Applied Probability and Stochastic Processes

Feldman, Richard M.; Valdez‐Flores, Ciriaco

doi:10.1007/978-3-642-05158-6

Cited by 107 publications

(69 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Referring to Eq. (B3), since both the particle sizes D ij and the number of particles Np i,tot are realizations of random variables, the form of N (D i ) is seen to be that of a random sum of random variables (Feldman and Valdez-Flores, 2010), also known as a randomly stopped sum.…”

Section: B2 Sampling Uncertainties For D Svifi and N D Svifimentioning

confidence: 99%

“…the variance of y i can be shown to be V y i = V x j E Np i,tot + E x j 2 V Np i,tot , (B11) (Feldman and Valdez-Flores, 2010) by applying the law of total variance, where V [] indicates variance and E[] indicates expectation. Since Np i,tot is Poisson-distributed, the best estimate of the expectation and variance is the observed count, so that V y i = Np i,tot V x j + Np i,tot E x j 2 .…”

Section: B2 Sampling Uncertainties For D Svifi and N D Svifimentioning

confidence: 99%

See 1 more Smart Citation

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

et al. 2013

View full text Add to dashboard Cite

Abstract. Estimates of snow microphysical properties obtained by analyzing collections of individual particles are often limited to short timescales and coarse time resolution. Retrievals using disdrometer observations coincident with bulk measurements such as radar reflectivity and snowfall amounts may overcome these limitations; however, retrieval techniques using such observations require uncertainty estimates not only for the bulk measurements themselves, but also for the simulated measurements modeled from the disdrometer observations. Disdrometer uncertainties arise due to sampling and analytic errors and to the discrete, potentially truncated form of the reported size distributions. Imaging disdrometers such as the Snowflake Video Imager and 2-D Video Disdrometer provide remarkably detailed representations of snow particles, but view limited projections of their three-dimensional shapes. Particle sizes determined by such instruments underestimate the true dimensions of the particles in a way that depends, in the mean, on particle shape, also contributing to uncertainties. An uncertainty model that accounts for these uncertainties is developed and used to establish their contributions to simulated radar reflectivity and snowfall rate. Viewing geometry effects are characterized by a parameter, φ, that relates disdrometer-observed particle size to the true maximum dimension of the particle. Values and uncertainties for φ are estimated using idealized ellipsoidal snow particles. The model is applied to observations from seven snow events from the Canadian CloudSat/CALIPSO Validation Project (C3VP), a mid-latitude cold-season cloud and precipitation field experiment. Typical total uncertainties are 4 dB for reflectivity and 40-60 % for snowfall rate, are highly correlated, and are substantial compared to expected uncertainties for radar and precipitation gauge observations. The dominant sources of errors are viewing geometry effects and the discrete, truncated form of the size distributions. While modeled Ze-S relationships are strongly affected by assumptions about snow particle mass properties, such relationships are only modestly sensitive to φ owing to partially compensating effects on both the reflectivity and snowfall rate.

show abstract

Section: B2 Sampling Uncertainties For D Svifi and N D Svifimentioning

confidence: 99%

Section: B2 Sampling Uncertainties For D Svifi and N D Svifimentioning

confidence: 99%

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Fundamental facts for age replacements are included in papers [2,3]. A review of results connected with preventive replacements is to be found in papers [5,21,24]. Certain generalizations of the question of preventive replacements were arrived at in papers [12,13].…”

Section: Introductionmentioning

confidence: 99%

Multi-state model of maintenance policy

Knopik¹,

Migawa²

2018

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

Preventive replacement is applied to improve the device availability or increase the profit per unit time of the maintenance system. In this paper, we study age-replacement model of technical object for n-state system model. The criteria function applied in this paper describe profit per unit time or coefficient of availability. The probability distribution of a unit‘s failure time is assumed to be known, and preventive replacement strategy will be used over very long period of time. We investigate the problem of maximization of profit per unit time and coefficient availability for increasing the failure rate function of the lifetime and for a wider class of lifetime. The purpose of this paper is to obtain conditions under which the profit per unit time approaches a maximum. In this paper we shows that the criteria function (profit per unit time or coefficient availability) can be expressed using the matrix calculation method. Finally, a numerical example to evaluate an optimal replacement age is presented.

show abstract

“…It is expected that the pit depth in state i at a given time δt will remain in the state until a later time [32] however, it can move to another state j by passing through an arbitrary state h in s time (see Figure 2) whilst obeying the time-dependent probability condition of Chapman-Kolmogorov equation shown in Equation (11) [32]:…”

Section: Time Evolution Of Pit Depthmentioning

confidence: 99%

Markov chain modelling for time evolution of internal pitting corrosion distribution of oil and gas pipelines

Ossai

Boswell

Davies

2016

Engineering Failure Analysis

View full text Add to dashboard Cite

A continuous time non-homogenous linear growth pure birth Markov model was used to predict the future pit depth distribution of internally corroded oil and gas pipelines. A negative binomial distribution was used for calculating the transition probability functions of the pit depths whilst pit depths growth was estimated for low, moderate, high and severe pitting corrosion rates using field measured data of pit depths, temperatures, CO2 partial pressures, pH and flow rates. The Markov predicted results agreed well with field measured pit depth data from X52 grade pipeline and L-80 and N-80 grades offshore well tubing.

show abstract

Applied Probability and Stochastic Processes

Cited by 107 publications

References 0 publications

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

Multi-state model of maintenance policy

Markov chain modelling for time evolution of internal pitting corrosion distribution of oil and gas pipelines

Contact Info

Product

Resources

About