bluuebunny/crossref_metadata_2025_split huggingface dataset:
Model of a real cycle of a power installation with a real-gas working fluid
Authors: Chirkov A Yu , Egorov K S , Ganeev K B , Zuev T R 
Published: November 2019

Abstract
The modelling of the working cycle of a closed cycle power installation with real gas is considered. The properties of a real gas are investigated using carbon dioxide as an example. The indicators of the real thermodynamic cycle of the installation when working on carbon dioxide are investigated. Particular attention is paid to the simulation of heat transfer in the recuperator.
Method of manual symmetrization of electric networks
Authors: Zakurdaev R Yu , Chernetskaya I E 
Published: November 2019

Abstract
The article describes a method of reducing asymmetry in the electrical network supplying the utility load. The essence of the method lies in the uniform distribution of loads by switching branches. The results of experimental studies are presented, confirming that the proposed method actually reduces the asymmetry of the network; and also reduces power loss and voltage loss during the transmission of electricity. It gives a comparison with the existing method of uniform distribution. The high efficiency of the proposed method has been experimentally proved.
Implementation of the FDTD method block algorithm in the MATLAB language using a graphics processing unit. 2D-decomposition case
Authors: Morunov N D , Golovashkin D L 
Published: November 2019

Abstract
The problem of limited video memory when organizing parallel computing using the FDTD method on a non-professional graphics processor was considered in this article. As a solution, a block algorithm of the FDTD method with 2-D decomposition and its implementation in the MATLAB language are proposed. As a result, the problem of limited graphics memory was solved, the maximum possible discretization of the grid in calculations on the GPU was expanded from 10 million to 85 million nodes with an average acceleration of 7,5 times for the two-dimensional case of the FDTD method.
Investigation of the multiple comparisons problem in the analysis of the wave train electrical activity of muscles in Parkinson’s disease patients
Authors: Sushkova O S , Morozov A A , Gabova A V , Karabanov A V 
Published: November 2019

Abstract
A new method has been developed for the analysis of the wave train electrical activity of muscles based on the wavelet analysis and ROC analysis that enables to study the time-frequency characteristics of electromyograms (EMG) and acceleration (ACC) signals in patients with Parkinson’s disease (PD). The idea of the method is to find local maxima (that correspond to the wave trains) in the wavelet spectrogram and to calculate various characteristics describing these maxima: the leading frequency, the duration of the wave trains in periods, the bandwidth of the wave trains, the number of wave trains per second. The degree of difference between a group of patients and a control group of volunteers in the space of these parameters is analyzed. ROC analysis is used for this purpose. The functional dependence of AUC (the area under the ROC curve) on the values of the boundaries of parameters’ ranges under consideration is investigated. The developed method involves investigation of a big number of ranges of selected characteristics; therefore a multiple comparisons problem appears during statistical hypothesis testing. It is necessary to find a compromise between the degree of detail of the studied characteristics and the magnitude of the Bonferroni correction. The paper describes the statistical hypothesis testing on the data of early Parkinson’s disease patients.
CUDA parallel programming technology application for analysis of big biomedical data based on computation of effectiveness features
Authors: Ilyasova N Yu , Shikhevich V A , Shirokanev A S 
Published: November 2019

Abstract
This paper proposes the technology for large biomedical data analysis based on CUDA computation. The technology was used to analyze a large set of fundus images used for diabetic retinopathy automatic diagnostics. A high-performance algorithm that calculates effective textural characteristics for medical image analysis has been developed. During the automatic image diagnostics, the following classes were distinguished: thin vessels, thick vessels, exudates and a healthy area. The study of the mentioned algorithm efficiency was conducted with 500x500-1000x1000 pixels images using a square 12x12 dimension window. The acceleration relationship between the developed algorithm and various data sizes was demonstrated. The study showed that the algorithm effectiveness can be affected by certain characteristics of the image, e.g. its clarity, shape of exudate zone, variability of blood vessels, and optic disc location.
Detection of epileptic seizures in EEG signals during long-term monitoring of patients after traumatic brain injury
Authors: Kershner I A , Sinkin M V , Obukhov Yu V 
Published: November 2019

