scholarly journals Weighed ranking of aprioristic and experimental data in control system functioning efficiency estimation problem with Pascal-distributed number of tests

2020 ◽  
pp. 39-47
Author(s):  
Vladimir Arseniev ◽  
Anatolij Khomonenko ◽  
Andrei Yadrenkin
Keyword(s):  
Experimental Data ◽  
Control System ◽  
Functional Dependence ◽  
A Priori ◽  
Real Life ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Efficiency Estimation ◽  
System Functioning ◽  
Priori Information

Introduction: In order to steadily estimate the efficiency of control systems for new objects, a great number of prototypes should be tested, which is not always possible in practice. The estimation quality can be improved by joint processing of the a priori information you have before the tests by analyzing certain indicators, and the data obtained from the tests. To estimate the efficiency a posteriori, taking into account both the a priori knowledge and the test results, you have to find their functional dependence on each of them, and specify the parameters of this dependence. Purpose: Integrated processing of the results from both aprioristic and experimental research of a control system, and obtaining posterior estimations of the efficiency indices. Results: A control system efficiency estimation method is proposed, which integrates the aprioristic and experimental estimations of the efficiency indices obtained a priori and during a limited number of tests of system prototypes. It can be used when the results of aprioristic research and the tests are presented by point estimations of the efficiency indices, and the most common methods are difficult to apply. We present analytical expressions for posterior estimation of the probability that the system will perform its task, along with the indicators which are used to study the influence of the aprioristic information on the estimation accuracy and number of tests. The working capacity of the method is illustrated by a real-life example. This approach, unlike others, takes into account how close the aprioristic estimations are to the experimental ones. Practical relevance: The proposed approach is universal enough, as it allows you to integrate the information obtained at various stages of studying the system, and essentially improve the efficiency estimation accuracy, specifying the gain in the number of tests in all the cases when the aprioristic research results are in consonance with the experimental data.

Integrative fMRI-MEG Methods and Optically Pumped Atomic Magnetometers for Exploring Higher Brain Functions

2011 ◽  
pp. 9-17
Author(s):  
Tetsuo Kobayashi
Keyword(s):  
A Priori ◽  
Estimation Method ◽  
Generalized Least Squares ◽  
Spatial Filter ◽  
Estimation Accuracy ◽  
Optically Pumped ◽  
Brain Functions ◽  
Temporal Correlations ◽  
Priori Information ◽  
Beamforming Technique

This chapter introduces a newly developed integrative fMRI-MEG method combined with a spatial filtering (beamforming) technique as a non-invasive neuroimaging method to reveal dynamic processes in the brain. One difficulty encountered when integrating fMRI-MEG analyses is mismatches between the activated regions detected by fMRI and MEG. These mismatches may decrease the estimation accuracy, especially when there are strong temporal correlations among activity in fMRI-invisible and -visible regions. To overcome this difficulty, a spatial filter was devised based on a generalized least squares (GLS) estimation method. The filter can achieve accurate reconstruction of MEG source activity even when a priori information obtained by fMRI is insufficient. In addition, this chapter describes the feasibility of a newly developed optically pumped atomic magnetometer as a magnetic sensor to simultaneously measure MEG and MR signals.

scholarly journals A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals

2019 ◽  
Vol 12 (7) ◽  
pp. 3943-3961 ◽  
Author(s):  
Ali Jalali ◽  
Shannon Hicks-Jalali ◽  
Robert J. Sica ◽  
Alexander Haefele ◽  
Thomas von Clarmann
Keyword(s):  
Water Vapor ◽  
A Priori ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Mixing Ratio ◽  
A Priori Information ◽  
Fine Grid ◽  
Large Power ◽  
Priori Information ◽  
Water Vapor Mixing Ratio

Abstract. Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval's averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.

scholarly journals Seismic Constrained Gravity Inversion: A Reliable Tool to Improve Geophysical Models Away from Seismic Information

