scholarly journals Comparison of Three Methodologies for Removal of Random‐Noise‐Induced Biases From Second‐Order Statistical Parameters of Lidar and Radar Measurements

2021 ◽  
Author(s):  
Jackson Jandreau ◽  
Xinzhao Chu
Keyword(s):  
Random Noise ◽  
Second Order ◽  
Statistical Parameters ◽  
Radar Measurements

Genetic algorithm based automated threshold estimation in translation invariant wavelet transform for denoising PD signal

2015 ◽  
Vol 34 (4) ◽  
pp. 1252-1269 ◽  
Author(s):  
R.V. Maheswari ◽  
B. Vigneshwaran ◽  
L. Kalaivani
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Computational Complexity ◽  
Condition Monitoring ◽  
Random Noise ◽  
Life Time ◽  
Discrete Wavelet ◽  
Statistical Parameters ◽  
Translation Invariant ◽  
Content Type

Purpose – The purpose of this paper is to investigate the condition of insulation in high-voltage equipments using partial discharge (PD) measurements. It proposes the methods to eliminate several noises like white noise, random noise and discrete spectral interferences which severely pollutes the PD signals. The study aims to remove these noises from the PD signal effectively by preserving the signal features. Design/methodology/approach – This paper employs fast Fourier transform, discrete wavelet transform and translational invariant wavelet transform (TIWT) for denoising the PD signals. The simulated damped exponential pulse and damped oscillatory pulse with low- and high-level noises and a measured PD signal are considered for this analysis. The conventional wavelet denoising approach is also improved by estimating the automated global optimum threshold value using genetic algorithm (GA). The statistical parameters are evaluated and compared. Among these methods, GA-based TIWT approach provides robustness and reduces computational complexity. Findings – This paper provides effective condition monitoring of power apparatus using GA-based TIWT approach. This method provides the low value of mean square error, pulse amplitude distortion and also high reduction in noise level due to its robustness and reduced computational complexity. It suggests that this approach works well for both signals immersed in noise as well as for noise immersed in signals. Research limitations/implications – Because of the chosen PD signals, the research results may lack for multiple discharges. Therefore, researchers are encouraged to test the proposed propositions further. Practical implications – The paper includes implication for the development of online testing for equipment analysis and diagnostics during normal operating condition. Corrective actions can be planned and implemented, resulting in reduced unscheduled downtime. Social implications – This PD-based analysis often present well in advance of insulation failure, asset managers can monitor it over time and make informed strategic decisions regarding the repair or replacement of the equipment. These predictive diagnostics help society to prioritize investments before an unexpected outage occurs. Originality/value – This paper provides an enhanced study of condition monitoring of HV power apparatus by which life time of insulation can be increased by taking preventive measures.

A novel kinetic modeling method for the stabilization phase of the composting process for biodegradation of solid wastes

2017 ◽  
Vol 35 (12) ◽  
pp. 1226-1236 ◽  
Author(s):  
Reza Ebrahimzadeh ◽  
Ahmad Ghazanfari Moghaddam ◽  
Mehdi Sarcheshmehpour ◽  
Hamid Mortezapour
Keyword(s):  
Moisture Content ◽  
Rate Constant ◽  
Degradation Kinetics ◽  
Life Time ◽  
Degradation Process ◽  
Second Order ◽  
Statistical Parameters ◽  
Elementary Reactions ◽  
Composting Process ◽  
Expected Time

Biomass degradation kinetics of the composting process for kitchen waste, pruned elm tree branches and sheep manure were studied to model changes in volatile solids (VS) over time. Three experimental reactors containing raw mixtures with a carbon to nitrogen (C/N) ratio of 27:1 and a moisture content of 65% were prepared. During the composting process two of the reactors used forced air and the third used natural aeration. The composting stabilization phases in all reactors were completed in 30 days. During this period, composting indexes such as temperature, moisture content and VS changes were recorded. Elementary reactions were used for kinetics modeling of the degradation process. Results showed that the numerical values of rate constant ( k) for zero-order ranged from 0.86 to 1.03 VS×day-1, for first-order models it ranged from 0.01 to 0.02 day-1, for second-order the range was from 1.36×10-5 to 1.78×10-5 VS-1×day-1 and for n-order the rate constant ranged from 0.031 to 0.095 VS(1-n)×day-1. The resulting models were validated by comparing statistical parameters. Evaluation of the models showed that, in the aerated reactors, the n-order models (less than 1) successfully estimated the VS changes. In the non-aeration reactor, for the second-order model good agreement was achieved between the simulated and actual quantities of VS. Also, half-life time provided a useful criterion for the estimation of expected time for completion of different phases of composting.

