Cryptography plays a crucial role in Industry 4.0, safeguarding the security and privacy of data and communications within highly interconnected and automated industrial systems. As vast amounts of data are generated ...
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Model reduction simplifies the mathematical complexities inherent in a system model. Specifically, within the Linear Time-Invariant (LTI) systems, reducing complexity translates to reducing the dynamic order of the eq...
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The War Strategy Optimization (WSO) algorithm is being suggested as a fresh approach which is inspired by old war tactics and their lessons with the purpose to generate better optimization processes. This study focuse...
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With the increasing number of vehicles on roads, the occurrence of road accidents has become a major concern globally. Automatic road accident detection systems based on Internet of Things (IoT) technologies have emer...
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Autonomous driving technology is advancing rapidly and is becoming an indispensable part of the world's automation landscape. Achieving effective control and planning for autonomous vehicles is crucial to ensuring...
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The use of encryption algorithms is more and more turning into a crucial issue of defensive wireless networks. Encryption algorithms may be used to cozy facts that is transferred over a Wi-Fi network while also presen...
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A predictive model refers to a mathematical or computational model that is designed to make predictions or forecasts based on input data. These models are used in various fields such as finance, healthcare, marketing,...
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One of the benefits of using executable specifications such as Behavioral Programming (BP) is the ability to align the system implementation with its requirements. This is facilitated in BP by a protocol that allows i...
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ISBN:
(纸本)9798350395129;9798350395112
One of the benefits of using executable specifications such as Behavioral Programming (BP) is the ability to align the system implementation with its requirements. This is facilitated in BP by a protocol that allows independent implementation modules that specify what the system may, must, and must not do. By that, each module can enforce a single system requirement, including negative specifications such as "don't do X after Y." The existing BP protocol, however, allows only the enforcement of safety requirements and does not support the execution of liveness properties such as "do X at least three times." To model liveness requirements in BP directly and independently, we propose idioms for tagging states with "must-finish," indicating that tasks are yet to be completed. We show that this idiom allows a direct specification of known requirements patterns from the literature. We also offer semantics and two execution mechanisms, one based on a translation to Buchi automata and the other based on a Markov decision process (MDP). The latter approach offers the possibility of utilizing deep reinforcement learning (DRL) algorithms, which bear the potential to handle large software systems effectively. This paper presents a qualitative and quantitative assessment of the proposed approach using a proof-of-concept tool. A formal analysis of the MDP-based execution mechanism is given in an appendix.
In order to efficiently apply Wireless Power Transfer (WPT) technology in photovoltaic (PV) systems, mathematical modeling and mechanism analysis of Magnetic Coupling Resonant Wireless Power Transfer (MCR-WPT) systems...
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The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily...
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