Parallel Interval Algorithm for the Parameter Identification of Linear Elastomechanical Systems

1995 ◽  
Author(s):  
Michael Oeljeklaus ◽  
H. Günther Natke
Keyword(s):  
Parameter Identification ◽  
Measurement Errors ◽  
Degrees Of Freedom ◽  
A Priori ◽  
Search Space ◽  
Information Measurement ◽  
Large Structures ◽  
Speed Up ◽  
Parallel Iterative ◽  
Priori Information

Abstract An interval analytical approach to parameter identification in the frequency domain of mathematical models for linear elasto-mechanical systems is described. A priori information, measurement errors and — if possible — unmeasurable degrees of freedom are modelled in terms of intervals. A parallel iterative update method — based on the interval analytical Gauss-Seidel method — is used to reduce the volume of the parameter search space initially given. The search for the global minimum of the WLS objective function using output residuals is performed on the reduced parameter space in a last step1. Subsystem identification and sub-model synthesis are used in the case of realistic models with a large number of degrees of freedom. Parallelization of the algorithm with respect to subsystems is applied in the case of large structures to reduce the amount of memory and to speed-up the computation. Test results for some simulations for a test structure are given in order to illustrate the method.

scholarly journals An Information-Motivated Exploration Agent to Locate Stationary Persons with Wireless Transmitters in Unknown Environments

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7695
Author(s):  
Daniel Barry ◽  
Andreas Willig ◽  
Graeme Woodward
Keyword(s):  
Information Gain ◽  
Search Algorithm ◽  
A Priori ◽  
Search Space ◽  
Wireless Transmitters ◽  
Multiple Sensors ◽  
Single Target ◽  
Information Exploration ◽  
Speed Up ◽  
Priori Information

Unmanned Aerial Vehicles (UAVs) show promise in a variety of applications and recently were explored in the area of Search and Rescue (SAR) for finding victims. In this paper we consider the problem of finding multiple unknown stationary transmitters in a discrete simulated unknown environment, where the goal is to locate all transmitters in as short a time as possible. Existing solutions in the UAV search space typically search for a single target, assume a simple environment, assume target properties are known or have other unrealistic assumptions. We simulate large, complex environments with limited a priori information about the environment and transmitter properties. We propose a Bayesian search algorithm, Information Exploration Behaviour (IEB), that maximizes predicted information gain at each search step, incorporating information from multiple sensors whilst making minimal assumptions about the scenario. This search method is inspired by the information theory concept of empowerment. Our algorithm shows significant speed-up compared to baseline algorithms, being orders of magnitude faster than a random agent and 10 times faster than a lawnmower strategy, even in complex scenarios. The IEB agent is able to make use of received transmitter signals from unknown sources and incorporate both an exploration and search strategy.

scholarly journals IASI-METOP and MIPAS-ENVISAT data fusion

2010 ◽  
Vol 10 (1) ◽  
pp. 183-211 ◽  
Author(s):  
S. Ceccherini ◽  
U. Cortesi ◽  
S. Del Bianco ◽  
P. Raspollini ◽  
B. Carli
Keyword(s):  
Data Fusion ◽  
Degrees Of Freedom ◽  
Solution Method ◽  
Information Gain ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information ◽  
Single Instrument ◽  
Fusion Products ◽  
Temporal Coverage

Abstract. The combination of data obtained with different sensors (data fusion) is a powerful technique that can provide target products of the best quality in terms of precision and accuracy, as well as spatial and temporal coverage and resolution. In this paper the results are presented of the data fusion of measurements of ozone vertical profile performed by two space-borne interferometers (IASI on METOP and MIPAS on ENVISAT) using the new measurement-space-solution method. With this method both the loss of information due to interpolation and the propagation of possible biases (caused by a priori information) are avoided. The data fusion products are characterized by means of retrieval errors, information gain, averaging kernels and number of degrees of freedom. The analysis is performed both on simulated and real measurements and the results demonstrate and quantify the improvement of data fusion products with respect to measurements of a single instrument.

