In the past few years, the advances in context aware systems and sensor technologies, has elevated the Internet of Things (loT) development greatly and rather quickly. Services ofIoT systems must be reasonably designed to provide not only the user's requirements and requests, but also perceive the environmental context and customized services to get user's satisfaction. Systematic modeling methodologies are essential to control the correctness of the services and the systems behaviors among dynamic changing contexts. The presented solution will be a novel loT framework, "CANthings" (Context-Aware Networks for the Design of Connected Things) to identify loT needs.This paper mainly promotes and analyzes an loT system modeling methodology based on Timed Colored Petri Net (TCPN) to check the effectiveness of the provided services in the CANthings framework. Our goal is to present a standard solution that can be used in high-technical research and industrial projects.
This paper presents the application of artificial neural networks to implement a magnetic hysteresis model. Accurate modelling of hysteresis is essential for both the design and the performance evaluation of electromagnetic devices. It is shown that artificial neural networks (ANNs) provide natural settings whereby the Preisach model can be readily implemented. The comparison with the experiments shows that the proposed approach is able to satisfactorily reproduce many features of observed hysteresis phenomena and in turn can be used for many applications of interest.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.