Search results for "Time delay"
showing 10 items of 72 documents
Stability of solution for Rao-Nakra sandwich beam model with Kelvin-Voigt damping and time delay
2022
This paper deals with stability of solution for a one-dimensional model of Rao?Nakra sandwich beam with Kelvin?Voigt damping and time delay given by ??1?1?????? ? ??1?1?????? ? ??(??? + ?? + ??????) ? ?????????? ? ??????????( ? , ?? ? ??) = 0, ??3?3?????? ? ??3?3?????? + ??(??? + ?? + ??????) ? ?????????? = 0, ????????? + ?????????????? ? ????(??? + ?? + ??????)?? ? ?????????? = 0. A sandwich beam is an engineering model that consists of three layers: two stiff outer layers, bottom and top faces, and a more compliant inner layer called ?core layer?. Rao?Nakra system consists of three layers and the assumption is that there is no slip at the interface between contacts. The top and bottom lay…
The nuclear structure of 229Th
2002
Abstract The γ -rays following the β − decay of 229 Ac have been investigated by means of γ -ray singles and γγ -coincidence measurements using Ge detectors. Multipolarities of 40 transitions in 229 Th have been established by measuring conversion electrons with a mini-orange electron spectrometer. The half-lives of the 146.35, 164.53 and 261.96 keV levels have been measured using the advanced time delayed βγγ (t) method. The low-lying states in 229 Th and observed transition rates have been interpreted within the quasiparticle–phonon model with inclusion of Coriolis coupling. Two octupole correlated parity partner bands, with K π =5/2 ± and K π =3/2 ± , were identified in 229 Th.
Contextual neural-network based spectrum prediction for cognitive radio
2015
Cognitive radio is the technique of effective electromagnetic spectrum usage important for future wireless communication including 5G networks. Neural networks are nature-inspired computational models used to solve cognitive radio prediction problems. This paper presents the use of contextual Sigma-if neural network in prediction of channel states for cognitive radio. Our results indicate that Sigma-if neural network confirms better predictions than Multilayer Perceptron (MLP) network and decreases sensing time for the benefit of the increase of the effectiveness of e-m spectrum usage.
Analysis of motor control and behavior in multi agent systems by means of artificial neural networks
2004
Abstract This article gives a short introduction to Self-Organizing Maps, a particular form of Artificial Neural Networks and shows by some examples, how these approaches can be used in order to analyze and visualize time series data originating from complex systems. The methods shown in this article have originally been developed for the analysis of RoboCup robot soccer games, a special kind of so-called Multi Agent Systems. Although this application has no direct connection to biomechanics, the examples shown here may give an impression of the abilities of Neural Networks in the field of Time Series Analysis in general. Because of the abstractness of the methods, it appears to be very lik…
Application of the LISS Lyapunov-Krasovskii small-gain theorem to autonomously controlled production networks with time-delays
2010
Accepted version of a paper published by IEEE. (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works Published version: http://dx.doi.org/10.1109/SYSTOL.2010.5676085 In this paper we consider general autonomously controlled production networks. A production network consists of geographically distributed plants, which are connected by transport routes such that transportation times (time-delays) …
Adaptive H ∞ synchronization of master-slave systems with mixed time-varying delays and nonlinear perturbations: An LMI approach
2011
Published version of an article in the journal: International Journal of Automation and Computing. Also available from the publisher on SpringerLink: http://dx.doi.org/10.1007/s11633-011-0595-z This paper proposes an adaptive synchronization problem for the master and slave structure of linear systems with nonlinear perturbations and mixed time-varying delays comprising different discrete and distributed time delays. Using an appropriate Lyapunov-Krasovskii functional, some delay-dependent sufficient conditions and an adaptation law including the master-slave parameters are established for designing a delayed synchronization law in terms of linear matrix inequalities(LMIs). The time-varying…
Time delays in starting thrombolytic therapy
1998
A Reconfigurable Neural Environment on Active Networks
2000
This paper proposes the deployment of a neural network computing environment on Active Networks. Active Networks are packet-switched computer networks in which packets can contain code fragments that are executed on the intermediate nodes. This feature allows the injection of small pieces of codes to deal with computer network problems directly into the network core, and the adoption of new computing techniques to solve networking problems. The goal of our project is the adoption of a distributed neural network for approaching tasks which are specific of the computer network environment. Dynamically reconfigurable neural networks are spread on an experimental wide area backbone of active no…
Electrically tunable photonic true-time-delay line
2010
We present a new application of the acousto-optic superlattice modulation of a fiber Bragg grating based on the dynamic phase and group delay properties of this fiber-optic component. We demonstrate a tunable photonic true-time-delay line based on the group delay change of the light reflected from the grating sidebands. The delay is electrically tuned by adjusting the voltage applied to a piezoelectric transducer that generates the acoustic wave propagating along the grating. In our experiments, a true-time delay of 400 ps is continuously adjusted (300 ps within the 3 dB amplitude range of the first sideband), using a 12 cm long uniform grating.
Unknown order process emulation
2002
Approaches the emulation problem using feedforward neural networks of single input single output (SISO) processes, applying a backpropagation method with a higher convergence rate. In this kind of application, difficult problems appear when the system's order is a priori unknown. A search through the SISO processes space is proposed, aiming to find a favorable neural emulator over the training examples set.