A correctness criterion for asynchronous circuit validation and optimization

Author(s):  
G. Gopalakrishnan ◽  
E. Brunvand ◽  
N. Michell ◽  
S.M. Nowick
2004 ◽  
Vol 13 (03) ◽  
pp. 289-332 ◽  
Author(s):  
JULIANE DEHNERT ◽  
WIL M. P. VAN DER AALST

This paper presents a methodology to bridge the gap between business process modeling and workflow specification. While the first is concerned with intuitive descriptions that are mainly used for communication, the second is concerned with configuring a process-aware information system, thus requiring a more rigorous language less suitable for communication. Unlike existing approaches the gap is not bridged by providing formal semantics for an informal language. Instead it is assumed that the desired behavior is just a subset of the full behavior obtained using a liberal interpretation of the informal business process modeling language. Using a new correctness criterion (relaxed soundness), it is verified whether a selection of suitable behavior is possible. The methodology consists of five steps and is illustrated using event-driven process chains as a business process modeling language and Petri nets as the workflow specification language.


Integration ◽  
2019 ◽  
Vol 64 ◽  
pp. 29-39
Author(s):  
Heechun Park ◽  
Taewhan Kim
Keyword(s):  

1996 ◽  
Vol 9 (3) ◽  
pp. 189-233 ◽  
Author(s):  
Alexandre Yakovlev ◽  
Michael Kishinevsky ◽  
Alex Kondratyev ◽  
Luciano Lavagno ◽  
Marta Pietkiewicz-Koutny
Keyword(s):  

2015 ◽  
Vol 62 (9) ◽  
pp. 856-860 ◽  
Author(s):  
Tiben Che ◽  
Jingwei Xu ◽  
Gwan Choi
Keyword(s):  

2013 ◽  
Vol 427-429 ◽  
pp. 1506-1509
Author(s):  
Yong Yan Yu

A robust estimation procedure is necessary to estimate the true model parameters in computer vision. Evaluating the multiple-model in the presence of outliers-robust is a fundamentally different task than the single-model problem.Despite there are many diversity multi-model estimation algorithms, it is difficult to pick an effective and advisably approach.So we present a novel quantitative evaluation of multi-model estimation algorithms, efficiency may be evaluated by either examining the asymptotic efficiency of the algorithms or by running them for a series of data sets of increasing size.Thus we create a specifical testing dataset,and introduce a performance metric, Strongest-Intersection.and using the model-aware correctness criterion. Finally, well show the validity of estimation strategy by the Experimention of line-fitting.


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