scholarly journals Turbulent diffusivity in the free atmosphere inferred from MST radar measurements: a review

2004 ◽  
Vol 22 (11) ◽  
pp. 3869-3887 ◽  
Author(s):  
R. Wilson
Keyword(s):  
Atmospheric Turbulence ◽  
Small Scale ◽  
Doppler Width ◽  
Free Atmosphere ◽  
Statistical Parameters ◽  
Actual Impact ◽  
Dissipation Rates ◽  
Mst Radar ◽  
Radar Measurements ◽  
Scale Turbulence

Abstract. The actual impact on vertical transport of small-scale turbulence in the free atmosphere is still a debated issue. Numerous estimates of an eddy diffusivity exist, clearly showing a lack of consensus. MST radars were, and continue to be, very useful for studying atmospheric turbulence, as radar measurements allow one to estimate the dissipation rates of energy (kinetic and potential) associated with turbulent events. The two commonly used methods for estimating the dissipation rates, from the backscattered power and from the Doppler width, are discussed. The inference methods of a local diffusivity (local meaning here "within" the turbulent patch) by using the dissipation rates are reviewed, with some of the uncertainty causes being stressed. Climatological results of turbulence diffusivity inferred from radar measurements are reviewed and compared. As revealed by high resolution MST radar measurements, atmospheric turbulence is intermittent in space and time. Recent theoretical works suggest that the effective diffusivity of such a patchy turbulence is related to statistical parameters describing the morphology of turbulent events: filling factor, lifetime and height of the patches. It thus appears that a statistical description of the turbulent patches' characteristics is required in order to evaluate and parameterize the actual impact of small-scale turbulence on transport of energy and materials. Clearly, MST radars could be an essential tool in that matter.

Deficits and Recovery of First-Order and Second-Order Motion Perception in Patients with Unilateral Posterior Parietal Lesions

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 22-22 ◽  
Author(s):  
D Braun ◽  
M Fahle ◽  
P Schönle ◽  
J Zanker
Keyword(s):  
Motion Perception ◽  
Time Course ◽  
Random Noise ◽  
Second Order ◽  
The Other ◽  
Rectangular Region ◽  
First Order ◽  
Posterior Parietal ◽  
Short Time ◽  
Visual Hemifields

Our aim was to test whether unilateral posterior parietal lesions degrade first-order and second-order motion differentially, and to investigate the time course of any potential recovery. We tested ten patients with circumscribed parietal lesions. Thresholds were measured for the discrimination of the direction of motion of stimuli presented 5.5° peripherally in the ipsilesional and contralesional visual hemifields. Subjects had to indicate whether a rectangular region (1.6 deg × 3 deg) embedded in dynamic random noise background moved up or down. The region contained moving (signal) and flickering (background) dots and moved for 1 s at 2.36 deg s−1. Signal dots were either (a) coherently moving in the same direction as the region (first-order), (b) stationary (second-order), or (c) coherently moving in the opposite direction (theta). Thresholds were defined as percentage of signal dots within the region yielding 75% correct responses. All patients had higher thresholds for second-order than for first-order motion. When contralesional and ipsilesional thresholds were compared, three patients showed proportional threshold elevations for all three types of motion stimuli in the contralesional hemifield. Two of these three patients were tested again five months later. Both showed considerable recovery: in one patient, the contralesional deficit was no longer present; in the other, it was reduced by about half. None of our patients had lesions affecting first-order or second-order motion differentially; lesions always affected first-order and second-order motion similarly. Owing to recovery, these deficits might be detectable only for a short time.

