A Novel Methodology for Combined Parameter and Function Estimation Problems

2009 ◽  
Author(s):  
Ali Hakkaki-Fard ◽  
Hosein Molavi ◽  
Ramin K. Rahmani
Keyword(s):  
Measurement Errors ◽  
Present Method ◽  
Adjoint Problem ◽  
Limiting Factor ◽  
Function Estimation ◽  
Estimation Problems ◽  
Convergence Performance ◽  
Complete Set ◽  
And Function ◽  
Correlated Parameters

This article presents a novel methodology, which is highly efficient and simple to implement, for simultaneous retrieval of a complete set of thermal coefficients in combined parameter and function estimation problems. Moreover, the effect of correlated parameters on convergence performance is examined. The present methodology is a combination of two different methods: The Conjugate Gradient Method with Adjoint Problem (CGMAP) and Box-Kanemasu method (BKM). The methodology uses the benefit of CGMAP in handling function estimation problems and BKM for parameter estimation problems. One of the unique features about the present method is that the correlation among the separate unknowns does not behave as a limiting factor to the convergence of the problem. Numerical experiments using measurement errors are performed to verify the proposed method in solving the combined parameter and function estimation problems. The obtained results show that the combined procedure can efficiently and reliably estimate the values of the thermal coefficients.

A Novel Methodology for Combined Parameter and Function Estimation Problems

2010 ◽  
Vol 132 (12) ◽  
Author(s):  
Hosein Molavi ◽  
Ali Hakkaki-Fard ◽  
Ramin K. Rahmani ◽  
Anahita Ayasoufi ◽  
Mehdi Molavi
Keyword(s):  
Measurement Errors ◽  
Present Method ◽  
Numerical Experiments ◽  
Adjoint Problem ◽  
Function Estimation ◽  
Combined Procedure ◽  
Estimation Problems ◽  
Convergence Performance ◽  
Complete Set ◽  
And Function

This article presents a novel methodology, which is highly efficient and simple to implement, for simultaneous retrieval of a complete set of thermal coefficients in combined parameter and function estimation problems. Moreover, the effect of correlated unknown variables on convergence performance is examined. The present methodology is a combination of two different classical methods: The conjugate gradient method with adjoint problem (CGMAP) and Box–Kanemasu method (BKM). The methodology uses the benefit of CGMAP in handling function estimation problems and BKM for parameter estimation problems. One of the unique features about the present method is that the correlation among the separate unknowns does not disrupt the convergence of the problem. Numerical experiments using measurement errors are performed to verify the efficiency of the proposed method in solving the combined parameter and function estimation problems. The results obtained by the present approach show that the combined procedure can efficiently and reliably estimate the values of the unknown thermal coefficients.

scholarly journals Kernel methods in system identification, machine learning and function estimation: A survey

Automatica ◽  
2014 ◽  
Vol 50 (3) ◽  
pp. 657-682 ◽  
Author(s):  
Gianluigi Pillonetto ◽  
Francesco Dinuzzo ◽  
Tianshi Chen ◽  
Giuseppe De Nicolao ◽  
Lennart Ljung
Keyword(s):  
Machine Learning ◽  
System Identification ◽  
Kernel Methods ◽  
Function Estimation ◽  
And Function

scholarly journals Quantitative Monitoring of Dynamic Blood Flows Using Coflowing Laminar Streams in a Sensorless Approach

2021 ◽  
Vol 11 (16) ◽  
pp. 7260
Author(s):  
Yang Jun Kang
Keyword(s):  
Blood Flow ◽  
Flow Rate ◽  
Blood Viscosity ◽  
Measurement Errors ◽  
Present Method ◽  
Test Fluid ◽  
Constant Flow Rate ◽  
Rbc Aggregation ◽  
Sinusoidal Flow ◽  
Analytical Expressions

