performed using non-complexing (nitrate) and complexing (chloride, citrate and phosphate) electrolyte ions with additions of tetramethylammonium hydroxide (TMAOH), which is commonly applied to control the synthesis of stable iron oxides. The use of TMAOH as a background electrolyte (BGE) additive for capillary electrophoresis (CE) separations provided for the first time electropherograms of BSPMNPs exhibiting symmetrical and highly reproducible peaks, free of spurious spikes characteristic of nanoparticle clusters. Consequently, accurate determination of the electrophoretic effective mobility of BSPMNPs was possible, yielding a value of -3.345E-08 m2 V-1 s-1 (relative standard deviation (RSD) of 0.500%). The obtained mobilities of BSPMNPs in the presence of various electrolyte ions show that the degree of complexation with the surface of BSPMNPs follows the order chloride < citrate < phosphate, correlating with the stabilities of Fe(III) complexes with the respective anions. Finally, bare and carboxylated iron oxide nanoparticles were successfully separated in only 10 min using 10 mM Tris-nitrate containing 20mM of TMAOH as electrolyte. Our findings show that simple and rapid CE experiments are an excellent tool to characterise and monitor properties and interactions of iron oxide nanoparticles with other molecules for surface modification purposes.

Numerical shockwave anomalies in the resolu- tion of the Shallow Water Equations with bed variations

The presence of numerical shockwave anomalies appearing in the resolution of hyperbolic systems of conservation laws is a well-known problem in the scientific community. The most common anomalies are the carbuncle and the slowly-moving shock anomaly. They have been studied for decades in the framework of Euler equations, but only a few authors have considered such problems for the Shallow Water Equations (SWE). In this work, the SWE are considered and the aforementioned anomalies are studied. They arise in presence of hydraulic jumps, which are transcritical shockwaves mathematically modelled as a pure discontinuity. When solving numerically such discontinuities, an unphysical intermediate state appears and gives rise to a spurious spike in the momentum. This is observed in the numerical solution as a spike in the discharge appearing in the cell containing the jump. The presence of the spurious spike in the discharge has been taken for granted by the scientific community and has even become a feature of the solution. Even though it does not disturb the rest of the solution in steady cases, it produces an undesirable shedding of spurious oscillations downstream when considering transient events. We show how it is possible to define a coherent spike reduction technique that reduces the magnitude of this anomaly and ensures convergence to the exact solution with mesh refinement. Concerning the carbuncle, which may also appear in presence of strong hydraulic jumps, a combination of Riemann solvers is proposed to circumvent it. Also, it will be shown how there is still room from improvement when treating anomalies in moving hydraulic jumps over variable topography.

Convex composite wavelet frame and total variation-based image deblurring using nonconvex penalty functions

Total variation (TV)-based image deblurring method can bring on staircase artifacts in the homogenous region of the latent images recovered from the degraded images while a wavelet/frame-based image deblurring method will lead to spurious noise spikes and pseudo-Gibbs artifacts in the vicinity of discontinuities of the latent images. To suppress these artifacts efficiently, we propose a nonconvex composite wavelet/frame and TV-based image deblurring model. In this model, the wavelet/frame and the TV-based methods may complement each other, which are verified by theoretical analysis and experimental results. To further improve the quality of the latent images, nonconvex penalty function is used to be the regularization terms of the model, which may induce a stronger sparse solution and will more accurately estimate the relative large gradient or wavelet/frame coefficients of the latent images. In addition, by choosing a suitable parameter to the nonconvex penalty function, the subproblem that splits by the alternative direction method of multipliers algorithm from the proposed model can be guaranteed to be a convex optimization problem; hence, each subproblem can converge to a global optimum. The mean doubly augmented Lagrangian and the isotropic split Bregman algorithms are used to solve these convex subproblems where the designed proximal operator is used to reduce the computational complexity of the algorithms. Extensive numerical experiments indicate that the proposed model and algorithms are comparable to other state-of-the-art model and methods.

Overcoming numerical shockwave anomalies using energy balanced numerical schemes. Application to the Shallow Water Equations with discontinuous topography

When designing a numerical scheme for the resolution of conservation laws, the selection of a particular source term discretization (STD) may seem irrelevant whenever it ensures convergence with mesh refinement, but it has a decisive impact on the solution. In the framework of the Shallow Water Equations (SWE), well-balanced STD based on quiescent equilibrium are unable to converge to physically based solutions, which can be constructed considering energy arguments. Energy based discretizations can be designed assuming dissipation or conservation, but in any case, the STD procedure required should not be merely based on ad hoc approximations. The STD proposed in this work is derived from the Generalized Hugoniot Locus obtained from the Generalized Rankine Hugoniot conditions and the Integral Curve across the contact wave associated to the bed step. In any case, the STD must allow energy-dissipative solutions: steady and unsteady hydraulic jumps, for which some numerical anomalies have been documented in the literature. These anomalies are the incorrect positioning of steady jumps and the presence of a spurious spike of discharge inside the cell containing the jump. The former issue can be addressed by proposing a modification of the energy-conservative STD that ensures a correct dissipation rate across the hydraulic jump, whereas the latter is of greater complexity and cannot be fixed by simply choosing a suitable STD, as there are more variables involved. The problem concerning the spike of discharge is a well-known problem in the scientific community, also known as slowly-moving shock anomaly, it is produced by a nonlinearity of the Hugoniot locus connecting the states at both sides of the jump. However, it seems that this issue is more a feature than a problem when considering steady solutions of the SWE containing hydraulic jumps. The presence of the spurious spike in the discharge has been taken for granted and has become a feature of the solution. Even though it does not disturb the rest of the solution in steady cases, when considering transient cases it produces a very undesirable shedding of spurious oscillations downstream that should be circumvented. Based on spike-reducing techniques (originally designed for homogeneous Euler equations) that propose the construction of interpolated fluxes in the untrustworthy regions, we design a novel Roe-type scheme for the SWE with discontinuous topography that reduces the presence of the aforementioned spurious spike. The resulting spike-reducing method in combination with the proposed STD ensures an accurate positioning of steady jumps, provides convergence with mesh refinement, which was not possible for previous methods that cannot avoid the spike.

WIDEBAND INTERDIGITAL CAPACITOR WITH SPURIOUS SPIKES SUPPRESSION

WIDEBAND INTERDIGITAL CAPACITOR WITH SPURIOUS SPIKES SUPPRESSION

Automatic Spike Removal Algorithm for Raman Spectra.

Raman spectroscopy is a powerful technique, widely used in both academia and industry. In part, the technique's extensive use stems from its ability to uniquely identify and image various material parameters: composition, strain, temperature, lattice/excitation symmetry, and magnetism in bulk, nano, solid, and organic materials. However, in nanomaterials and samples with low thermal conductivity, these measurements require long acquisition times. On the other hand, charge-coupled device (CCD) detectors used in Raman microscopes are vulnerable to cosmic rays. As a result, many spurious spikes occur in the measured spectra, which can distort the result or require the spectra to be ignored. In this paper, we outline a new method that significantly improves upon existing algorithms for removing these spikes. Specifically, we employ wavelet transform and data clustering in a new spike-removal algorithm. This algorithm results in spike-free spectra with negligible spectral distortion. The reduced dependence on the selection of wavelets and intuitive wavelet coefficient adjustment strategy enables non-experts to employ these powerful spectra-filtering techniques.