bluuebunny/crossref_metadata_2025_split huggingface dataset:
The AME 2020 atomic mass evaluation (I). Evaluation of input data, and adjustment procedures*
Authors: Huang W.J. , Wang Meng , Kondev F.G. , Audi G. , Naimi S. 
Published: January 2021

Abstract
This is the first of two articles (Part I and Part II) that presents the results of the new atomic mass evaluation, AME2020. It includes complete information on the experimental input data that were used to derive the tables of recommended values which are given in Part II. This article describes the evaluation philosophy and procedures that were implemented in the selection of specific nuclear reaction, decay and mass-spectrometric data which were used in a least-squares fit adjustment in order to determine the recommended mass values and their uncertainties. All input data, including both the accepted and rejected ones, are tabulated and compared with the adjusted values obtained from the least-squares fit analysis. Differences with the previous AME2016 evaluation are discussed and specific examples are presented for several nuclides that may be of interest to AME users.
Inverse Problem Solving Approach Using Deep Network Trained by GAN Simulated Data
Authors: Krylov Sergei , Krylov Vladimir 
Published: January 2021

Abstract
In this paper authors deal with tasks of reliably recover a hidden multi-dimensional model parameter from indirect process observations. Such task is known as inverse problem. There are a lot of inverse problems that have practical value, for example in seismic wave propagation, low-dose tomography. To solve many of these problems in a practical style, this article proposes an approach based on the many simulations of the corresponding forward problem and using the set of simulation data as the training dataset. Most of physical processes have computer models that generate precise results. The existing simulators provide ways to predict process output by input parameters. A difficulty in solving of most inverse problems is that the solution is sensitive to variations in data, which is referred to as ill-posedness. From broad spectrum of methods to overcome ill-posedness authors use machine learning model trained on special simulated data. The paper describes the deep network model using some regularization. The key idea is to use Generative Adversarial Network (GAN) to generate correct input parameters values and support the unique existence. This network is trained by parameter examples that are real solutions of inverse problem. The small manually built dataset transforms to infinite dataset automatically by GAN. The augmented dataset feeds the simulator to get output data to train deep learning network. The network has regularization layers to support stability. The paper describes details of this model using deep augmentation to solve inverse problems on the easy example: the task of throwing a heavy ball at an angle to the horizon, taking into account the force of friction against air.
wUUNET: advanced fully convolutional neural network for multiclass fire segmentation
Authors: Bochkov Vladimir , Kataeva Liliya 
Published: January 2021

Abstract
This paper describes a solution of computer vision task concerning multiclass fire segmentation to get and show location of red, yellow and orange flame. We use UNet model as the best open-sourced convolutional neural network baseline. Based on this model we introduce UUNet-concatenative and wUUNet models. Since the multiclass fire segmentation task is solved for the first time in science, we collect the appropriate dataset and use the dataset-labeling alignment via look-up-tables. Also we compare models trained by Soft Dice and Jac-card indexes in combination with binary cross-entropy as a loss functions. Paper shows the problem of accuracy loss at bounding nodes of splitting the frame. As a solution we introduce combinational methods of partially intersected areas. The comparison of the used models and calculation schemes is demonstrated and the corresponding conclusions of the investigation are made.
Synthesized optimal control based on machine learning
Authors: Diveev Askhat , Shmalko Elizaveta 
Published: January 2021

Abstract
The article discusses symbolic regression methods as a machine learning technology. The technique is tested on a complex problem of control systems synthesis. A new type of control based on changing the position of a stable equilibrium point is proposed. The implementation of such control requires the construction of a double feedback loop. The inner contour ensures the stability of the control object relative to some point in the state space. The outer contour provides optimal control of the stable equilibrium point position. To implement control, symbolic regression methods are used as machine learning technologies. It is shown that such a control is the least sensitive to external disturbances and model uncertainties.
The effect of density thresholding on the EEG network construction
Authors: Zakharov Ilya , Adamovich Timofey , Tabueva Anna , Ismatullina Victoria , Malykh Sergey 
Published: January 2021

