scholarly journals Parametric Verification and Test Coverage for Hybrid Automata Using the Inverse Method

Author(s):  
Laurent Fribourg ◽  
Ulrich Kühne
2013 ◽  
Vol 24 (02) ◽  
pp. 233-249 ◽  
Author(s):  
LAURENT FRIBOURG ◽  
ULRICH KÜHNE

Hybrid systems combine continuous and discrete behavior. Hybrid Automata are a powerful formalism for the modeling and verification of such systems. A common problem in hybrid system verification is the good parameters problem, which consists in identifying a set of parameter valuations which guarantee a certain behavior of a system. Recently, a method has been presented for attacking this problem for Timed Automata. In this paper, we show the extension of this methodology for hybrid automata with linear and affine dynamics. The method is demonstrated with a hybrid system benchmark from the literature.


2000 ◽  
Vol 12 (3-4) ◽  
pp. 219-226 ◽  
Author(s):  
P. Bellingham ◽  
N. White

2019 ◽  
Vol 51 (10) ◽  
pp. 23-30
Author(s):  
Alexey S. Bychkov ◽  
Olga N. Suprun ◽  
Irzhy Krzhyzh ◽  
Veronika Navotna
Keyword(s):  

Author(s):  
Rommel Estores ◽  
Karo Vander Gucht

Abstract This paper discusses a creative manual diagnosis approach, a complementary technique that provides the possibility to extend Automatic Test Pattern Generation (ATPG) beyond its own limits. The authors will discuss this approach in detail using an actual case – a test coverage issue where user-generated ATPG patterns and the resulting ATPG diagnosis isolated the fault to a small part of the digital core. However, traditional fault localization techniques was unable to isolate the fault further. Using the defect candidates from ATPG diagnosis as a starting point, manual diagnosis through fault Injection and fault simulation was performed. Further fault localization was performed using the ‘not detected’ (ND) and/or ‘detected’ (DT) fault classes for each of the available patterns. The result has successfully deduced the defect candidates until the exact faulty net causing the electrical failure was identified. The ability of the FA lab to maximize the use of ATPG in combination with other tools/techniques to investigate failures in detail; is crucial in the fast root cause determination and, in case of a test coverage, aid in having effective test screen method implemented.


Author(s):  
Sagnik Pal ◽  
Ranjan Das

The present paper introduces an accurate numerical procedure to assess the internal thermal energy generation in an annular porous-finned heat sink from the sole assessment of surface temperature profile using the golden section search technique. All possible heat transfer modes and temperature dependence of all thermal parameters are accounted for in the present nonlinear model. At first, the direct problem is numerically solved using the Runge–Kutta method, whereas for predicting the prevailing heat generation within a given generalized fin domain an inverse method is used with the aid of the golden section search technique. After simplifications, the proposed scheme is credibly verified with other methodologies reported in the existing literature. Numerical predictions are performed under different levels of Gaussian noise from which accurate reconstructions are observed for measurement error up to 20%. The sensitivity study deciphers that the surface temperature field in itself is a strong function of the surface porosity, and the same is controlled through a joint trade-off among heat generation and other thermo-geometrical parameters. The present results acquired from the golden section search technique-assisted inverse method are proposed to be suitable for designing effective and robust porous fin heat sinks in order to deliver safe and enhanced heat transfer along with significant weight reduction with respect to the conventionally used systems. The present inverse estimation technique is proposed to be robust as it can be easily tailored to analyse all possible geometries manufactured from any material in a more accurate manner by taking into account all feasible heat transfer modes.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Imanol Allende ◽  
Nicholas Mc Guire ◽  
Jon Perez-Cerrolaza ◽  
Lisandro G. Monsalve ◽  
Jens Petersohn ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document