Abstract
Long-term (several days) monitoring of epileptiform activity in scalp EEG of posttraumatic brain injury patients is an important task. EEG signals contain epileptiform seizures and similar signals of myographic activity associated with chewing. Both epileptiform activity and chewing artifacts appear in the same frequency range, which complicates their differentiation. To distinguish epileptiform activity from chewing artifacts, a method based on the wavelet spectrogram analysis of EEG is proposed. EEG wavelet spectrogram contains broadband peaks at times corresponding to peak-wave epileptiform activity on the one hand, and peaks of myographic activity at chewing on the other hand. The periodicity of these peaks is investigated. The difference in the period dispersion of epileptiform peaks and chewing peaks are found.
An SGD-based meta-learner with “growing” descent
Authors: Kulikovskikh I , Prokhorov S , Legović T , Šmuc T 
Published: November 2019

Abstract
The paper considers the problem of accelerating the convergence of stochastic gradient descent (SGD) in an automatic way. Previous research puts forward such algorithms as Adagrad, Adadelta, RMSprop, Adam and etc. to adapt both the updates and learning rates to the slope of a loss function. However, these adaptive methods do not share the same regret bound as the gradient descent method. Adagrad provably achieves the optimal regret bound on the assumption of convexity but accumulates the squared gradients in the denominator that dramatically shrinks the learning rate. This research is aimed at introducing a generalized logistic map directly into the SGD method in order to automatically set its parameters to the slope of the logistic loss function. The optimizer based on the population may be considered as a meta-learner that learns how to tune both the learning rate and gradient updates with respect to the rate of population growth. The present study yields the “growing” descent method and a series of computational experiments to point out the benefits of the proposed meta-learner.
Analysis of geomagnetic field data during periods of increased solar activity and magnetic storms
Authors: Mandrikova O V , Rodomanskay A I , Mandrikova B S 
Published: November 2019

Abstract
We present and describe an automated method for analysis of magnetic data and the detection of geomagnetic disturbances based on wavelet transformation. The parameters of the computational algorithms allow us to estimate the characteristics of non-uniformly scaled peculiar properties in the variations of the geomagnetic field that arise during periods of increasing geomagnetic activity. The analysis of geomagnetic data on the eve and during periods of magnetic storms was carried out on the basis of the method according to the network of ground stations. Periods of increasing geomagnetic activity are highlighted which precede and accompany magnetic storms. The dynamic of variation of the geomagnetic field in the auroral zone is considered in detail.
Detection of quasi-harmonic signals with a priori unknown parameters in strong additive noise by machine learning methods
Authors: Nevzorov A A , Orlov A A , Stankevich D A 
Published: November 2019

Abstract
Signals with a priori unknown parameters in strong noise are used in various fields of science and technology. This paper is devoted by features and limits deep neural networks for signal detection. We study quasi-harmonic signals with a priori unknown parameters. Neural network method was compared with classical methods for detecting signals in terms of accuracy and speed. We use realistic models of hexogen nuclear quadruple resonance (NQR) signals with parameters dependence by temperature. Experiments show that proposed method is more accurate and one hundred times faster than alternative ones. We achieve a probability of NQR signal detection about 95%, when signal-to-noise ratio is -15 dB and the signal parameters are unknown. When the signal-to-noise ratio is -20 dB, probability of NQR signal detection is 80%.
Binary data storage, retrieval and feature calculation on Netezza PureData system for Analytics
Authors: Donon Y , Paringer R , Kupriyanov A 
Published: November 2019

