19 research outputs found
Very high frequency gravitational wave background in the universe
Astrophysical sources of high frequency gravitational radiation are
considered in association with a new interest to very sensitive HFGW receivers
required for the laboratory GW Hertz experiment. A special attention is paid to
the phenomenon of primordial black holes evaporation. They act like black body
to all kinds of radiation, including gravitons, and, therefore, emit an
equilibrium spectrum of gravitons during its evaporation. Limit on the density
of high frequency gravitons in the Universe is obtained, and possibilities of
their detection are briefly discussed.Comment: 14 page
The Evolution of Compact Binary Star Systems
We review the formation and evolution of compact binary stars consisting of
white dwarfs (WDs), neutron stars (NSs), and black holes (BHs). Binary NSs and
BHs are thought to be the primary astrophysical sources of gravitational waves
(GWs) within the frequency band of ground-based detectors, while compact
binaries of WDs are important sources of GWs at lower frequencies to be covered
by space interferometers (LISA). Major uncertainties in the current
understanding of properties of NSs and BHs most relevant to the GW studies are
discussed, including the treatment of the natal kicks which compact stellar
remnants acquire during the core collapse of massive stars and the common
envelope phase of binary evolution. We discuss the coalescence rates of binary
NSs and BHs and prospects for their detections, the formation and evolution of
binary WDs and their observational manifestations. Special attention is given
to AM CVn-stars -- compact binaries in which the Roche lobe is filled by
another WD or a low-mass partially degenerate helium-star, as these stars are
thought to be the best LISA verification binary GW sources.Comment: 105 pages, 18 figure
Fault Detection for a Class of Unknown Nonlinear Systems via Associative Memory Networks
This paper presents a novel approach for the detection of faults for a class of nonlinear systems whose parameters are unknown nonlinear functions of both the measurable operating point and the faults of the system. Neural networks are used to estimate the healthy model's parameters, based on the measurable operating points, when no fault occurs within the system (this procedure is called the training of a healthy system). For this purpose, a modified version of recursive least squares algorithm with normalised signals and an output dead zone are employed. After the training of the healthy system, this recursive algorithm remains on-line to estimate the system parameters which, together with trained neural networks, are used to recognise, and differentiate, parameter changes which are caused either by the variation of the operating points or by faults