Abstract
The procedure of thresholding for graph construction is one of the common steps in calculating networks of brain connections. However, this procedure can lead to incomparable results from different studies. In the present study we aim to test the effect of thresholding or algorithmic reduction of the number of connected nodes on the construction of a set of widely used connectivity graph metrics derived from EEG data. 164 people took part in our study. Participants were recruited via social networks. EEG was recorded during resting state. At the beginning of the procedure each participant was asked to relax and not to think about anything. Source reconstruction was performed using standard source localization pipeline from MNE-package. Desikan-Killiany Atlas was used for cortical parcellation with 34 ROI per hemisphere. Synchronization was estimated with weighted phase lag index in 4–30 Hz frequency range for eyes closed and eyes open separately. We have found that All metrics except average participation coefficient vary monotonously as a function of density level (moreover, we have found, that for Cluster Coefficient, more than 95% and for the Characteristic Path Length ∼50% of the variance is related to thresholding cut-off). The different data-driven approaches to the network construction leads to significant changes in the group-level graph metrics and can eliminate the variance in the data that can be crucial for individual differences studies.
A system for off-line validation of medical data in DICOM archive
Authors: Magomedov SH G 
Published: January 2021

Abstract
The problem of consistency of medical data in Hospital Data Management Systems is considered in the context of correctness of medical images itself to minimize possible harm from spurious DICOM files. The approach should be considered as an addition to other securing techniques like watermarking, encrypting, testing conformance with the standard. To achieve amenable accuracy in practice two aspects are taken into account: correctness of periodicity and correctness of image data (time series) itself for considered modality. This paper proposes an architecture of an information system and network filter integrated to it to provide facilities for analysis and alert management. The architecture is designed to perform the analysis of incoming data streams within components working in a clustered manner, providing horizontal scalability and fault tolerance to be BigData-ready.
The effective interaction of the models of classification with the usage of auction heuristics
Authors: Barakhnin V B , Kozhemyakina O Yu , Pastushkov I S 
Published: January 2021

Abstract
The article proposes the approach that allows to make multi-model systems of classification systems more suitable for solving the practical problems in the field of information technologies. To achieve this, it was necessary to provide a stable system response time to support SLA (service-level agree-ment) and, on the other hand, to minimize the downtime of server hardware. The first requirement would allow to give the requests correctly and quickly to the client, and the second one would make the cost of renting servers more justified. The main idea of the proposed approach is to obtain the pre-dictions with the highest possible accuracy in a fixe time. We considered three auction models: Dutch auction (rate decreasing), English auction (rate increasing) and the adapted version of Vickrey auction (highest rate switched off). In all cases, we used the highest probability of the mark as a rate, and we set a time parameter, after which the prediction is recognized as final. The obtained results and their comparison with the methods of ensembling and balancing allow us to conclude that the proposed approaches can be use-ful in the development of multi-model systems of classification.
Generation of echocardiographic 2D images of the heart using cGAN
Authors: Zyuzin Vasily , Komleva Julia , Porshnev Sergey 
Published: January 2021

Abstract
One of the most significant tasks of echocardiography is the automatic delineation of the cardiac structures from 2D echocardiographic images. Over the past decades, the automation of this task has been the subject of intense research. One of the most effective approaches is based on the deep convolutional neural networks. Nonetheless, it is necessary to use echocardiogram frames of the cardiac muscle, which show the boundaries of the cardiac structures labeled/annotated by experts/cardiologists to train it. However, the number of databases containing the necessary information is relatively small. Therefore, generated echocardiogram frames are used to increase the amount of training samples. This process is based on the ultrasound images of the heart, annotated by experts. The article proposes an improved method for generating echocardiograms using a generative adversarial neural network (GAN) with a patch-based conditional discriminator. It has been demonstrated that it is possible to improve the quality of generated echocardiogram frames in both two and four chamber views (AP4C, AP2C) using the masks of cardiac segmentation with sub-pixel convolution layer (pixel shuffle). It is demonstrated that the proposed approach makes it possible to generate ultrasound images, the structure of which corresponds to the specified segmentation masks. It is expected that this method will improve the accuracy of solving the direct problem of automatic segmentation of the left ventricle.
Computer vision system for the automatic asbestos content control in stones
Authors: Zyuzin Vasily , Ronkin Mikhail , Porshnev Sergey , Kalmykov Alexey 
Published: January 2021

Abstract
The paper discusses the results of the first stage of research and development an innovative computer vision system for the automatic asbestos content control in stones veins at an asbestos processing factory. The discussed system is based on the applying of a semantic segmentation artificial neural networks, in particular U-Net based network architectures for solving both: the boundaries of stones segmentation and veins inside them. At the current stage, the following tasks were solved. 1. The discussed system prototype is developed. The system is allowing to takes images of the asbestos stones on the conveyor belt in the near-infrared range (NIR), avoiding the outer lighting influence, and processing the obtaining images. 2. The training, validation and test datasets were collected. 3. Substantiated the choice of the U-Net based neural network. 4. Proposed to estimate the resulted specific asbestos concentration as the average relation of all the veins square to all stones square on the image. 5. The resulted deviation between obtained and laboratory given results of the asbestos concentration is about 0.058 in the slope of graduation curve. The farther improvement recommendations for the developed system are given.
Classification of negative publication in mass media using topic modeling
Authors: Yakunin Kirill , Mukhamediev Ravil , Kuchin Yan , Musabayev Rustam , Buldybayev Timur , Murzakhmetov Sanzhar 
Published: January 2021

