Probability distribution for the number of cycles between successive regime transitions for the Lorenz model

2007 ◽  
Vol 233 (1) ◽  
pp. 14-20 ◽  
Author(s):  
A.K. Mittal ◽  
S. Dwivedi ◽  
R.S. Yadav
Keyword(s):  
Probability Distribution ◽  
Lorenz Model ◽  
Number Of Cycles ◽  
Regime Transitions

scholarly journals Energy-based predictions in Lorenz system by a unified formalism and neural network modelling

2010 ◽  
Vol 17 (6) ◽  
pp. 809-815 ◽  
Author(s):  
A. Pasini ◽  
R. Langone ◽  
F. Maimone ◽  
V. Pelino
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Lorenz System ◽  
Lorenz Model ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
Regime Transitions

Abstract. In the framework of a unified formalism for Kolmogorov-Lorenz systems, predictions of times of regime transitions in the classical Lorenz model can be successfully achieved by considering orbits characterised by energy or Casimir maxima. However, little uncertainties in the starting energy usually lead to high uncertainties in the return energy, so precluding the chance of accurate multi-step forecasts. In this paper, the problem of obtaining good forecasts of maximum return energy is faced by means of a neural network model. The results of its application show promising results.

THE DISTRIBUTION OF DISTANCES BETWEEN RANDOMLY CONSTRUCTED GENOMES: GENERATING FUNCTION, EXPECTATION, VARIANCE AND LIMITS

2008 ◽  
Vol 06 (01) ◽  
pp. 23-36 ◽  
Author(s):  
WEI XU
Keyword(s):  
Probability Distribution ◽  
Generating Function ◽  
Chromosomal Rearrangement ◽  
Genomic Distance ◽  
Exact Probability ◽  
Breakpoint Graph ◽  
Exact Probability Distribution ◽  
Number Of Cycles ◽  
Linear Chromosomes ◽  
Large Repertoire

Based on a large repertoire of chromosomal rearrangement operations, the genomic distance d between two genomes with χr and χb linear chromosomes, respectively, both containing the same (or orthologous) n genes or markers, is d = n + max (χr,χb) - c, where c is the number of cycles in the breakpoint graph of the two genomes. In this paper, we study the exact probability distribution of c. We derive the expectation and variance, and show that, in the limit, the expectation of d is [Formula: see text].

Experimental Research on Residual Strength of Recycled Aggregate Concrete under Compressive Fatigue Loading

2010 ◽  
Vol 150-151 ◽  
pp. 1379-1382 ◽  
Author(s):  
Hong Bing Zhu ◽  
Xiu Li
Keyword(s):  
Probability Distribution ◽  
Experimental Research ◽  
Residual Strength ◽  
Functional Relation ◽  
Fatigue Loading ◽  
Strength Degradation ◽  
Recycled Aggregate Concrete ◽  
Recycled Aggregate ◽  
Aggregate Concrete ◽  
Number Of Cycles

To investigate the residual strength degradation of recycled aggregate concrete under fatigue loading, experiments were conducted to determine the functional relation between residual strength and the number of cycles. Fifty 100mm ×100mm ×100mm specimens of recycled aggregate concrete were tested under uniaxial compressive fatigue loading. Based on probability distribution of the residual strength of concrete under fatigue loading, the P-R-N curves are obtained based on test data, the empirical expressions of the residual strength corresponding to the number of cycles were obtained. The curves can be used to predict the residual strength with reliability.

Determination of the Probability Distribution of the Mean Sound Level

2010 ◽  
Vol 35 (4) ◽  
pp. 543-550 ◽  
Author(s):  
Wojciech Batko ◽  
Bartosz Przysucha
Keyword(s):  
Probability Distribution ◽  
Probability Distribution Function ◽  
Mean Value ◽  
Sound Level ◽  
Function Form ◽  
Noise Levels ◽  
Logarithmic Mean ◽  
The Mean ◽  
Estimation Of Uncertainty

AbstractAssessment of several noise indicators are determined by the logarithmic mean <img src="/fulltext-image.asp?format=htmlnonpaginated&src=P42524002G141TV8_html\05_paper.gif" alt=""/>, from the sum of independent random resultsL1;L2; : : : ;Lnof the sound level, being under testing. The estimation of uncertainty of such averaging requires knowledge of probability distribution of the function form of their calculations. The developed solution, leading to the recurrent determination of the probability distribution function for the estimation of the mean value of noise levels and its variance, is shown in this paper.

