system design
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2022 ◽  
Vol 23 (2) ◽  
pp. 1-39
Tzanis Anevlavis ◽  
Matthew Philippe ◽  
Daniel Neider ◽  
Paulo Tabuada

While most approaches in formal methods address system correctness, ensuring robustness has remained a challenge. In this article, we present and study the logic rLTL, which provides a means to formally reason about both correctness and robustness in system design. Furthermore, we identify a large fragment of rLTL for which the verification problem can be efficiently solved, i.e., verification can be done by using an automaton, recognizing the behaviors described by the rLTL formula φ, of size at most O(3 |φ |), where |φ | is the length of φ. This result improves upon the previously known bound of O(5|φ |) for rLTL verification and is closer to the LTL bound of O(2|φ |). The usefulness of this fragment is demonstrated by a number of case studies showing its practical significance in terms of expressiveness, the ability to describe robustness, and the fine-grained information that rLTL brings to the process of system verification. Moreover, these advantages come at a low computational overhead with respect to LTL verification.

2022 ◽  
Vol 277 ◽  
pp. 108422
Swarna Ronanki ◽  
Jan Pavlík ◽  
Jan Masner ◽  
Jan Jarolímek ◽  
Michal Stočes ◽  

2022 ◽  
Vol 148 (3) ◽  
Jonathan B. Burkhardt ◽  
Nick Burns ◽  
Dustin Mobley ◽  
Jonathan G. Pressman ◽  
Matthew L. Magnuson ◽  

2022 ◽  
Vol 13 (1) ◽  
pp. 05021009
Iqrash Shafiq ◽  
Sumeer Shafique ◽  
Muhammad Mudassir ◽  
Muhammad Haris Hamayun ◽  
Murid Hussain

2022 ◽  
Vol 308 ◽  
pp. 118204
Christian Vering ◽  
Laura Maier ◽  
Katharina Breuer ◽  
Hannah Krützfeldt ◽  
Rita Streblow ◽  

2022 ◽  
Vol 135 ◽  
pp. 103569
Luís Cavique ◽  
Mariana Cavique ◽  
Armando Mendes ◽  
Miguel Cavique

2022 ◽  
Song Guo ◽  
Zhihao Qu

Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 567
Adrian Gambier

advanced control system design for large wind turbines is becoming increasingly complex, and high-level optimization techniques are receiving particular attention as an instrument to fulfil this significant degree of design requirements. Multiobjective optimal (MOO) control, in particular, is today a popular methodology for achieving a control system that conciliates multiple design objectives that may typically be incompatible. Multiobjective optimization was a matter of theoretical study for a long time, particularly in the areas of game theory and operations research. Nevertheless, the discipline experienced remarkable progress and multiple advances over the last two decades. Thus, many high-complexity optimization algorithms are currently accessible to address current control problems in systems engineering. On the other hand, utilizing such methods is not straightforward and requires a long period of trying and searching for, among other aspects, start parameters, adequate objective functions, and the best optimization algorithm for the problem. Hence, the primary intention of this work is to investigate old and new MOO methods from the application perspective for the purpose of control system design, offering practical experience, some open topics, and design hints. A very challenging problem in the system engineering application of power systems is to dominate the dynamic behavior of very large wind turbines. For this reason, it is used as a numeric case study to complete the presentation of the paper.

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