Abstract
The storage of images in databases has long been a delicate matter but comes now more and more in use. This kind of data is in most DBMS limited in storage size to 64kb, making necessary to divide large objects into fragments; moreover, programming languages also present limitations in data retrieval. In this paper, we experimented the retrieval, reconstitution, feature calculation of pictures using different fragment size and determined the optimal one. In this work, the server used was a Netezza model from the N2001 line. In this work, we present the architecture of the database, our research and bring recommendation about the optimal fragmentation size to store images into a database.
Evolutionary methods in modelling behaviour of complex system
Authors: Mokshin V V , Sharnin L M 
Published: November 2019

Abstract
In this paper, a recursive-regression approach is formulated in the formation of a functioning model of a complex system. The complex system consist of the object of study represented by a set of related factors. Some of these factors can be specified from the outside, whereas other factors contain information generated by system. We demonstrate that time-dependence of these factors can be reproduced by the nonlinear regression model. The proposed sub-course combines the method of group accounting of arguments with standard regression analysis adapted to fast dynamic processes. As an example, recursive-regression modelling of the functioning of an industrial enterprise was performed. This allows us to predict a possible behaviour of the system and to identify the so-called significant factors that have a significant impact on the behaviour of the system. To demonstrate validity of the method, we apply it to analyse the data characterizing a manufacturing company. Also paper includes meteorological data analysis. An important advantage of the proposed approach is that models for effective factors are formed automatically at each time step. The given results make possible to determine the optimal parameters of introduced algorithm and number of time observations for analysis.
Intelligent OFDM telecommunication system. Part 3. Anti-eavesdropping and anti-jamming properties of system, based on many-parameter wavelet and Golay transforms
Authors: Labunets V G , Komarov D E , Chasovskikh V P , Smetanin J G , Ostheimer E V 
Published: November 2019

Abstract
In this paper, we aim to investigate the superiority and practicability of many-parameter wavelet and Golay transforms (MPWT and MPGT) from the physical layer security (PHY-LS) perspective. We propose novel Intelligent OFDM-telecommunication system (Intelligent-OFDM-TCS), based on many-parameter transforms (MPTs). New system uses inverse MPT for modulation at the transmitter and direct MPT for demodulation at the receiver. The purpose of employing the MPTs is to improve the PHY-LS of wireless transmissions against to the wide-band anti-jamming communication. Each MPT depends on finite set of independent Jacobi parameters (angles), which could be changed independently one of another. When parameters are changed, multi-parametric transform is changed too taking form of a set known (and unknown) orthogonal (or unitary) wavelet transforms. We implement the following performances as bit error rate (BER), symbol error rate (SER), peak to average power ratio (PAPR), the Shannon-Wyner secrecy capacity (SWSC) for novel Intelligent-MPWT-OFDM-TCS. Previous research has shown that the conventional OFDM TCS based on discrete Fourier transform (DFT) has unsatisfactory characteristics in BER, PARP, SWSC and in anti-eavesdropping communications. We study Intelligent-MPT-OFDM-TCS to find out optimal values of angle parameters of MPT optimized BER, PAPR, SWSC, anti-eavesdropping effects. Simulation results show that the proposed Intelligent OFDM-TCS have better performances than the conventional OFDM system based on DFT against eavesdropping.
Method of analysis of cosmic ray data based on neural networks of LVQ
Authors: Mandrikova O V , Geppener V V , Mandrikova B S 
Published: November 2019

Abstract
An automated method for analysis of cosmic ray data and for detection of sporadic effects is described. The method is based on the application of multi-scale wavelet decompositions and neural networks of vector quantization. Using the method as the base, cosmic ray dynamics was investigated during increased solar activity and magnetic storms. Sporadic effects preceding and accompanying magnetic storms were detected. The method showed its efficiency for data real time analysis and detection of sporadic effects including those of low amplitude. The results are important in the tasks of space weather forecast.
Machine-learning algorithms for helicopter hydraulic faults detection: model based research
Authors: Gareev A M , Minaev E Yu , Stadnik D M , Davydov N S , Protsenko V I , Popelniuk I A , Nikonorov A V , Gimadiev A G 
Published: November 2019