Abstract
The paper proposes a method for evaluating text documents by arbitrary criteria, combining the topic modeling on the text corpora and multiple-criteria decision making. The evaluation is based on an analysis of the corpora as follows: the conditional probability distribution of media by topics, properties and classes is calculated after the formation of the topic model of the corpora. Weights assigned by experts to each topic along with topic model can be applied to evaluate each document in the corpora according to each of the considered criteria and classes. The proposed method was applied to a corpus of 804829 news publications from 40 Kazakhstani sources published from 01.01.2018 to 31.12.2019, in order to classify negative information on socially significant topics. A BigARTM model was calculated (200 topics) and the proposed model was applied. Experiments confirm the general possibility of evaluating the sentiment of publications using the topic model of the text corpora, since ROC AUC score of 0.93 was achieved on the classification task.
Water environment monitoring with an autonomous unmanned surface vessel
Authors: Chensky D A , Aisuev B B , Chensky A G , Fedotov A P , Poletaev A S 
Published: January 2021

Abstract
The paper discusses the use of catamaran-type ultra-small unmanned vessels for studying lakes, rivers and different offshore areas. The key features of a surface vessel designed in Irkutsk Technical University are presented. The article describes major systems of the vessel and concepts of autonomous functioning and remote control. The apparatus can fulfil automatically mapping, searching of sunken objects, hydroacoustic scanning of bottom relief and underwater video recording without the use of additional boats. It is capable to inspect 100 linear kilometers (∼15 km2) of the water area per one day. The use of solar panels and Li-ion batteries charged during a single sunny day allows it to work for several days without use of any external sources of energy for recharging. All systems have been checked and debugged in the laboratory and the designed vessel was tested in Irkutsk reservoir. The field testing showed that the error of coordinates determining was within 2 meters. The planned and actual routes almost coincided with an accuracy better than 3 meters.
Application of particle-size analysis in various industries
Authors: Gerasimov A M , Eremina O V , Cherkasova M V , Dmitriev S V 
Published: January 2021

Abstract
For various materials, rapid generation of results is always critical. Particle-size analysis of processing products using a laser-based particle-size analyzer is a valid alternative to the traditional sizing test. The article presents the results of studies conducted using Microsizer 201C equipment by the example of layered aluminosilicates and metal powders for additive manufacturing. The size analysis for these products is generally complicated by their ability to stick and agglomerate. Ultrasonic mixing does not harm the material analyzed and promotes uniform distribution of the sample during analysis. The results of granulometric analysis of kaolin ores obtained after different heat treatment are presented. The results of air-electric classification of metal powders obtained after vibration disintegration of metal chips are presented. The variety of raw materials shows a wide range of applications of this method in industry.
Moving object recognition system on a pedestrian crossing
Authors: Semenova E I , Kyarimova Sh D , Kukartsev V V , Leonteva A A , Ogol A R , Bondarev A S 
Published: January 2021

Abstract
The article discusses the development of a system for recognizing people at a pedestrian crossing. The recognition system includes a trained classifier and two sets of images taken from an open database containing images of city streets from outdoor cameras. Input information prepared using PictureCropper application. The classifier was tested based on the prepared sets of images. The result is shown in the figure, and after testing, the metrics were calculated, necessary to assess the effectiveness of the resulting classifier for each set. System is being developed for specialists, which will allow individual and centralized adjustment of the adjustment of pedestrian phases for each road situation, both for the local system of controllers, and in accordance with the existing algorithms of coordination systems with the expansion of such.
Scanning steel junctions using eddy current probe
Authors: Ishkov A , Malikov V , Dmitriev S , Fadeev D , Katasonov A , Rykova K 
Published: January 2021

