IPA: Error Propagation Analysis of Multi-Threaded Programs Using Likely Invariants

2017 ◽  
Author(s):  
Abraham Chan ◽  
Stefan Winter ◽  
Habib Saissi ◽  
Karthik Pattabiraman ◽  
Neeraj Suri
Keyword(s):  
Error Propagation ◽  
Propagation Analysis ◽  
Error Propagation Analysis

scholarly journals A Workflow Based on SNAP–StaMPS Open-Source Tools and GNSS Data for PSI-Based Ground Deformation Using Dual-Orbit Sentinel-1 Data: Accuracy Assessment with Error Propagation Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 753 ◽  
Author(s):  
Francesco Mancini ◽  
Francesca Grassi ◽  
Nicola Cenni
Keyword(s):  
Open Source ◽  
Error Propagation ◽  
Ground Deformation ◽  
Accuracy Assessment ◽  
Decomposition Analysis ◽  
Anthropogenic Sources ◽  
Synthetic Aperture Radar Data ◽  
Persistent Scatterers ◽  
Propagation Analysis ◽  
Error Propagation Analysis

This paper discusses a full interferometry processing chain based on dual-orbit Sentinel-1A and Sentinel-1B (S1) synthetic aperture radar data and a combination of open-source routines from the Sentinel Application Platform (SNAP), Stanford Method for Persistent Scatterers (StaMPS), and additional routines introduced by the authors. These are used to provide vertical and East-West horizontal velocity maps over a study area in the south-western sector of the Po Plain (Italy) where land subsidence is recognized. The processing of long time series of displacements from a cluster of continuous global navigation satellite system stations is used to provide a global reference frame for line-of-sight–projected velocities and to validate velocity maps after the decomposition analysis. We thus introduce the main theoretical aspects related to error propagation analysis for the proposed methodology and provide the level of uncertainty of the validation analysis at relevant points. The combined SNAP–StaMPS workflow is shown to be a reliable tool for S1 data processing. Based on the validation procedure, the workflow allows decomposed velocity maps to be obtained with an accuracy of 2 mm/yr with expected uncertainty levels lower than 2 mm/yr. Slant-oriented and decomposed velocity maps provide new insights into the ground deformation phenomena that affect the study area arising from a combination of natural and anthropogenic sources.

Automated Transformation of UML/SysML Behavioral Diagrams for Stochastic Error Propagation Analysis of Autonomous Systems

2021 ◽  
Author(s):  
Andrey Morozov ◽  
Thomas Mutzke ◽  
Kai Ding
Keyword(s):  
Error Propagation ◽  
System Modeling ◽  
Dual Graph ◽  
Medical Patient ◽  
Transformation Method ◽  
Propagation Model ◽  
Probabilistic Model Checking ◽  
Model Based ◽  
Propagation Analysis ◽  
Error Propagation Analysis

Abstract Modern technical systems consist of heterogeneous components, including mechanical parts, hardware, and the extensive software part that allows the autonomous system operation. The heterogeneity and autonomy require appropriate models that can describe the mutual interaction of the components. UML and SysML are widely accepted candidates for system modeling and model-based analysis in early design phases, including the analysis of reliability properties. UML and SysML models are semi-formal. Thus, transformation methods to formal models are required. Recently, we introduced a stochastic Dual-graph Error Propagation Model (DEPM). This model captures control and data flow structures of a system and allows the computation of advanced risk metrics using probabilistic model checking techniques. This article presents a new automated transformation method of an annotated State Machine Diagram, extended with Activity Diagrams, to a hierarchical DEPM. This method will help reliability engineers to keep error propagation models up to date and ensure their consistency with the available system models. The capabilities and limitations of transformation algorithm is described in detail and demonstrated on a complete model-based error propagation analysis of an autonomous medical patient table.

A Family of Explicit Dissipative Algorithms for Pseudodynamic Testing

2006 ◽  
Vol 22 (2) ◽  
pp. 145-154 ◽  
Author(s):  
S.- Y. Chang
Keyword(s):  
High Frequency ◽  
Error Propagation ◽  
Numerical Dissipation ◽  
Explicit Method ◽  
High Frequency Response ◽  
Frequency Responses ◽  
Pseudodynamic Test ◽  
Propagation Analysis ◽  
Error Propagation Analysis ◽  
Pseudodynamic Testing

AbstractThe α-function method is a family of second-order explicit methods with controlled numerical dissipation. Thus, it is very promising for the pseudodynamic testing of a system where high frequency responses are of no interest. This is because that favorable numerical dissipation can suppress the spurious growth of high frequency responses, which might arise from numerical and/or experimental errors during a test. Furthermore, the implementation of an explicit method for the pseudodynamic testing is much simpler than for an implicit method. The superiority of using this method in performing a pseudodynamic test was verified both analytically and experimentally. In fact, results of error propagation analysis reveal that the spurious growth of high frequency responses can be suppressed and less error propagation is identified when compared to the Newmark explicit method. Actual tests were conducted pseudodynamically to confirm all the analytical results. It is also illustrated that although the high frequency response is insignificant to the total response it may be significantly amplified and propagated and finally destroys the pseudodynamic test results.

Error propagation analysis for underwater cooperative multi-hop communications

2009 ◽  
Vol 7 (4) ◽  
pp. 759-769 ◽  
Author(s):  
Cecilia Carbonelli ◽  
Shiou-Hung Chen ◽  
Urbashi Mitra
Keyword(s):  
Error Propagation ◽  
Propagation Analysis ◽  
Error Propagation Analysis
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