Simulation of Normal Differentiable Stochastic Process of Ice Loads

2017 ◽  
Author(s):  
Petr Zvyagin
Keyword(s):  
Stochastic Process ◽  
Autocorrelation Function ◽  
Critical Level ◽  
Process Approach ◽  
The Other ◽  
Normal Process ◽  
Expected Number ◽  
Stationary Model ◽  
Tank Experiment ◽  
Ice Loads

Ice loads time series should be treated in the other way than separate independent ice loads observations. The stochastic process approach can provide information about such important characteristic as mean length of signal’s outcome beyond some critical level and expected number of such outcomes. The paper considers global ice loads registered in an ice tank experiment with a cylindrical indenter of 100 mm width. The autocorrelation function is fitted in a manner that the observed load process is differentiable. The study conducted in the paper demonstrates that characteristics, such as the number outcomes beyond some critical level and the time spent off this level, are governed in the same way as parameters of a stationary differentiable normal process. Normal stationary model of ice loads process allows its simulation, if autocorrelation function is given. In the paper, such simulation is performed.

Study of Local Ice Loads Measured at Norströmsgund Lighthouse

2017 ◽  
Author(s):  
Petr Zvyagin ◽  
Gesa Ziemer
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Stationary Process ◽  
The Other ◽  
Stationary Time Series ◽  
Distribution Law ◽  
Local Loads ◽  
Simultaneous Record ◽  
Key Factor ◽  
Ice Loads

It is believed that ice loading can be a stationary process at least sometimes during the state of continuous brittle crushing. Confidence in the distribution law, stationarity in time, and autocorrelation function of local ice loads is the key factor for assessment of such loads and their successful simulation. Good understanding of the load process on the level of a single transducer record can be helpful in future analysis and simulation of loads on wider contact areas. In this paper local loads, simultaneously measured by two middle subpanels at the Norströmsgrund lighthouse in March 2001, are studied. Stationary time series of lognormal origin of 50 seconds duration are extracted from both of the subpanel records. From the studied data, stationarity was not observed simultaneously at different subpanels. The correlation of one stationary subpanel record with simultaneous record of the other subpanel found to be weak. A simple function with good fit to the observed autocorrelation curve of stationary load fragments is suggested. The findings are compared with parameters obtained for local loads in previous studies. A transition from autocorrelation function for raw lognormal data to autocorrelation function of logarithmic normal data is performed.

Analysis and Probabilistic Modeling of the Stationary Ice Loads Stochastic Process With Lognormal Distribution

2014 ◽  
Author(s):  
Petr Zvyagin ◽  
Kirill Sazonov
Keyword(s):  
Stochastic Process ◽  
Stochastic Processes ◽  
Normal Distribution ◽  
Autocorrelation Function ◽  
Strain Gauge ◽  
Lognormal Distribution ◽  
Probabilistic Modeling ◽  
Stationary Stochastic Process ◽  
Strain Gauge Transducer ◽  
Ice Loads

Until recent times researchers who investigated ice loads stochastic processes usually stated the fact of normal distribution for them. In the paper the model of a stationary stochastic process with a lognormal distribution for ice loads is offered. This model relates to the strain gauge transducer ice loads measurements as well as to some examples considered in different papers that were published earlier. For this model dependencies of the autocorrelation function were found that allows to simulate the ice loads process relatively easily. The procedure of such a simulation is described in details and the example of the analysis and simulation ice loads measurements is provided.

scholarly journals Maximization of availability of 1-out-of-2:G repairable dependent system

1989 ◽  
Vol 21 (03) ◽  
pp. 717-720
Author(s):  
B. H. Joshi ◽  
A. D. Dharmadhikari
Keyword(s):  
Stochastic Process ◽  
Steady State ◽  
Lower Bound ◽  
Upper Bounds ◽  
The Other ◽  
Expected Number ◽  
Component Failure ◽  
Component System ◽  
Component Failure Rates ◽  
Repair Facility

The IFR property of the stochastic process governing a one-component system supported by an inactive standby and a repair facility when the lifetime of one component and the repair time of the other component are dependent, is established. We solve the problem of selecting repair rates to maximize the steady-state availability for given component failure rates when a lower bound for the MTBF and upper bounds for the steady-state expected number of repairs of the components per unit time and expected number of failures of the system per unit time are given.

scholarly journals Maximization of availability of 1-out-of-2:G repairable dependent system

1989 ◽  
Vol 21 (3) ◽  
pp. 717-720 ◽  
Author(s):  
B. H. Joshi ◽  
A. D. Dharmadhikari
Keyword(s):  
Stochastic Process ◽  
Steady State ◽  
Lower Bound ◽  
Upper Bounds ◽  
The Other ◽  
Expected Number ◽  
Component Failure ◽  
Component System ◽  
Component Failure Rates ◽  
Repair Facility