Classification of Brodatz Dataset Samples (36-64) Through Second-Order Statistical Parameters (B)

2020 ◽  
Author(s):  
Amit Shakya ◽  
Ayushman Ramola ◽  
Prag Mittal ◽  
Anurag Vidyarthi ◽  
Rishi Prakash
Keyword(s):  
Second Order ◽  
Statistical Parameters

SOLVING (n×n) LINEAR EQUATIONS WITH PARAMETERS COVARIANCES

2004 ◽  
Vol 01 (03) ◽  
pp. 471-489
Author(s):  
PEERAYUTH CHARNSETHIKUL
Keyword(s):  
Gaussian Distribution ◽  
Numerical Models ◽  
Symmetric Matrix ◽  
Linear Equations ◽  
Matrix Multiplication ◽  
Second Order ◽  
Cholesky Factorization ◽  
Statistical Parameters ◽  
The Matrix ◽  
Non Gaussian

The aim of this paper is to propose numerical models and methods for solving (n×n) linear equations, AX=b where parameters in the matrix A and the vector b are random and correlated. In case of a probabilistic b and a deterministic A, the amount of second order co-variances computation is equivalent to solving the (n×n) problem sequentially n times with additional efforts of performing a Cholesky factorization plus a symmetric matrix multiplication of size n×n while the complexity for kth order correlation can be proven to be O(nk+1). For randomness in both A and b with an assumption of multidimensional Gaussian distribution, two systems of linear equations with size n2(n+1) and n(n+1)/2 are derived and can be solved sequentially to obtain the desired statistical parameters up to the second order of X. Both approaches are coded as Matlab M-files, complied and run to test their efficiency. For higher order correlation with non-Gaussian distribution, an approximation scheme is proposed. Two applications in optimization are discussed.

Disappearance Voltage Determinations Using a Convergent Beam

1971 ◽  
Vol 29 ◽  
pp. 184-185
Author(s):  
W. L. Bell
Keyword(s):  
Second Order ◽  
Objective Method ◽  
Uniform Thickness ◽  
Rocking Curve ◽  
Crystal Perfection ◽  
Kikuchi Line ◽  
Central Maximum ◽  
Diffraction Patterns ◽  
Convergent Beam ◽  
Beam Technique

Disappearance voltages for second order reflections can be determined experimentally in a variety of ways. The more subjective methods, such as Kikuchi line disappearance and bend contour imaging, involve comparing a series of diffraction patterns or micrographs taken at intervals throughout the disappearance range and selecting that voltage which gives the strongest disappearance effect. The estimated accuracies of these methods are both to within 10 kV, or about 2-4%, of the true disappearance voltage, which is quite sufficient for using these voltages in further calculations. However, it is the necessity of determining this information by comparisons of exposed plates rather than while operating the microscope that detracts from the immediate usefulness of these methods if there is reason to perform experiments at an unknown disappearance voltage.The convergent beam technique for determining the disappearance voltage has been found to be a highly objective method when it is applicable, i.e. when reasonable crystal perfection exists and an area of uniform thickness can be found. The criterion for determining this voltage is that the central maximum disappear from the rocking curve for the second order spot.

A method for direct measurement of specimen noise in HREM images

1993 ◽  
Vol 51 ◽  
pp. 458-459
Author(s):  
Sidnei Paciornik ◽  
Roar Kilaas ◽  
Ulrich Dahmen ◽  
Michael Adrian O'Keefe
Keyword(s):  
Random Noise ◽  
Image Interpretation ◽  
Thin Foil ◽  
High Resolution Electron Microscopy ◽  
Black Dot ◽  
Atomic Column ◽  
Amorphous Oxide ◽  
Resolution Electron ◽  
Imaging Conditions ◽  
Image Simulations

High resolution electron microscopy (HREM) is a primary tool for studying the atomic structure of defects in crystals. However, the quantitative analysis of defect structures is often seriously limited by specimen noise due to contamination or oxide layers on the surfaces of a thin foil.For simple monatomic structures such as fcc or bcc metals observed in directions where the crystal projects into well-separated atomic columns, HREM image interpretation is relatively simple: under weak phase object, Scherzer imaging conditions, each atomic column is imaged as a black dot. Variations in intensity and position of individual image dots can be due to variations in composition or location of atomic columns. Unfortunately, both types of variation may also arise from random noise superimposed on the periodic image due to an amorphous oxide or contamination film on the surfaces of the thin foil. For example, image simulations have shown that a layer of amorphous oxide (random noise) on the surfaces of a thin foil of perfect crystalline Si can lead to significant shifts in image intensities and centroid positions for individual atomic columns.

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