A Multi-Level Framework for Validation of Ontology-Driven and Community-Based Web Services Composition

2012 ◽  
Vol 3 (1) ◽  
pp. 58-72
Author(s):  
Nacéra Bennacer ◽  
Guy Vidal-Naquet
Keyword(s):  
Web Services ◽  
Description Logic ◽  
A Priori ◽  
Search Space ◽  
Community Based ◽  
Execution Plan ◽  
Multi Level ◽  
Priori Information ◽  
Dynamic Execution ◽  
Execution Order

This paper proposes an Ontology-driven and Community-based Web Services (OCWS) framework which aims at automating discovery, composition and execution of web services. The purpose is to validate and to execute a user’s request built from the composition of a set of OCWS descriptions and a set of user constraints. The defined framework separates clearly the OCWS external descriptions from internal realistic implementations of e-services. It identifies three levels: the knowledge level, the community level and e-services level and uses different participant agents deployed in a distributed architecture. First, the reasoner agent uses a description logic extended for actions in order to reason about: (i) consistency of the pre-conditions and post-conditions of OCWS descriptions and the user constraints with ontologies semantics, (ii) consistency of the workflow matching assertions and the execution dependency graph. Then the execution plan model is generated automatically to be run by the composer agents using the dynamic execution plan algorithm (DEPA), according to the workflow matching and the established execution order. The community composer agents invoke the appropriate e-services and ensure that the non functional constraints are satisfied. DEPA algorithm works dynamically without a priori information about e-services states and has interesting properties such as taking into account the non-determinism of e-services and reducing the search space.

Better estimation of origin–destination matrix using automated intersection movement count data

2015 ◽  
Vol 42 (7) ◽  
pp. 490-502 ◽  
Author(s):  
Hediye Tuydes-Yaman ◽  
Oruc Altintasi ◽  
Nuri Sendil
Keyword(s):  
Measurement Errors ◽  
Programming Model ◽  
A Priori ◽  
Mathematical Formulation ◽  
A Priori Information ◽  
Matrix Estimation ◽  
Flow Estimation ◽  
Proposed Model ◽  
Priori Information ◽  
Link Count

Intersection movements carry more disaggregate information about origin–destination (O–D) flows than link counts in a traffic network. In this paper, a mathematical formulation is presented for O–D matrix estimation using intersection counts, which is based on an existing linear programming model employing link counts. The proposed model estimates static O–D flows for uncongested networks assuming no a priori information on the O–D matrix. Both models were tested in two hypothetical networks previously used in O–D matrix studies to monitor their performances assuming various numbers of count location and measurement errors. Two new measures were proposed to evaluate the model characteristics of O–D flow estimation using traffic counts. While both link count based and intersection count based models performed with the same success under complete data collection assumption, intersection count based formulation estimated the O–D flows more successfully under decreasing number of observation locations. Also, the results of the 30 measurement error scenarios revealed that it performs more robustly than the link count based one; thus, it better estimates the O–D flows.

scholarly journals Atmospheric composition and thermodynamic retrievals from the ARIES airborne FTS system – Part 1: Technical aspects and simulated capability

2013 ◽  
Vol 6 (6) ◽  
pp. 10833-10887 ◽  
Author(s):  
S. M. Illingworth ◽  
G. Allen ◽  
S. Newman ◽  
A. Vance ◽  
F. Marenco ◽  
...  
Keyword(s):  
Evaluation System ◽  
Degrees Of Freedom ◽  
Retrieval System ◽  
A Priori ◽  
Atmospheric Composition ◽  
Trace Gas ◽  
Technical Aspects ◽  
System Part ◽  
Priori Information ◽  
The Uk

Abstract. In this study we present an assessment of the retrieval capability of the Airborne Research Interferometer Evaluation System (ARIES); an airborne remote sensing Fourier Transform Spectrometer (FTS) operated on the UK Facility for Airborne Atmospheric Measurement (FAAM) aircraft. Simulated optimally-estimated-retrievals of partial column trace gas concentrations, and thermodynamic vertical profiles throughout the troposphere and planetary boundary layer have been performed here for simulated infrared spectra representative of the ARIES system. We also describe the operational and technical aspects of the pre-processing necessary for routine retrieval from the FAAM platform and the selection and construction of a priori information. As exemplars of the capability of the ARIES retrieval system, simulated retrievals of temperature, water vapour (H2O), carbon monoxide (CO), ozone (O3), and methane (CH4), and their corresponding sources of error and potential vertical sensitivity, are discussed for ARIES scenes across typical global environments. The maximum Degrees of Freedom for Signal (DOFS) for the retrievals, assuming a flight altitude of 7 km, were: 3.99, 2.97, 0.85, 0.96, and 1.45 for temperature, H2O, CO, O3, and CH4, respectively for the a priori constraints specified. Retrievals of temperature display significant vertical sensitivity (DOFS in the range 2.6 to 4.0 across the altitude range) as well as excellent simulated accuracy, with the vertical sensitivity for H2O also extending to lower altitudes (DOFS ranging from 1.6 to 3.0). It was found that the maximum sensitivity for CO, O3, and CH4 was approximately 1–2 km below the simulated altitudes in all scenarios. Comparisons of retrieved and simulated-truth partial atmospheric columns are used to assess the capability of the ARIES measurement system. Maximum mean biases (and bias standard deviations) in partial columns (i.e. below aircraft total columns) were found to be: +0.06 (±0.02 at 1σ) %, +3.95 (±3.11)%, +3.74 (±2.97)%, −8.26 (±4.64)% and +3.01 (±2.61)% for temperature, H2O, CO, O3, and CH4 respectively, illustrating that the retrieval system performs well compared to an optimal scheme. The maximum total a posteriori retrieval errors across the partial columns were also calculated, and were found to be 0.20%, 22.57%, 18.22%, 17.61%, and 16.42% for temperature, H2O, CO, O3, and CH4 respectively.

