Study of Solving Crossing Origin-Destination Matrix Based on Entropy Maximizing Model

2012 ◽  
Vol 182-183 ◽  
pp. 970-974
Author(s):  
Guo Jiang Fu
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Maximum Entropy ◽  
Nonlinear Equations ◽  
Lagrange Multiplier ◽  
Programming Model ◽  
Nonlinear Integer Programming ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Flow Matrix

The maximum-entropy model is one of important methods in estimating traffic origin-destination matrix from observed traffic link flows, and it is a nonlinear integer programming model. To find the best solution, traditionally it was transformed to solve nonlinear equations by the introduction of Lagrange multiplier and Newton’s method is adopted to solve the nonlinear equations. In this paper, a entropy maximizing model to estimate the crossing origin-destination flow matrix from in-out flows is given, a genetic algorithm is proposed to solve the model and the introduction of Lagrange multiplier is avoid. A practical example showed the validity of the genetic algorithm.

scholarly journals Gauging flow velocity in 2 positions for discharge calculation in pipes

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Victor Emanuel Mello de Guimarães Diniz ◽  
Edevar Luvizotto Júnior ◽  
Podalyro Amaral de Souza ◽  
Pedro Alves Silva
Keyword(s):  
Genetic Algorithm ◽  
Maximum Entropy ◽  
Circular Cross Section ◽  
Venturi Tube ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Water Mains ◽  
In Series ◽  
Electromagnetic Flow Meter ◽  
Two Parameters

ABSTRACT This paper utilizes the maximum entropy model to calculate discharges in pipes. The proposed model requires the flow velocities to be gauged in just two positions along the pipe radius to calculate the discharge of any given pipe with circular cross-section regardless its diameter size. A genetic algorithm is used to determine the two parameters of the entropy equation for pipe flow. Three water mains are assessed. The discharge values achieved by the maximum entropy model coupled to the genetic algorithm for two water mains are compared to those achieved by a calibrated AquaProbe ABB electromagnetic flow meter and remain within the device accuracy (± 2%), as reported by its manufacturer. A Cole type Pitot tube in series with a Venturi tube are used to respectively define three velocity profiles and gauge three different discharges for the third water main. The three discharge values obtained by the maximum entropy model are compared to the ones obtained by the Venturi tube and remain within the presented uncertainties (3.3%, 3.1% and 2.8%) for each discharge gauged by the Venturi tube. The discharge calculation in any given pipe is facilitated by the presented method.

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

Video segmentation using Maximum Entropy Model

2005 ◽  
Vol 6 (S1) ◽  
pp. 47-52
Author(s):  
Li-juan Qin ◽  
Yue-ting Zhuang ◽  
Yun-he Pan ◽  
Fei Wu
Keyword(s):  
Maximum Entropy ◽  
Video Segmentation ◽  
Maximum Entropy Model ◽  
Entropy Model
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