Abstract
The problem of automatic reliability monitoring and reliability-centered maintenance is increasingly important today. In this paper, we compare the accuracy of four machine learning approaches for fault detection in a hydraulic system. The first three approaches are based on SVM classifiers with linear, polynomial and RBF kernels and the last one is a gradient boosting on oblivious decision trees. We evaluate algorithms on the synthetic dataset generated by our simulation model of the helicopter hydraulic system and show that high accuracy fault detection can be achieved.
Dynamical physically structured data modeling vs. classical time series analysis: A case study related to clinical trial data analysis
Authors: Semushin I V , Tsyganova Yu V 
Published: November 2019

Abstract
At the time series analysis’s core is the fitting of a preselected model—in the most straightforward case a linear combination of basis functions—to experimental data with one of the performance indices, for example, least squares criterion. However, no matter what the type of such a model is and whatever be the fitting performance index, such an approach does not stipulate for reliance on the understanding those dynamical laws of physics dictating the observable data behavior, given their complexity or uncertainty.
Our research looks into a question: “What benefits or advantages could mathematical modeling of such laws be capable of giving when included in nature or experimental data analysis?—nowhere more so than in deciding on life-changing events based on time series analysis in medical practice”.
Subject-oriented computing environment for solving large-scale problems of energy security research
Authors: Bychkov I V , Oparin G A , Feoktistov A G , Sidorov I A , Gorsky S A , Kostromin R O , Edelev A V 
Published: November 2019

Abstract
We represent a new approach to the development and use of a subject-oriented heterogeneous distributed computing environment for solving large-scale problems of energy security research. Both the development of scalable applications (distributed applied software packages) and the computing resources integration are conducted using the Orlando Tools framework. It supports the collaborative application development based on the continuous integration of their software. Computation management tools in Orlando Tools are integrated with a multi-agent system. We propose special multi-agent tools for the heterogeneous resource allocation in the environment. These tools apply knowledge about the subject domain specifics of solved-problems and computing environment characteristics. The effectiveness of such management is highlighted by the results of solving practical problems of energy security research.
Similarity-based decision tree induction method and its application to cancer recognition on tomographic images
Authors: Berikov V B , Pestunov I A , Kozinets R M , Rylov S A 
Published: November 2019

Abstract
The paper proposes a pattern recognition method using a modification of the class of logical decision functions presented in the form of decision tree. Instead of standard statements corresponding to the tree nodes, in which a variable is tested for a certain set of its values, a more general type of statements is used regarding the similarity of the point in question to different subsets of the observations. At the same time, to determine the degree of similarity, various metrics and subspaces of features can be used. This type of decision tree allows one to obtain more complex decision boundaries, which at the same time have a clear logical interpretation for the user. Several tree induction strategies are considered based on data transformation using support points selected with Relief, SVM, and k -means procedures. The method is experimentally investigated on the problem of tomographic images analysis, as well as on several synthetic datasets. Experiments have shown that the proposed method gives more accurate predictions than CART, SVM, kNN classifiers and deep convolutional neural network (AlexNet).
Recognition of Herpes Viruses on the Basis of a New Metric for Protein Sequences
Authors: Sulimova V , Seredin O , Mottl V 
Published: November 2019

Abstract
This paper addresses the problem of intellectual human herpes viruses recognition based on the analysis of their protein sequences. To compare proteins, we use a new dissimilarity measure based on finding an optimal sequence alignment. In the previous work, we proved that the proposed way of sequence comparison generates a measure that has properties of a metric. These properties allow for more convenient and effective use of the proposed measure in further analysis in contrast to the traditional similarity measure, such as Needleman-Wunch alignment. The results of herpes viruses recognition show, that the metric properties allow to improve the classification quality. In addition, in this paper, we adduce an updated computational scheme for the proposed metric, which allows to speed up the comparison of proteins.
Anomalies detection in social services data in the sphere of digital economy
Authors: Khripunov P V , Minaev E Y , Protsenko V I , Davydov N S , Nikonorov A V 
Published: November 2019

