arXiv subjects
Publications and source records attributed to Alexey Egorov.
This contribution presents two exponential stability criteria for linear systems with multiple pointwise and distributed delays. These results (necessary and sufficient conditions) are given in terms of the delay Lyapunov matrix and the fundamental matrix of the system. An important property is that the stability test requires a finite number of mathematical operations.
An overview of stability conditions in terms of the Lyapunov matrix for time-delay systems is presented. The main results and proof are presented in details for the case of systems with multiple delays. The state of the art, ongoing research and potential extensions to other classes of delay systems are discussed.
A Lyapunov-Krasovskii functional with prescribed derivative whose construction does not require the stability of the system is introduced. It leads to the presentation of stability/instability theorems. By evaluating the functional at initial conditions depending on the fundamental matrix we are able to present necessary and sufficient stability conditions expressed exclusively in terms of the delay Lyapunov matrix for integral delay systems. Some examples illustrate and validate the stability conditions.
Once completed, the Cherenkov Telescope Array (CTA) will be able to map the gamma-ray sky in a wide energy range from several tens of GeV to some hundreds of TeV and will be more sensitive than previous experiments by an order of magnitude. It opens up the opportunity to observe transient phenomena like gamma-ray bursts (GRBs) and flaring active galactic nuclei (AGN). In order to successfully trigger multi-wavelength observations of transients, CTA has to be able to alert other observatories as quickly as possible. Multi-wavelength observations are essential for gaining insights into the processes occurring within these sources of such high energy radiation. CTA will consist of approximately 100 telescopes of different sizes and designs. Images are streamed from all the telescopes into a central computing facility on site. During observation CTA will produce a stream of up to 20 000 images per second. Noise suppression and feature extraction algorithms are applied to each image in the stream as well as previously trained machine learning models. Restricted computing power of a single machine and the limits of network's data transfer rates become a bottleneck for stream processing systems in a traditional single-machine setting. We explore several different distributed streaming technologies from the Apache Big-Data eco-system like Spark, Flink, Storm to handle the large amount of data coming from the telescopes. To share a single code base while executing on different streaming engines we employ abstraction layers such as the streams-framework. These use a high level language to build up processing pipelines that can transformed into the native pipelines of the different platforms. Here we present results of our investigation and show a first prototype capable of analyzing CTA data in real-time.