The IFR property of the stochastic process governing a one-component system supported by an inactive standby and a repair facility when the lifetime of one component and the repair time of the other component are dependent, is established. We solve the problem of selecting repair rates to maximize the steady-state availability for given component failure rates when a lower bound for the MTBF and upper bounds for the steady-state expected number of repairs of the components per unit time and expected number of failures of the system per unit time are given.

scholarly journals Activation of the lac Repressor in the Transgenic Mouse

Genetics ◽  
1997 ◽  
Vol 147 (1) ◽  
pp. 297-304
Author(s):  
Heidi Scrable ◽  
Peter J Stambrook
Keyword(s):  
Copy Number ◽  
Methylation Status ◽  
Lac Repressor ◽  
The Other ◽  
Lac Operon ◽  
Normal Process ◽  
Divergent Sequence ◽  
Somatic Tissues ◽  
New Material ◽  
Bacterial Sequence

Abstract We have introduced sequences encoding the lac repressor of Escherichia coli into the genome of the mouse. One sequence was derived from the bacterial lac operon and the other was created by reencoding the amino acid sequence of lacI with mammalian codons. Both versions are driven by an identical promoter fragment derived from the human β-actin locus and were microinjected into genetically identical pronuclear stage embryos. All transgenes utilizing the bacterial coding sequence were transcriptionally silent in all somatic tissues tested. The sequence re-encoded with mammalian codons was transcriptionally active at all transgene loci and expressed ubiquitously. Using methylation-sensitive enzymes, we have determined the methylation status of lac repressor transgenes encoded by either the bacterial or mammalian sequence. The highly divergent bacterial sequence was hypermethylated at all transgene loci, while the mammalian sequence was only hypermethylated at a high copy number locus. This may reflect a normal process that protects the genome from acquiring new material that has an abnormally divergent sequence or structure.

scholarly journals Stochastic Processes with a Particular Type of Variograms

2007 ◽  
Vol 2007 ◽  
pp. 1-5 ◽  
Author(s):  
Chunsheng Ma
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
Stochastic Processes ◽  
Series Expansion ◽  
Real Line ◽  
Random Fields ◽  
The Other ◽  
Simple Construction ◽  
The Real ◽  
Line Form

This paper is concerned with a class of stochastic processes or random fields with second-order increments, whose variograms have a particular form, among which stochastic processes having orthogonal increments on the real line form an important subclass. A natural issue, how big this subclass is, has not been explicitly addressed in the literature. As a solution, this paper characterizes a stochastic process having orthogonal increments on the real line in terms of its variogram or its construction. Our findings are a little bit surprising: this subclass is big in terms of the variogram, and on the other hand, it is relatively “small” according to a simple construction. In particular, every such process with Gaussian increments can be simply constructed from Brownian motion. Using the characterizations we obtain a series expansion of the stochastic process with orthogonal increments.

scholarly journals Blood Perfusion of the Kidney of Lophius Piscatorius L.

1953 ◽  
Vol 32 (2) ◽  
pp. 329-336 ◽  
Author(s):  
L. Brull ◽  
E. Nizet ◽  
E. B. Verney
Keyword(s):  
Blood Flow ◽  
Critical Level ◽  
Perfusion Pressure ◽  
Blood Perfusion ◽  
Urine Flow ◽  
The Other ◽  
Protein Nitrogen ◽  
Other Hand ◽  
Flow Through

Lophius kidneys perfused with the heparinized blood (venous) of the fish secrete urine in which total non-protein nitrogen is concentrated, magnesium highly concentrated, and chloride only slightly so or not at all. Oxygenation of the blood, or lowering the temperature of the perfusate from c. 20° to c. 5° C. does not appear to influence secretion. The blood flow through the kidneys increases with the perfusion pressure, the increase often becoming disproportionately large. The urine flow, on the other hand, above a certain critical level is largely independent of changes in perfusion pressure.

scholarly journals A stochastic process approach to false discovery control

2004 ◽  
Vol 32 (3) ◽  
pp. 1035-1061 ◽  
Author(s):  
Christopher Genovese ◽  
Larry Wasserman
Keyword(s):  
Stochastic Process ◽  
Process Approach ◽  
False Discovery

scholarly journals A Markov Model for Production and Maintenance Decision

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mohammad Saber Fallahnezhad ◽  
Alie Ranjbar ◽  
Faeze Zahmatkesh Saredorahi
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
The Other ◽  
Catastrophic Failure ◽  
Expected Number ◽  
Maintenance Decision

In this paper, we consider a production machine which may fail and it is necessary to repair the machine after each failure and there are two statuses for each repair; in one case, we should replace the machine because of catastrophic failure and in the other case, only small repairs are needed. Times between failures and repair and replace times are random and demands are satisfied by inventory during repairing and replacing the machine; shortage level is limited. We model described system as a Markov chain and develop an algorithm to compute the expected number of transitions among states.

Sign in / Sign up

Export Citation Format

Share Document