Subspace Algorithms for Error Localization With Quantized DFT Codes

2004 ◽  
Vol 52 (12) ◽  
pp. 2115-2124 ◽  
Author(s):  
G. Rath ◽  
C. Guillemot
Keyword(s):  
Error Localization ◽  
Subspace Algorithms

ERROR LOCALIZATION DIAGNOSTIC MODEL OPTIMIZATION IN DIGITAL STATE MACHINES NETWORKS

2019 ◽  
Author(s):  
Irina Bystrova ◽  
E. Danil'chuk ◽  
Boris Podkopaev
Keyword(s):  
Single Component ◽  
Diagnostic Model ◽  
State Machines ◽  
Model Optimization ◽  
Error Localization ◽  
Diagnostic Models

The problem of constructing a diagnostic model for a network S consisting of a number of digital automata is considered, provided that the diagnostic models of all network components are known. It is assumed that these models are given by systems of logical equations, and the errors to be detected are localized in any but a single component of the network.

Identification of Linear Tire Cornering Stiffness Using Subspace Methods

2014 ◽  
Vol 701-702 ◽  
pp. 492-497
Author(s):  
Teng Yue Ba ◽  
Xi Qiang Guan ◽  
Jian Wu Zhang
Keyword(s):  
Data Driven ◽  
Subspace Identification ◽  
Subspace Methods ◽  
Identification Methods ◽  
The Road ◽  
Strong Robustness ◽  
Subspace Algorithms ◽  
Double Lane Change ◽  
On The Road ◽  
Lane Change Test

In this paper, subspace identification methods are proposed to estimate the linear tire cornering stiffness, which are only based on the road tests data without any prior knowledge. This kind of data-driven method has strong robustness. In order to validate the feasibility and effectiveness of the algorithms, a series of standard road tests are carried out. Comparing with different subspace algorithms used in road tests, it can be concluded that the front tire cornering stiffness can be estimated accurately by the N4SID and CCA methods when the double lane change test data are taken into analysis.

Automated Design Error Localization in RTL Designs

2014 ◽  
Vol 31 (1) ◽  
pp. 83-92 ◽  
Author(s):  
Maksim Jenihhin ◽  
Anton Tsepurov ◽  
Valentin Tihhomirov ◽  
Jaan Raik ◽  
Hanno Hantson ◽  
...  
Keyword(s):  
Automated Design ◽  
Design Error ◽  
Error Localization

Conventional and Subspace Algorithms for Mobile Source Detection and Radiation Formation

2021 ◽  
Vol 38 (1) ◽  
pp. 135-145
Author(s):  
Sadiya Thazeen ◽  
S Mallikarjunaswamy ◽  
G K Siddesh ◽  
N Sharmila
Keyword(s):  
Source Detection ◽  
Mobile Source ◽  
Subspace Algorithms

Reliable Mode Shape Expansion Method for Error Localisation and Model Updating

1997 ◽  
Author(s):  
Philippe Collignon ◽  
Jean-Claude Golinval
Keyword(s):  
Cantilever Beam ◽  
Mode Shape ◽  
Constitutive Equations ◽  
Structural Model ◽  
Model Updating ◽  
Failure Detection ◽  
Expansion Method ◽  
Relative Performance ◽  
Mode Shapes ◽  
Error Localization

Abstract Failure detection and model updating using structural model are based on the comparison of an appropriate indicator of the discrepancy between experimental and analytical results. The reliability of the expansion of measured mode shapes is very important for the process of error localization and model updating. Two mode shape expansion techniques are examined in this paper : the well known dynamic expansion (DE) method and a method based on the minimisation of errors on constitutive equations (MECE). A new expansion method based on some improvements of the previous techniques is proposed to obtain results that are more reliable for error localisation and for model updating. The relative performance of the different expansion methods is demonstrated on the example of a cantilever beam.

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