scholarly journals Atmospheric composition and thermodynamic retrievals from the ARIES airborne FTS system – Part 1: Technical aspects and simulated capability

2014 ◽  
Vol 7 (4) ◽  
pp. 1133-1150 ◽  
Author(s):  
S. M. Illingworth ◽  
G. Allen ◽  
S. Newman ◽  
A. Vance ◽  
F. Marenco ◽  
...  
Keyword(s):  
Evaluation System ◽  
Degrees Of Freedom ◽  
Retrieval System ◽  
A Priori ◽  
Atmospheric Composition ◽  
A Posteriori ◽  
Technical Aspects ◽  
System Part ◽  
Priori Information ◽  
The Uk

Abstract. In this study we present an assessment of the retrieval capability of the Airborne Research Interferometer Evaluation System (ARIES): an airborne remote-sensing Fourier transform spectrometer (FTS) operated on the UK Facility for Airborne Atmospheric Measurement (FAAM) aircraft. Simulated maximum a posteriori retrievals of partial column trace gas concentrations, and thermodynamic vertical profiles throughout the troposphere and planetary boundary layer have been performed here for simulated infrared spectra representative of the ARIES system operating in the nadir-viewing geometry. We also describe the operational and technical aspects of the pre-processing necessary for routine retrieval from the FAAM platform and the selection and construction of a priori information. As exemplars of the capability of the ARIES retrieval system, simulated retrievals of temperature, water vapour (H2O), carbon monoxide (CO), ozone (O3), and methane (CH4), and their corresponding sources of error and potential vertical sensitivity, are discussed for ARIES scenes across typical global environments. The maximum Degrees of Freedom for Signal (DOFS) for the retrievals, assuming a flight altitude of 7 km, were 3.99, 2.97, 0.85, 0.96, and 1.45 for temperature, H2O, CO, O3, and CH4, respectively, for the a priori constraints specified. Retrievals of temperature display significant vertical sensitivity (DOFS in the range 2.6 to 4.0 across the altitude range) as well as excellent simulated accuracy, with the vertical sensitivity for H2O also extending to lower altitudes (DOFS ranging from 1.6 to 3.0). It was found that the maximum sensitivity for CO, O3, and CH4 was approximately 1–2 km below the simulated altitudes in all scenarios. Comparisons of retrieved and simulated-truth partial atmospheric columns are used to assess the capability of the ARIES measurement system. Maximum mean biases (and bias standard deviations) in partial columns (i.e. below aircraft total columns) were found to be +0.06 (±0.02 at 1σ)%, +3.95 (±3.11)%, +3.74 (±2.97)%, −8.26 (±4.64)%, and +3.01 (±2.61)% for temperature, H2O, CO, O3, and CH4, respectively, illustrating that the retrieval system performs well compared to an optimal scheme. The maximum total a posteriori retrieval errors across the partial columns were also calculated, and were found to be 0.20, 22.57, 18.22, 17.61, and 16.42% for temperature, H2O, CO, O3, and CH4, respectively.

scholarly journals Elimination of hidden a priori information from remotely sensed profile data

2007 ◽  
Vol 7 (2) ◽  
pp. 397-408 ◽  
Author(s):  
T. von Clarmann ◽  
U. Grabowski
Keyword(s):  
Degrees Of Freedom ◽  
A Priori ◽  
Michelson Interferometer ◽  
State Variables ◽  
A Priori Information ◽  
Loss Of Information ◽  
Remote Measurements ◽  
Priori Information ◽  
Representation Scheme ◽  
Additional Loss

