bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perspective for the use of industrial waste in lubricating compositions to reduce wear in friction pairs
Authors: Yu Konyukhov V , Permyakova D N , Oparina T A 
Published: November 2021

Abstract
The article presents an analysis of the mechanisms of lubricating action used to reduce the wear of rails and wheels. A review of lubricant compositions that increase the service life of the wheel-rail pair showed that graphite is the main filler used to create rail lubricants. Since the production of synthetic graphite requires high energy costs, this material is relatively expensive. Replacing graphite with cheaper analogs will help reduce the cost of operating rails without losing lubricating properties in friction pairs. Today, there are many lubricant compositions. Many of them require experimental research since they are based only on theoretical knowledge in the field of tribology. In addition, the adhesion properties of a wheel to a rail depend not only on the action of the lubricant but also on weather and climatic conditions. When choosing a rational lubricant, it is worth focusing on a set of factors, and not only on the properties of an individual filler. The data obtained in theoretical analysis cannot always be verified experimentally. The reasons may be economic inexpediency, the complexity of experimental research, or lack of time. The analysis carried out in the course of this work showed that the study of the effect of lubricant components could be optimized using mathematical and digital modeling. The application of these methods will help to select a rational area for research, simulate the behavior of lubricants containing production wastes, and make the mathematical forecast of the operation of friction pairs more accurate.
The role of hydrogen in the kinetic mechanism of formation of a carbon fraction of dispersed particles in the combustion of hydrocarbon fuels
Authors: Konoplev V , Fomin V , Melnikov Z , Korzin A , Ramsey Hassan Youssef Abu-Nijim 
Published: November 2021

Abstract
The issues of the scientific search for ways to limit emissions of dispersed particles are of particular importance within the context of socially important problem of environmental protection in the field of heat power during the operation of power plants for various purposes. The bulk of the dispersed particles are particles of the carbon fraction. These particles formed during the combustion of hydrocarbon fuel are among the most harmful substances. The high potential hazard of the particles of the carbon fraction is due to the fact that they contain a number of highly toxic mutagens and carcinogens. Moreover, the ultra-small particle sizes allow them to actively penetrate the human respiratory organs. An alternative version of the method for reducing carbon particles emission based on the use of an additive of a small fraction of free hydrogen to the working fluid of a power plant is considered. A number of scientific provisions on the role of hydrogen as a chemical reagent in the acts of formation and burning of carbon particles are substantiated, the validity of which is confirmed by the results of experimental testing.
Handling improvement of the M1 class vehicles by the way of hydraulic power steering adaptive characteristic optimization
Authors: Potashov I S , Ya Grabarnik S 
Published: November 2021

Abstract
In the paper, a work on handling improvement of the M1 class vehicle by means of optimization of the volume flow-pressure characteristic of the power steering pump with adaptive characteristic is described. A work on the simulation of the liquid flow through the electromagnetic valve of the power steering pump in initial and modified state is described. Results of bench testing of the optimized pump prototypes and subjective and objective road tests of the M1 vehicle with optimized prototype and initial one installed are presented.
Prototype of a remotely controlled model of a tugboat with an azimuth rudder and a hazard identification function
Authors: Stukonog S N 
Published: November 2021

Abstract
The author’s approach, based on the technology of control actions, is proposed for the interface display of the algorithm for the operation of an automatic tugboat with an azimuth rudder and a hazard assessment function. To define the object as presumably dangerous, the article used the formulas of the direct and inverse navigation problems. To compare the coordinates of the vessel and the perceived “hazard”, one first needs to enter data, process the information, and display information that is understandable for the decision-maker. To eliminate the misunderstanding of this situation, the operation of the danger prevention algorithm was demonstrated using the visual basic programming language as an example. When constructing an algorithm for the safe movement of the vessel and the existing danger, each object, even a stand-alone one, has many coordinate points. Knowing the number of points of coordinates of the vessel and “danger”, they were taken as n and m, respectively. The algorithm searches for the optimal point-object. The scheme of interaction of the components of the automatic tug azimuth model (ABA) is proposed. It is based on a Raspberry Pi single-board computer with an Arduino Mega board connected to it. A program has been written to control the ABA model using omnidirectional wheels to simulate maneuvering on water.
Structuring tasks of control over driverless vehicles within intelligent transport systems
Authors: Buznikov S E , Evgrafov V V , Saykin A M 
Published: November 2021