Abstract
The aim of this research is the process of developing a miniature probe for studying electrical steel. The issue of safety when using the products made of this type of steel, assessment and forecast of their quality make this topic relevant. We have found it possible to study the characteristics of steel at different depths by changing the signal amplitude generated by the eddy current probe. The novelty of this research is due to the use of a superminiature eddy current probe of a transformer type that can perform point measurements (0.05 mm sections) of ferromagnetic materials using the eddy current method and is based on the study of conductivity of electrical steel. A hardware and software package is designed based on the developed probe. It can test steel at its boundary with a dielectric. A system that can automatically move the probe over a controlled object at a preset speed has been developed. The study consisted of continuous and discrete measurements that were made by moving the probe over a ferromagnetic and dielectric material at a given speed and scanning the object in 0.1 mm increments. The results of the test performed on samples at different measurements and at different frequencies from 2000 to 20000 Hz are indicated. The study showed that the magnetic field lines of steel, which occur as a result of the impact of the eddy current probe on the ferromagnetic material, have a strong effect on the signal received by the eddy current probe after it is removed from the controlled object at a distance of 1 mm. The signal frequency on the exciting coil of the eddy current probe has a significant influence on this process. The conducted tests have demonstrated the nonlinear nature of the dependency of the probe response when it passes through the steel-dielectric boundary; the mathematical regularity of this dependency is revealed. The effect of the gap between the eddy current probe and the object of study is also analyzed.
Evaluation of the effectiveness of dust screens and the possibilities of taking into account their influence in software models
Authors: Ivanov A V , Strizhenok A V 
Published: January 2021

Abstract
The variety of approaches used to reduce dust load from the enterprises of the mineral resource complex is wide. Often the normative methodological base shows a lag behind the development and implementation of new devices and methods of protection. The article considers an example of a granite quarry in the Leningrad region of Russia, on which the construction of dust screens was proposed. The impact of the enterprise’s facilities on the residential area has been assessed. The inverse subsurface nature of the movement of air and the spread of dust from quarry facilities is revealed. Proposed placement of a semi-permeable dust screen along the edge of the quarry provides comprehensive dust protection. An assessment was made of the negative impact of the facility on atmospheric air after installing a dust screen with various options for methodological accounting for the impact of such structures on reducing dust transfer outside the quarry field.
Physical principles of operation and basic capabilities of the four channel receiver 1288хк1т (mf-01)
Authors: Nikitin P V , Ryabov I V , Bazhenov R I , Gorokhova R I , Zueva T G 
Published: January 2021

Abstract
In radar detection and radio navigation ensuring the reception of signals transmitted over the meteor channel of radio communication is often controversial[1-3]. Solving these problems requires the ability to synthesize precision signals directly[4, 5] for heterodyning systems in addition to modern analog-to-digital conversion devices, correlation analysis, and statistical processing of signal-noise complex. The step of adapting four channel receivers to dynamically changing conditions of transmitting information may include determining the composition of the signal-noise mixture, determining the phase characteristics of the communication channel, estimating the channel transmission rate, and obtaining QoS information. In addition, adaptation often implies the use of a logical channel control loop, which entails the need for complex feedback procedures. Software-defined radio systems are radio and telecommunication systems that can be tuned to an arbitrary frequency band and receive various types of modulated signal, consisting of programmable equipment with program control. SDR performs a significant part of digital signal processing on a conventional personal computer, signal processor or FPGA. In such systems and complexes, the parameters are changed by software configuration. The article considers the implementation of DDS for heterodyning an intermediate frequency signal on the FPGA basis for four channel receiver 1288хк1т (mf-01), which allows you to work with both narrow-band and wide-band communication channels (with simultaneous operation of 4 channels, the band can be expanded) and can be used in modern telecommunication communication systems.
Combustion optimization in gas burners of reverberatory furnaces during the melting of nickel alloys
Authors: Quiroz Cabascango V E , Bazhin V Yu 
Published: January 2021

Abstract
In recent years, due to climate change environmental sustainability programs have been created that require the search for alternatives to reduce air emissions and reduce fuel consumption. The combustion industry is looking for an effective solution to increase the life cycle of its equipment and improve combustion efficiency without neglecting safety or savings. The efficiency of the medium pressure natural gas burners associated with chemical energy, which is converted into heat, that is, the hotter the gaseous products of combustion, the more efficient the burner. The work analyzes the combustion process and their chemical reactions; combustion products obtained in accordance with various conditions; combustion characteristics and factors that influence; and measurement methods needed to optimize combustion and their equipment. In the metallurgical production of nickel, optimization of the burners is very important due to the high temperature used in the reverberatory furnace. That is, the use of a large excess of air would be unfavorably energetic, and the use of a very low excess would result in small intimate mixtures of air and fuel, followed by the release of unreacted reagents and the formation of toxic gases arising at high temperatures. For this reason, the results of this work are an important source of information. The innovation is to automate the use of non-invasive sensors to control the combustion process. The sensors play a fundamental role in the industry, because the information they provide supports the enhanced use of data in the enterprise, increase productivity, efficiency and safety.