Abstract. Profiles of atmospheric state variables retrieved from remote measurements often contain a priori information which causes complication in the statistical use of data and in the comparison with other measured or modeled data. For such applications it often is desirable to remove the a priori information from the data product. If the retrieval involves an ill-posed inversion problem, formal removal of the a priori information requires resampling of the data on a coarser grid, which in some sense, however, is a prior constraint in itself. The fact that the trace of the averaging kernel matrix of a retrieval is equivalent to the number of degrees of freedom of the retrieval is used to define an appropriate information-centered representation of the data where each data point represents one degree of freedom. Since regridding implies further degradation of the data and thus causes additional loss of information, a re-regularization scheme has been developed which allows resampling without additional loss of information. For a typical ClONO2 profile retrieved from spectra as measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), the constrained retrieval has 9.7 degrees of freedom. After application of the proposed transformation to a coarser information-centered altitude grid, there are exactly 9 degrees of freedom left, and the averaging kernel on the coarse grid is unity. Pure resampling on the information-centered grid without re-regularization would reduce the degrees of freedom to 7.1 (6.7) for a staircase (triangular) representation scheme.

scholarly journals Retrieval of Water Vapour Profiles from GLORIA Nadir Observations

2021 ◽  
Vol 13 (18) ◽  
pp. 3675
Author(s):  
Nils König ◽  
Gerald Wetzel ◽  
Michael Höpfner ◽  
Felix Friedl-Vallon ◽  
Sören Johansson ◽  
...  
Keyword(s):  
Water Vapour ◽  
Degrees Of Freedom ◽  
Infrared Imaging ◽  
A Priori ◽  
Reanalysis Data ◽  
Radiometric Calibration ◽  
Imaging Spectrometer ◽  
The North ◽  
Priori Information ◽  
Radiosonde Ascent

We present the first analysis of water vapour profiles derived from nadir measurements by the infrared imaging Fourier transform spectrometer GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere). The measurements were performed on 27 September 2017, during the WISE (Wave driven ISentropic Exchange) campaign aboard the HALO aircraft over the North Atlantic in an area between 37°–50°N and 20°–28°W. From each nadir recording of the 2-D imaging spectrometer, the spectral radiances of all non-cloudy pixels have been averaged after application of a newly developed cloud filter. From these mid-infrared nadir spectra, vertical profiles of H2O have been retrieved with a vertical resolution corresponding to five degrees of freedom below the aircraft. Uncertainties in radiometric calibration, temperature and spectroscopy have been identified as dominating error sources. Comparing retrievals resulting from two different a priori assumptions (constant exponential vs. ERA 5 reanalysis data) revealed parts of the flight where the observations clearly show inconsistencies with the ERA 5 water vapour fields. Further, a water vapour inversion at around 6 km altitude could be identified in the nadir retrievals and confirmed by a nearby radiosonde ascent. An intercomparison of multiple water vapour profiles from GLORIA in nadir and limb observational modes, IASI (Infrared Atmospheric Sounding Interferometer) satellite data from two different retrieval processors, and radiosonde measurements shows a broad consistency between the profiles. The comparison shows how fine vertical structures are represented by nadir sounders as well as the influence of a priori information on the retrievals.

scholarly journals IASI-METOP and MIPAS-ENVISAT data fusion

2010 ◽  
Vol 10 (10) ◽  
pp. 4689-4698 ◽  
Author(s):  
S. Ceccherini ◽  
U. Cortesi ◽  
S. Del Bianco ◽  
P. Raspollini ◽  
B. Carli
Keyword(s):  
Data Fusion ◽  
Degrees Of Freedom ◽  
Solution Method ◽  
Information Gain ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information ◽  
Single Instrument ◽  
Fusion Products ◽  
Temporal Coverage

Abstract. The combination of data obtained with different sensors (data fusion) is a powerful technique that can provide target products of the best quality in terms of precision and accuracy, as well as spatial and temporal coverage and resolution. In this paper the results are presented of the data fusion of measurements of ozone vertical profile performed by two space-borne interferometers (IASI on METOP and MIPAS on ENVISAT) using the new measurement-space-solution method. With this method both the loss of information due to interpolation and the propagation of possible biases (caused by a priori information) are avoided. The data fusion products are characterized by means of retrieval errors, information gain, averaging kernels and number of degrees of freedom. The analysis is performed both on simulated and real measurements and the results demonstrate and quantify the improvement of data fusion products with respect to measurements of a~single instrument.

Sign in / Sign up

Export Citation Format

Share Document