Geosciences ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 467
Author(s):  
Daniele Sampietro ◽  
Martina Capponi
Keyword(s):  
High Resolution ◽  
Gravity Field ◽  
A Priori ◽  
Geophysical Methods ◽  
Real Life ◽  
Gravity Inversion ◽  
Seismic Surveys ◽  
Real Model ◽  
Gravity Fields ◽  
Priori Information

The exploitation of gravity fields in order to retrieve information about subsurface geological structures is sometimes considered a second rank method, in favour of other geophysical methods, such as seismic, able to provide a high resolution detailed picture of the main geological horizons. Within the current work we prove, through a realistic synthetic case study, that the gravity field, thanks to the availability of freely of charge high resolution global models and to the improvements in the gravity inversion methods, can represent a valid and cheap tool to complete and enhance geophysical modelling of the Earth’s crust. Three tests were carried out: In the first one a simple two-layer problem was considered, while in tests two and three we considered two more realistic scenarios in which the availability on the study area of constraints derived from 3D or 2D seismic surveys were simulated. In all the considered test cases, in which we try to simulate real-life scenarios, the gravity field, inverted by means of an advanced Bayesian technique, was able to obtain a final solution closer to the (simulated) real model than the assumed a priori information, typically halving the uncertainties in the geometries of the main geological horizons with respect to the initial model.

scholarly journals Production Possibility Frontiers and Estimation of Competition Effects: The Use of A Priori Information on Biological Processes in Intercropping

1992 ◽  
Vol 28 (3) ◽  
pp. 351-368 ◽  
Author(s):  
Radha Ranganathan
Keyword(s):  
Experimental Data ◽  
Biomass Yield ◽  
Plant Density ◽  
A Priori ◽  
Biological Processes ◽  
A Priori Information ◽  
Production Frontier ◽  
Production Possibility ◽  
Competition Effects ◽  
Priori Information

SummaryProduction possibility frontiers contribute much to an economic evaluation of yield advantages from intercropping. The difficulty with estimating a production frontier empirically from experimental data is one of ascertaining that the fitted curve corresponds with the frontier. This problem has been overcome by deriving the frontier from a priori knowledge of the biological processes that determine the outcome in intercropping. The hyperbolic relationship between biomass yield and plant density, and the parameters that characterize the degree of intra-and inter-specific competition in intercropping are used in this paper to derive production possibility frontiers. The method is illustrated with data from three intercropping studies. A brief review of the two main methods used by researchers to evaluate the results of intercropping, and their limitations, is also presented.

Joint interpretation and uncertainty analysis of petrophysical properties in unconventional shale reservoirs

2015 ◽  
Vol 3 (1) ◽  
pp. SA33-SA49 ◽  
Author(s):  
Qinshan Yang ◽  
Carlos Torres-Verdín
Keyword(s):  
Joint Inversion ◽  
A Priori ◽  
Estimation Method ◽  
Monte Carlo Algorithm ◽  
Posteriori Probability ◽  
Inversion Method ◽  
A Posteriori ◽  
A Priori Information ◽  
Core Data ◽  
Priori Information

Interpretation of hydrocarbon-bearing shale is subject to great uncertainty because of pervasive heterogeneity, thin beds, and incomplete and uncertain knowledge of saturation-porosity-resistivity models. We developed a stochastic joint-inversion method specifically developed to address the quantitative petrophysical interpretation of hydrocarbon-bearing shale. The method was based on the rapid and interactive numerical simulation of resistivity and nuclear logs. Instead of property values themselves, the estimation method delivered the a posteriori probability of each property. The Markov-chain Monte Carlo algorithm was used to sample the model space to quantify the a posteriori distribution of formation properties. Additionally, the new interpretation method allows the use of fit-for-purpose statistical correlations between water saturation, salt concentration, porosity, and electrical resistivity to implement uncertain, non-Archie resistivity models derived from core data, including those affected by total organic carbon (TOC). In the case of underdetermined estimation problems, i.e., when the number of measurements was lower than the number of unknowns, the use of a priori information enabled plausible results within prespecified petrophysical and compositional bounds. The developed stochastic interpretation technique was successfully verified with data acquired in the Barnett and Haynesville Shales. Core data (including X-ray diffraction data) were combined into a priori information for interpretation of nuclear and resistivity logs. Results consisted of mineral concentrations, TOC, and porosity together with their uncertainty. Eighty percent of the core data was located within the 95% credible interval of estimated mineral/fluid concentrations.