Abstract
The relevance of the research topic is defined by the global level of significance of the problem of creation and safe operation of driverless transport on public roads. The research objective was to develop a mathematical model of the problem that allows forming a scientifically grounded strategy for driverless transport progress. The Zwicky Morphological Box method was used as a research method, which allowed building a structured set of intelligent transport system variants. Variables corresponding to the hard-surfaced road types, the level of informational support in the form of digital road models and the level of control tasks with increasing complexity were used as structural variables. A complex of tasks required to control traffic or driving in closed territories, on highways, suburban motorways passing through human settlements, urban streets, and yards has been defined. The control task complexes of each consecutive level include the task complexes of all the previous levels, and the digital road models of a higher level contain the digital models of all the previous levels. The analysis of the obtained results allowed building a trajectory of progressive development of the driverless vehicle focus area within the field of control task levels, road types, and their digital models.
Design principles for onboard engineering diagnostics systems of unmanned maritime vessels
Authors: Zhukov V , Butsanets A , Sherban S 
Published: November 2021

Abstract
Modern information technology makes it easier to control and adequately assess the performance of marine equipment and, specifically, the elements of ship power plants, since they deliver efficient technical diagnostic services. The paper proposes the structure and principles of constructing a monitoring and diagnostic system to keep track of elements of ship power plants, enabling to solve current problems facing technical diagnostic systems.
Selection of optimal forecasting models in the navigation safety decision support system
Authors: Astrein V V , Kondratyev S I , Boran-Keshishyan A L 
Published: November 2021

Abstract
One of the most important components of the navigation safety decision support system (NS DSS) is the forecasting block. The results of his work have an impact on the type of control actions formed by the system. The effectiveness of this block depends on the forecasting methods used. The accuracy of the result of the forecasting block depends largely on the choice of the forecasting method.To develop reliable forecasts, it is necessary to determine forecasting methods, in relation to the specifics of the functioning of the NS DSS. Several ways of solving the problem of choosing the appropriate forecasting method in relation to the NS DSS are proposed. Two methods are demonstrated: Artificial neural networks (ANN) using precedents and Intelligent process analysis (IPA). Solving the problem of choosing the optimal method will guarantee obtaining a forecast with a certain level of accuracy, which will significantly increase the reliability of the forecast and, as a result, the effectiveness of the NS DSS. Solving the problem of choosing the optimal method will guarantee obtaining a forecast with a certain level of accuracy, which will significantly increase the reliability of the forecast and, as a result, the effectiveness of the NS DSS, which allows us to assert the relevance of this problem and further research.
Multicriteria assessment of optimal forecasting models in decision support systems to ensure the navigation safety
Authors: Astrein VV , Kondratyev SI , Boran-Keshishyan AL 
Published: November 2021

Abstract
When developing the Decision Support System (DSS), the operability of the internal motion and maneuvering control systems of the vessel is characterized by a large number of parameters, which should be monitored in order to achieve the expected results. The task is to develop an appropriate methodology for automatic monitoring of these systems, which, using a minimum set of sensors, makes it possible to predict the state of the vessel and change the dangerous state to the safe one. To implement the method of automatic monitoring, a set of statistical control tools is selected depending on the alleged violations and the level of correlation of parameters. The uncorrelated parameters are monitored by instruments based on the Shewhart map [1], the correlated parameters are monitored on the basis of Hotelling statistics [2]. This approach makes it possible to diagnose the pre-emergency and emergency states of ship control systems in on-line mode. The method used for multi-level integrated monitoring of the technical state of control systems in on-line mode can improve the reliability of of identification of the technical state of vessel subsystems and expandthe scope of application of monitoring and diagnostics tools. The data obtained can become the basis for the development of rational decisions in the DSS at the level of control subsystems for the vessel transfer from the dangerous to safe state.
On the possibility of organizing communication for e-Navigation in the coastal zone using radio-technical posts of the vessel traffic control system
Authors: Buzenkov I I , Tyufanova A A , Khaleeva E P 
Published: November 2021

