234,967 research outputs found
Real-time and Probabilistic Temporal Logics: An Overview
Over the last two decades, there has been an extensive study on logical
formalisms for specifying and verifying real-time systems. Temporal logics have
been an important research subject within this direction. Although numerous
logics have been introduced for the formal specification of real-time and
complex systems, an up to date comprehensive analysis of these logics does not
exist in the literature. In this paper we analyse real-time and probabilistic
temporal logics which have been widely used in this field. We extrapolate the
notions of decidability, axiomatizability, expressiveness, model checking, etc.
for each logic analysed. We also provide a comparison of features of the
temporal logics discussed
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Dynamic networks are used in a wide range of fields, including social network
analysis, recommender systems, and epidemiology. Representing complex networks
as structures changing over time allow network models to leverage not only
structural but also temporal patterns. However, as dynamic network literature
stems from diverse fields and makes use of inconsistent terminology, it is
challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a
lot of attention in recent years for their ability to perform well on a range
of network science tasks, such as link prediction and node classification.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. To address the challenges resulting from the fact that
this research crosses diverse fields as well as to survey dynamic graph neural
networks, this work is split into two main parts. First, to address the
ambiguity of the dynamic network terminology we establish a foundation of
dynamic networks with consistent, detailed terminology and notation. Second, we
present a comprehensive survey of dynamic graph neural network models using the
proposed terminologyComment: 28 pages, 9 figures, 8 table
Dynamic state reconciliation and model-based fault detection for chemical processes
In this paper, we present a method for the fault detection based on the residual generation. The main idea is to reconstruct the outputs of the system from the measurements using the extended Kalman filter. The estimations are compared to the values of the reference model and so, deviations are interpreted as possible faults. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical processe
Integration of a failure monitoring within a hybrid dynamic simulation environment
The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering
A Survey on Continuous Time Computations
We provide an overview of theories of continuous time computation. These
theories allow us to understand both the hardness of questions related to
continuous time dynamical systems and the computational power of continuous
time analog models. We survey the existing models, summarizing results, and
point to relevant references in the literature
Model based fault diagnosis for hybrid systems : application on chemical processes
The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless, this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering
Performance analysis with network-enhanced complexities: On fading measurements, event-triggered mechanisms, and cyber attacks
Copyright © 2014 Derui Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Nowadays, the real-world systems are usually subject to various complexities such as parameter uncertainties, time-delays, and nonlinear disturbances. For networked systems, especially large-scale systems such as multiagent systems and systems over sensor networks, the complexities are inevitably enhanced in terms of their degrees or intensities because of the usage of the communication networks. Therefore, it would be interesting to (1) examine how this kind of network-enhanced complexities affects the control or filtering performance; and (2) develop some suitable approaches for controller/filter design problems. In this paper, we aim to survey some recent advances on the performance analysis and synthesis with three sorts of fashionable network-enhanced complexities, namely, fading measurements, event-triggered mechanisms, and attack behaviors of adversaries. First, these three kinds of complexities are introduced in detail according to their engineering backgrounds, dynamical characteristic, and modelling techniques. Then, the developments of the performance analysis and synthesis issues for various networked systems are systematically reviewed. Furthermore, some challenges are illustrated by using a thorough literature review and some possible future research directions are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 61203139, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
- …