Design and Simulation of Speed Sensorless Control System for Induction Motor

2012 ◽  
Vol 201-202 ◽  
pp. 396-399
Author(s):  
Hong Yu Wang ◽  
Yan Peng ◽  
Yan Hou
Keyword(s):  
Control System ◽  
Induction Motor ◽  
Sensorless Control ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Rotor Speed ◽  
Speed Sensorless ◽  
Input Filter ◽  
Output Filter

In this paper, a method for estimating induction motor’s rotor speed is proposed. The proposed rotor speed estimation method is based on model reference adaptive identification theory. By applying the proposed method, the induction motor control system can estimate the rotor speed of the induction motor precisely. To improve the rotor speed estimation performance of the system, one input filter and one output filter are introduced into the speed sensorless control system. The introduced input filter and output filter enhance the estimation accuracy and improve the reliability and robustness of the system. The speed sensorless control system based on proposed method was built with Simulink blocks in Matlab platform. The simulation results indicate that the proposed method can operate stably in whole range of speed with preferable identification precision of the rotor speed.

scholarly journals Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

2019 ◽  
Author(s):  
Olexandr Khustochka ◽  
Sergiy Yepifanov ◽  
Roman Zelenskyi ◽  
Radoslaw Przysowa
Keyword(s):  
Experimental Data ◽  
Path Analysis ◽  
Measurement Errors ◽  
Dynamic Models ◽  
A Priori ◽  
Estimation Method ◽  
Parametric Uncertainty ◽  
Performance Parameters ◽  
Turbine Engine ◽  
Engine Components

Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. This results from the fact that the number of measured parameters is significantly lower than the number of components’ performance parameters needed to describe the real engine. In these conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, loss factors etc. can appear out of the physical range. The current methods of engine model identification have developed considerably to provide stable, precise and physically adequate solutions. Presented in this work is an estimation method of engine components’ parameters based on multi-criteria identification which provides stable estimations of parameters and their confidence intervals with the known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularised identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Forming objective functions and scalar convolutions synthesis of these functions is used to estimate gas-path components’ parameters. A comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, Gas Path Analysis and also adapting dynamic models for the needs of the engine control system.

Optimization Control for Dynamic Thermal Comfort in an Intelligent Inhabitation Environment

2013 ◽  
Vol 816-817 ◽  
pp. 371-374
Author(s):  
Pei Yong Duan ◽  
Hui Li ◽  
Cong Cong Liu
Keyword(s):  
Control System ◽  
Thermal Comfort ◽  
Energy Saving ◽  
Control Method ◽  
Thermal Environment ◽  
A Priori ◽  
Indoor Environments ◽  
Environment Model ◽  
Control Evaluation ◽  
Priori Information

Comfortable, healthy, and energy-saving indoor environments can be obtained via a dynamic thermal comfort control. Difficulties to design an optimal control system for a dynamic thermal environment arise due to the lack of coordinative control evaluation methods for conflicting comfort and energy-saving indices. An improved multi-objective algorithm based on discrete PSO (Particle Swarm Optimization) is proposed to calculate the optimal values of parameters in the dynamic comfort control system based on users balance between the comfort and energy conservation. No a priori information or physical indoor environment model is needed. Experiment results demonstrate the effectiveness of the proposed control method.

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