Develop Probability Distribution Function to Select Pavement Preventive Maintenance Materials and Methods

Preventive Maintenance ◽

Function Method ◽

Probability Function ◽

Useful Life ◽

Process Cost ◽

Maintenance Process

Maintenance and protection of road pavements are one of important tasks of highway agencies. Many studies are done about specifying suitable time for pavement preventive maintenance. The probability distribution isn't used in this study, although the significant factor in decision making is relevant to probability function. In present paper, the suitable time between two preventive repairs is determined by Erlangen probability distribution function method. Economical factors are one of important parameters in this method, so, maintenance and repairing process cost, the useful life of maintenance process and the cutting of traffic flow are applied in calculation of suggested method.

Analyzing Risk through Probabilistic Modeling in Operations Research - Advances in Logistics, Operations, and Management Science ◽

The Method of Probabilistic Nodes Combination in Decision Making and Risk Analysis

10.4018/978-1-4666-9458-3.ch002 ◽

2016 ◽

pp. 32-60

Author(s):

Dariusz Jacek Jakóbczak

Keyword(s):

Decision Making ◽

Distribution Function ◽

Risk Analysis ◽

Linear Interpolation ◽

Distribution Functions ◽

Probability Distribution Functions ◽

Characteristic Points ◽

Basic Probability

Proposed method, called Probabilistic Nodes Combination (PNC), is the method of 2D curve interpolation and extrapolation using the set of key points (knots or nodes). Nodes can be treated as characteristic points of data for modeling and analyzing. The model of data can be built by choice of probability distribution function and nodes combination. PNC modeling via nodes combination and parameter ? as probability distribution function enables value anticipation in risk analysis and decision making. Two-dimensional curve is extrapolated and interpolated via nodes combination and different functions as discrete or continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function. Novelty of the paper consists of two generalizations: generalization of previous MHR method with various nodes combinations and generalization of linear interpolation with different (no basic) probability distribution functions and nodes combinations.

Lecture Notes in Mechanical Engineering - Reliability and Risk Assessment in Engineering ◽

Comparative Study of Cepstral Editing and Unitary Sample Shifted Probability Distribution Function Method for Bearing Fault Diagnosis

10.1007/978-981-15-3746-2_15 ◽

2020 ◽

pp. 165-170

Author(s):

Ankush C. Jahagirdar ◽

Karunesh Kumar Gupta

Keyword(s):

Distribution Function ◽

Fault Diagnosis ◽

Comparative Study ◽

Function Method ◽

Bearing Fault ◽

Bearing Fault Diagnosis

The probability distribution function of structure factors with non-integral indices. III. The joint probability distribution in the P1¯ case

Acta Crystallographica Section A Foundations of Crystallography ◽

10.1107/s0108767398009994 ◽

1999 ◽

Vol 55 (2) ◽

pp. 322-331 ◽

Cited By ~ 6

Author(s):

Carmelo Giacovazzo ◽

Dritan Siliqi ◽

Angela Altomare ◽

Giovanni Luca Cascarano ◽

Rosanna Rizzi ◽

...

Keyword(s):

Distribution Function ◽

Function Method ◽

Joint Probability ◽

Joint Probability Distribution ◽

Conditional Distributions ◽

Structure Factors ◽

Joint Probability Distribution Function ◽

Atomic Parameters

The joint probability distribution function method has been developed in P1¯ for reflections with rational indices. The positional atomic parameters are considered to be the primitive random variables, uniformly distributed in the interval (0, 1), while the reflection indices are kept fixed. Owing to the rationality of the indices, distributions like P(F p 1 , F p 2 ) are found to be useful for phasing purposes, where p 1 and p 2 are any pair of vectorial indices. A variety of conditional distributions like P(|F p 1 | | |F p 2 |), P(|F p 1 | |F p 2 ), P(\varphi_{{\bf p}_1}|\,|F_{{\bf p}_1}|, F_{{\bf p}_2}) are derived, which are able to estimate the modulus and phase of F p 1 given the modulus and/or phase of F p 2 . The method has been generalized to handle the joint probability distribution of any set of structure factors, i.e. the distributions P(F 1, F 2,…, F n+1), P(|F 1| |F 2,…, F n+1) and P(\varphi1| |F|1, F 2,…, F_{n+1}) have been obtained. Some practical tests prove the efficiency of the method.

2010 IEEE International Conference on Industrial Engineering and Engineering Management ◽

Decision making of facility locations based on Fuzzy Probability Distribution Function

10.1109/ieem.2010.5674624 ◽

2010 ◽

Cited By ~ 3

Author(s):

P.-C. Lin ◽

S. Wang ◽

J. Watada

Keyword(s):

Decision Making ◽

Distribution Function ◽

Fuzzy Probability ◽

Facility Locations

INVESTIGATION OF SPONTANEOUS CHIRAL-SYMMETRY BREAKING FROM LATTICE QCD WITH MASSLESS QUARKS

Modern Physics Letters A ◽

10.1142/s0217732307023079 ◽

2007 ◽

Vol 22 (07n10) ◽

pp. 491-498

Author(s):

XIANG-QIAN LUO

Keyword(s):

Distribution Function ◽

Dirac Operator ◽

Lattice Qcd ◽

Function Method ◽

Chiral Symmetry Breaking ◽

Chiral Condensate ◽

Massless Limit ◽

Massless Dirac Operator

We apply the probability distribution function method to the study of chiral properties of QCD with quarks in the exact massless limit. A relation among the chiral condensate, zeros of the Bessel function and eigenvalue of Dirac operator is also given. The chiral condensate in this limit can be measured with small number of eigenvalues of the massless Dirac operator and without any ambiguous mass extrapolation. Results for lattice QCD with Kogut-Susskind quarks are shown.

Probabilistic Nodes Combination (PNC) for Object Modeling and Contour Reconstruction - Advances in Systems Analysis, Software Engineering, and High Performance Computing ◽

PNC in 3D Surface Modeling

10.4018/978-1-5225-2531-8.ch005 ◽

2017 ◽

pp. 178-208

Keyword(s):

Decision Making ◽

Distribution Function ◽

Feature Space ◽

Distribution Functions ◽

High Dimensional ◽

Two Dimensional ◽

Data Interpolation ◽

3D Surface Modeling

The model of data can be built by choice of probability distribution function and nodes combination. PFC modeling via nodes combination and parameter ? as probability distribution function enables value anticipation in risk analysis and decision making. Two-dimensional curve is extrapolated and interpolated via nodes combination and different functions as discrete or continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function. The method of Probabilistic Features Combination (PFC) enables interpolation and modeling of high-dimensional data using features' combinations and different coefficients ? as modeling function. Functions for ? calculations are chosen individually at each data modeling and it is treated as N-dimensional probability distribution function: ? depends on initial requirements and features' specifications. PFC method leads to data interpolation as handwriting or signature identification and image retrieval via discrete set of feature vectors in N-dimensional feature space.