Determination of blood viscosity requires consistent measurement of blood flow rates, which leads to measurement errors and presents several issues when there are continuous changes in hematocrit changes. Instead of blood viscosity, a coflowing channel as a pressure sensor is adopted to quantify the dynamic flow of blood. Information on blood (i.e., hematocrit, flow rate, and viscosity) is not provided in advance. Using a discrete circuit model for the coflowing streams, the analytical expressions for four properties (i.e., pressure, shear stress, and two types of work) are then derived to quantify the flow of the test fluid. The analytical expressions are validated through numerical simulations. To demonstrate the method, the four properties are obtained using the present method by varying the flow patterns (i.e., constant flow rate or sinusoidal flow rate) as well as test fluids (i.e., glycerin solutions and blood). Thereafter, the present method is applied to quantify the dynamic flows of RBC aggregation-enhanced blood with a peristaltic pump, where any information regarding the blood is not specific. The experimental results indicate that the present method can quantify dynamic blood flow consistently, where hematocrit changes continuously over time.

scholarly journals Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels

2021 ◽  
Author(s):  
Yining Wang ◽  
Yi Wu ◽  
Simon S. Du
Keyword(s):  
Operations Management ◽  
Polynomial Regression ◽  
Linear Time ◽  
Big Data Analytics ◽  
Function Estimation ◽  
Local Polynomial ◽  
Efficiency And Effectiveness ◽  
Exploratory Data ◽  
And Function ◽  
Novel Applications

Summary of Contribution: Big data analytics has become essential for modern operations research and operations management applications. Statistics methods, such as nonparametric density and function estimation, play important roles in predictive and exploratory data analysis for economics and operations management problems. In this paper, we concentrate on efficiently computing local polynomial regression estimates. We significantly accelerate the computation of such local polynomial estimates by novel applications of multidimensional binary indexed trees ( Fenwick 1994 ) and lazy memory allocation via hashing. Both time and space complexity of our proposed algorithm are nearly linear in the number of inputs. Simulation results confirm the efficiency and effectiveness of our proposed methods.

The evolution of air-breathing respiratory faculties in craniotes

2019 ◽  
pp. 177-191
Author(s):  
Steven F. Perry ◽  
Markus Lambertz ◽  
Anke Schmitz
Keyword(s):  
High Performance ◽  
Body Wall ◽  
Structure And Function ◽  
Control Of Breathing ◽  
Body Wall Muscle ◽  
Positive Pressure ◽  
Limiting Factor ◽  
Swim Bladder ◽  
Body Wall Musculature ◽  
And Function

The origin of lungs from a swim bladder, swim bladder from lungs, or both from a relatively undifferentiated respiratory pharynx remains unresolved. Once present, the lungs can be ventilated by a positive-pressure buccal pump, which can be easily derived from the gill ventilation sequence in a lungfish, or by negative-pressure aspiration. Although aspiration breathing is characteristic of amniotes, it has also been observed in a lungfish and body wall muscle contraction in response to respiratory stimuli has even been reported in lamprey larvae. The hypaxial body wall musculature used for aspiration breathing is also necessary for locomotion in most amniotes, just when respiratory demand is greatest. This paradox, called Carrier’s constraint, is a major limiting factor in the evolution of high-performance faculties, and the evolution of anatomical and physiological specializations that circumvent it characterize most major amniote groups. Serendipitous combinations have resulted in evolutionary cascades and high-performance groups such as birds and mammals. Complementing evolution are the capacities for acclimatization and adaptation not only in the structure and function of the gas exchanger, but also in the control of breathing and the composition of the blood.

Analysis of Different Kinds of Measurements on the Estimation of Time Dependent Mass and Heat Transfer Coefficients in Drying

2004 ◽  
Author(s):  
Leonardo F. Saker ◽  
Helcio R. B. Orlande ◽  
Cheng-Hung Huang ◽  
Gligor H. Kanevce ◽  
Ljubica P. Kanevce
Keyword(s):  
Inverse Problem ◽  
Adjoint Problem ◽  
Time Dependent ◽  
Problem Solution ◽  
Function Estimation ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Dependent Mass ◽  
Estimation Of Time ◽  
Local Moisture

In this paper we present the solution of the inverse problem of simultaneously estimating the heat and mass transfer coefficients at the surface of a drying one-dimensional body. The physical problem is formulated in terms of the linear Luikov equations and the unknown functions are time dependent. The inverse problem is solved by using the conjugate gradient method of function estimation with adjoint problem. Results are presented for the estimation of discontinuous functions by using simulated measurements of local temperature, local moisture content and/or total moisture weight. The main objective of the paper is to examine the effects of different types of measurements on the inverse problem solution.

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