Increase Power Efficientand Reliability of Electrosupply of Consumers of Low Power

GIS Business ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. 42-52
Author(s):  
Sadullayev Nasillo Nematovich ◽  
Safarov Alisher Bekmurodovich ◽  
Nematov Shuhrat Nasilloyevich ◽  
Mamedov Rasul Akif- Ogli
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Low Power ◽  
Electric Power ◽  
Energy Sources ◽  
Wind Flow ◽  
Wind Rose ◽  
Energy Potential ◽  
Wind Power Installation ◽  
Wind Energy Potential

This article assesses the wind speed data and wind energy potential in the Bukhara region of Uzbekistan. In article it is stated a principle construction "hybrid" a source of the electric power consisting from wind power installation with mechanical store of energy, the solar panel with аккумулятор in common working with an electric network. The speed and direction of the wind measured at a height of 10 m were analyzed by the Weibull probability distribution functionTo determine the direction of wind flow (wind rose), a graph in Matlab environment was constructed. The method of an estimation energy of efficiency of the objects eating from several energy sources is offered. It is proved efficiency of application of such source of the electric power low power consumers

The generalized regularities of damageability and integrity in estimations of the endurance in conditions of variability of loading regimes

2019 ◽  
Vol 85 (9) ◽  
pp. 61-65
Author(s):  
N. A. Makhutov
Keyword(s):  
Life Time ◽  
Cyclic Strength ◽  
Laboratory Diagnostics ◽  
Methodological Issues ◽  
Break Points ◽  
Number Of Cycles ◽  
Cyclic Damage ◽  
Damage Summation ◽  
Temperature Factors

We consider and analyze general methodological issues regarding the strength and endurance (life-time) of the materials and structure elements under a combined effect of various force, deformation and temperature factors. The Journal "Zavodskaya laboratoriya. Diagnostika materialov" (Industrial laboratory. Diagnostics of materials) has launched systematic publications on this problematic since 2018. For many decades, domestic and foreign laboratory studies have gleaned to a traditional methodology for obtaining initial curves of the long-term and cyclic strength that related the breaking stresses with time or number of cycles. These curves, with the characteristic sections and break points, separating the areas of elastic and inelastic (plastic strain or creep strain) strain, are used in analysis of long-term and cyclic damage. Using the elementary linear law of damage summation, it is possible to calculate at a first approximation the strength and endurance under varying conditions of loading. Stepping up the requirements to the accuracy of calculations necessitates a transition from force fracture criteria (at stresses a) to deformation criteria (in elastic and inelastic deformations e). Thus, it becomes possible to construct and use a unified expression for the curve of the long-term cyclic fracture (taking into account the temporal x and cyclic N factors) and a long-term cyclic damage. With such approach it is possible to remain the linear law of damage summation though those damages are obviously nonlinear. The goal of the study is to continue and support the discussion of the most complex problems of a comprehensive assessment of the strength, resource, survivability and safety of high-risk engineering equipment within the journal pages.

Prediction of Haemoglobin Level by Some Probability Distribution

2020 ◽  
Vol 9 (1) ◽  
pp. 84-88
Author(s):  
Govinda Prasad Dhungana ◽  
Laxmi Prasad Sapkota
Keyword(s):  
Probability Distribution ◽  
Normal Distribution ◽  
Goodness Of Fit ◽  
Continuous Variable ◽  
Hemoglobin Level ◽  
Chi Square ◽  
Chi Square Test ◽  
Log Normal ◽  
Two Parameters ◽  
Best Fit

 Hemoglobin level is a continuous variable. So, it follows some theoretical probability distribution Normal, Log-normal, Gamma and Weibull distribution having two parameters. There is low variation in observed and expected frequency of Normal distribution in bar diagram. Similarly, calculated value of chi-square test (goodness of fit) is observed which is lower in Normal distribution. Furthermore, plot of PDFof Normal distribution covers larger area of histogram than all of other distribution. Hence Normal distribution is the best fit to predict the hemoglobin level in future.

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