Abstract
This article addresses the study of the anomaly and fraud detection problem in the data from social services. The problem of detecting anomalies is extremely relevant for data-driven processes in the digital economy. In this paper, we propose a two-step approach for the detection of anomalies using auto-encoders and the conjugacy indicator. An experimental study of the efficiency of the proposed algorithms was conducted using open-access data set.
Management of thermal and mechanic flow characteristics in the output channels of a turbocharger centrifugal compressor
Authors: Plotnikov L V , Zhilkin B P , Brodov Yu M 
Published: November 2019

Abstract
It is known that the thermal and mechanical characteristics of the air flow in the output channel of a turbocharger compressor largely determine the effectiveness of the gas exchange processes quality of a piston engine. The studies were carried out on an experimental installation containing a turbocharger, output channels of different configurations, a measuring base, and a data collection system. It was found that stabilization of the flow in the compressor output channel leads to a significant increase in heat transfer intensity (up to 25 %) compared to the baseline pipeline while simultaneously reducing the turbulence number by up to 30 %. A more significant increase in heat transfer intensity (up to 30 %) was observed in the output channel of the compressor with grooves compared to the base channel while simultaneously increasing the turbulence number by up to 12 %. The proposed configuration of the output channels of the compressor can be used to intensify heat transfer for the natural cooling of the air during the intake process. The configuration with a leveling grid can be used to stabilize the gas-dynamic flow parameters in order to reduce the hydraulic resistance of the intake system of a turbocharged engine.
Spatial Temperature Distribution of an Open Square Capillary Tube Wall during Passage of a Taylor Liquid Plug
Authors: Ya Gatapova E , Khandekar S 
Published: November 2019

Abstract
Taylor plug/bubble flow is a predominant two-phase flow pattern occurring in several microfluidic devices such as in microchannel heat sinks, fuel cells, pulsating heat pipes, labon-chips, microreactors, etc. Such flows are characterized by an alternating sequence of liquid plug and gas bubbles, whose ensuing local transport characteristics (heat and mass transfer) are complex and still not fully understood. Presence of gas-liquid interfaces, thin-film dynamics, moving contact lines, wettability, contact angle hysteresis, are some of the physical phenomena involved in Taylor bubble flow systems that manifests into its complex transport characteristics. Understanding the local interfacial transport mechanism is of vital importance to develop a comprehensive model for micro-scale devices utilizing Taylor bubble flow. In this background, we probe locally into the local thermo-hydrodynamics of ‘unit-cell’ of Taylor plug flow, which is essentially an isolated liquid plug moving inside a mini square capillary copper tube (side = 2 mm), surrounded by gas from both its sides, and try discerning local evaporation dynamics, with the help of IR thermography. This study focuses on the interplay of conjugate conduction, local thin film hydrodynamics formed behind the moving liquid plug, and its subsequent evaporation. We obtain local temperature distribution in the meniscus region, as the plug moves through the capillary tube. As the cold plug passes through the tube, local temperatures drop drastically, the transient reaches the heater-tube interface a bit delayed and there the drop in temperature is modest. The study clearly reveals that there is a strong thermo-hydrodynamic coupling between the tube wall and the moving Taylor liquid plug.
Influence of the thermal parameters on the bubble heat balance at transient boiling of subcooled water
Authors: Levin A A , Khan P V 
Published: November 2019

Abstract
This paper considers the development of the approach presented [1–3] to the determination of heat trasnfer in subcooled boiling flow additively from the components of forced convection and heat fluxes associated with the presence of bubbles on the surface. The case of non-stationary heat release in a vertical channel with an uprising fluid was numerically studied on the basis of the experimental data from [5]. We have studied the effect of the fluid subcooling and the heat flux on the characteristics of transient boiling.