China
Africa
Explore chapters and articles related to this topic
The Future of Wide Area Networks
Published in Marcus K. Weldon, The Future X Network, 2018
T.V. Lakshman, Kevin Sparks, Marina Thottan
Network configuration protocol (NETCONF) is a new protocol enabling the service orchestrator function for the IP layer using remote procedure calls and notifications to configure network elements described through YANG models (Bjorklund 2010, Enns 2006). YANG is a data modeling language used to describe network elements and their possible configurations and parameter values and states. To enable end-to-end programmability across domains and network layers, the network modeling language must go beyond this element-level configuration and be able to provide description of the entire network.
Managing Mobility with SDN: A Practical Walkthrough
Published in Hrishikesh Venkatarman, Ramona Trestian, 5G Radio Access Networks: Centralized RAN, Cloud-RAN, and Virtualization of Small Cells, 2017
Xuan Thuy Dang, Manzoor Ahmed Khan
YANG was originally developed to model configuration and state data in network devices, but it can also be used to describe other network constructs, such as services, policies, protocols, or subscribers. YANG is tree- structured rather than object-oriented; data are structured into a tree and it can contain complex types, such as lists and unions. In addition to data definitions, YANG supports constructs to model remote procedure calls (RPCs) and notifications, which make it suitable for use as an interface description language (IDL) in a model-driven system.
SysML-based compositional verification and safety analysis for safety-critical cyber-physical systems
Published in Connection Science, 2022
Jian Xie, Wenan Tan, Zhibin Yang, Shuming Li, Linquan Xing, Zhiqiu Huang
There are several MDD languages and approaches covering various modelling demands, such as Unified Modeling Language (UML) for generic modelling, Systems Modeling Language (SysML) for system-level modelling (Stewart et al., 2017; Weilkiens, 2007; Zhang et al., 2020), Architecture Analysis and Design Language(AADL) (Sabaghian et al., 2020; Yang et al., 2014) for the architectural modelling and analysis of embedded systems, SCADE and Simulink for functional modelling, and Modelica for multi-disciplines modelling. SysML was designed by the International Council on Systems Engineering(INCOSE) and the Object Management Group (OMG). As a profile for UML2.0 (Group, 2007), SysML was created specifically for the systems engineering domain to integrate multiple views of large, complex systems engineering consisting of hardware, software, requirements, data, people and processes. Moreover, SysML provides several extension mechanisms such as stereotypes, diagram extensions, and model libraries. There are several commercial and open-source tools for SysML model creation and design, which include Rational Rhapsody, Modeler, Modelio, as well as Papyrus (Berumen-Flucker et al., 2019). They support model-based engineering and have been used successfully in industry to model complex systems. Thus, SysML is more and more considered as the system modelling language in the domain of SC-CPS.
Learning Dynamic Bayesian Networks structure based on a new hybrid K2-Bat learning algorithm
Published in Journal of the Chinese Institute of Engineers, 2021
Yu-Jing Deng, Hao-Ran Liu, Hai-Yu Wang, Bin Liu
Bayesian Networks (BN) are important theoretical tools in the field of artificial intelligence algorithms. They describe the causal relationship between variables and the characteristics of data sets through directed acyclic graphs and conditional probability tables (Tien and Kiureghian 2016). A Dynamic Bayesian Network (DBN) is one of the modeling tools for intelligent data analysis, which aims to deeply study the behavior of complex static and dynamic processes under risk and uncertainty. As an extension of BN, DBN combines time dimension with static BN to construct a dynamic reasoning model for dynamic analysis and prediction of time information. It is widely used in time sequence data modeling for clinical and ecological applications, including bird population in wildlife reserves (Milns, Beale, and Smith 2010), cancer detection (Van Gerven, Taal, and Lucas 2008), blood transcriptome data modeling (Yang and Dan 2018), etc. However, the increase of time dimension increases the difficulty and complexity of DBN learning method. Therefore, some researchers are committed to DBN learning technology.
Online sequential monitoring of spatio-temporal disease incidence rates
Published in IISE Transactions, 2020
In the literature, there is considerable discussion about retrospective modeling of spatio-temporal data obtained within a given spatial region and a given time interval. For instance, some authors suggested estimating the true mean function of the spatio-temporal data using penalized splines under the generalized additive model or mixed-effects model framework (Heuvelink and Griffith, 2010; Chouldechova and Hastie, 2015). Some other existing methods require certain parametric model assumptions (Diggle, 2014). Kafadar (1996) suggested analyzing spatial data using conventional local constant kernel smoothing procedures. Recent discussions about estimation of the spatio-temporal covariance structure can be found in Shand and Li (2017) and Yang and Qiu (2019). Some overviews on this topic can be found in Banerjee et al. (2004), Schabenberger and Gotway (2005), Gneiting and Guttorp (2010) Cressie and Wikle (2011), Diggle (2014), Lindström et al. (2015), and Gonzalez et al. (2016). Recently, Yang and Qiu (2018) suggested a flexible approach for spatio-temporal data modeling, which was shown to be effective than some alternative approaches in various different cases. This approach is adopted here and briefly described below.