The daily 110 Mhz radio wave sky survey: statistical analysis of impulse phenomena from observation in 2012-2013
On the Pushchino Radio Astronomy Observatory of Lebedev Physical Institute by radio telescope BSA (Big Scanning Antenna) in 2012 started daily multi-beam observations at the frequency range 109- 112 MHz. The number of frequency bands range from 6 to 32, while the time constants range from 0.1 to 0.0125 sec. This data is an enormous opportunity for both short and long-term monitoring of various classes of radio sources (including radio transients), the Earth's ionosphere, interplanetary and interstellar plasma monitoring, search and monitoring for different classes of radio sources, etc. A specialized database was constructed to facilitate the large amount of observational data (http://astro.prao.ru/ cgi/out_img.cgi ). We discuss in this paper method of allocation from the database for impulse data of various types. By using the database allocated 83096 individual impulses in declination from +3 to +42 degrees for July 2012 – October 2013 from pulsars, scintillation sources and so one. In result we constructed homogeneous sample suitable for statistical analyzes.
One of the most sensitive radio telescopes at the frequency of 110 MHz is a Big Scanning Antenna (BSA) in Pushchino Radio Astronomy Observatory of Lebedev Physical Institute (PRAO LPI, Moscow region, Russia). Since 2012 in the BSA the continuous survey observation was started in multibeam mode in the frequency band of 109–112 MHz. Now 96 beams covering from −8 and up to +42° in declination are used. The number of frequency bands are 6 with a time resolution of 0.1 s and 32 bands with the time resolution of 0.0125 s. In a fast mode (32 bands, 0.0125 s) daily data flow is 87.5 GB (32 TB per year). The data provide a great opportunity for both short-term and long-term monitoring of the various radio sources. The sources are fast radio transients of different nature, such as fast radio bursts (FRB), possible counterparts of gamma-ray bursts (GRB), and sources of gravitational waves, the Earth’s ionosphere, interplanetary and interstellar plasma. Based on the BSA observations the database is constructed. We discuss data base prop- erties, the methods of transient search and allocation in database. Using this database we were able to detect 83096 individual transient events in the period of July 2012 – October 2013, which may correspond to pulsars, scintillating sources and fast radio transients. We also present first results and statistics of transients classification. In particular we report parameters of two candidates in new RRAT pulsars.
In the process of astronomical observations are collected vast amounts of data. BSA (Big Scanning Antenna) LPI used in the study of impulse phenomena, daily logs 87.5 GB of data (32 TB per year). Experts classified 83096 individual observations (on the segment of the study July 2012 - October 2013). Over 75% of the sample correspond to pulsars, twinkling springs and rapid radiotransmitter, and all other classes of observations belong to hardware failures, interference, the flight of the Earth satellite and aircraft. There were allocated 15 classes of observations.
Such a sample, divided into classes allows using the machine learning algorithms. It has become possible to develop an automated service for short-term/long-term monitoring of various classes of radio sources (including radiotransmitted different nature), monitoring the Earth's ionosphere, the interplanetary and the interstellar plasma, the search and monitoring of different classes of radio sources. Monitoring in this case refers to the automatic filtering and detection of a previously unclassified impulse phenomena.
Currently, for automatic filtering, statistical analysis methods are used. This report examines an alternative method supposed to be using neural network machine learning algorithm that processes the input into raw data and after processing by the hidden layer through the output layer determines the class of pulse phenomena.
Creating a neural network model, trained on a sample and performing a classification of previously unclassified impulse phenomena is performed using the cloud service Microsoft Azure Machine Learning Studio. The Web service has been created based on the model allows classifying single impulse phenomena in real time (Request / Reply) and data sampling for a certain period (Batch processing).
In the process of astronomical observations collected vast amounts of data. BSA (Big Scanning Antenna) LPI used in the study of impulse phenomena, daily logs 87.5 GB of data (32 TB per year). These data have important implications for both short-and long-term monitoring of various classes of radio sources (including radio transients of different nature), monitoring the Earth's ionosphere, the interplanetary and the interstellar plasma, the search and monitoring of different classes of radio sources. In the framework of the studies discovered 83096 individual pulse events (in the interval of the study highlighted July 2012 - October 2013), which may correspond to pulsars, twinkling springs, and a rapid radio transients. Detected impulse events are supposed to be used to filter subsequent observations. The study suggests approach, using the creation of the multilayered artificial neural network, which processes the input raw data and after processing, by the hidden layer, the output layer produces a class of impulsive phenomena.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The dynamics of a two-component Davydov-Scott (DS) soliton with a small mismatch of the initial location or velocity of the high-frequency (HF) component was investigated within the framework of the Zakharov-type system of two coupled equations for the HF and low-frequency (LF) fields. In this system, the HF field is described by the linear Schrödinger equation with the potential generated by the LF component varying in time and space. The LF component in this system is described by the Korteweg-de Vries equation with a term of quadratic influence of the HF field on the LF field. The frequency of the DS soliton`s component oscillation was found analytically using the balance equation. The perturbed DS soliton was shown to be stable. The analytical results were confirmed by numerical simulations.
The Handbook of CO₂ in Power Systems' objective is to include the state-of-the-art developments that occurred in power systems taking CO₂ emission into account. The book includes power systems operation modeling with CO₂ emissions considerations, CO₂ market mechanism modeling, CO₂ regulation policy modeling, carbon price forecasting, and carbon capture modeling. For each of the subjects, at least one article authored by a world specialist on the specific domain is included.
I give the explicit formula for the (set-theoretical) system of Resultants of m+1 homogeneous polynomials in n+1 variables