Abstract
The concept of e-Navigation is to unify activities for the collection, integration, exchange, presentation and analysis of information on vessels and in coastal services through electronic technologies to improve the navigation safety, and the quality and efficiency of the relevant services.The generalized analysis of the obtained data is used to determine the appropriate actions in the prevailing conditions, to improve the process of the vessel’s transition from berth to berth (in ports of departure and destination) and the corresponding services that ensure the safety of navigation, environmental protection, information exchange between intelligent transport systems.The basis for fulfilling the assigned tasks are three basic components of e-Navigation: vessel systems; coastal and satellite systems; communication infrastructure. The purpose of the paper is the possibility of applying broadband radio communications for unmanned navigation in the coastal zone. The research object is the analysis of broadband communication systems; the subject is the assumed range of the WiMAX radio communication zone. EMC Planner 1.1_170328 is used to simulate the anticipated WiMAX radio zone.
A new method for creating Mivar knowledge bases in tabular-matrix form for ground intelligent vehicle control systems
Authors: Chuvikov D A , Aladin D V , Adamova L E , Varlamov O O , Osipov V G 
Published: November 2021

Abstract
This research presents a methodology for creating mivar knowledge bases in tabular-matrix form for ground intelligent vehicle control systems. At its core, this methodology is kind of instruction for analysts facing the task of formalizing knowledge in a given subject area. The result of this formalization is a “knowledge map” created according to a special proposed template. In the future, this template allows forming a knowledge base for a given subject area in the formalism of bipartite oriented mivar networks. As an example, the subject area of ground-based intelligent vehicle control systems is used as a template. The proposed methodology of knowledge formalization makes it possible to simplify the process of creating models in Wi!Mi “Razumator-Consultant” 2.1 and also to level the probability of logical collisions when designing a knowledge model in the formalism of bipartite oriented mivar networks.
Methods of formalized quality assessment of intelligent transport systems
Authors: Goloskokov K , Korotkov V , Gaskarov V , Knysh T 
Published: November 2021

Abstract
The purpose of the paper is to identify the main problems of creating software systems with a given level of reliability for intelligent transport systems. Considering the importance of this approach and the gained experience, the paper discusses design solutions to ensure software reliability in the development process. The paper is based on domestic and foreign experience of software design for intelligent information systems, which include transport systems. The issues of achieving a given level of software reliability during the control process are considered taking into account the continuation of the development process. It also reflects efforts to model and evaluate the reliability of software systems by considering the most common types of software reliability assessment models during development, as well as to predict the reliability during maintenance. The emphasis is upon detecting and correcting software errors.
Analytical models of decision-making theory in digital transport logistics
Authors: Terentyev A , Karelina M , Karelina E 
Published: November 2021

Abstract
The active introduction of information and communication (digital) technologies into the modern reality of transport systems operation requires the development of modeling methods for the creation of software that allows to do the following: determine the formal efficiency of decisions made in digital transport systems (DTS); process large volumes of DTS data; perform analysis of DTS functioning environment using artificial intelligence algorithms, analytical analogues of neural networks, etc. A digital transport system, being a complex system, is determined by a large set of formalized indicators (database) and requires finding effective solutions for a sufficiently large number of criteria or signs of effectiveness. Obtaining reliable solutions in multi-criteria information situations causes difficulties, which are objective. In most cases modern mathematical models artificially reduce multi-criteria information situations to single-criteria categories. This approach, based on the use of integral criteria, has a fundamental disadvantage - the use of integral criteria to obtain estimates of the efficiency of actions or processes in complex systems is characterized by a high level of subjectivism. In this case, the obtained solution may be acceptable, but not an objective result. Therefore, it is necessary to develop mathematical models for solving multi-criteria problems applicable to the solution of problems in complex transport systems, allowing one to operate in the environment of large databases for operational reconfiguration of a management system in conditions of uncertainty and/or possible counteraction of the external environment. The article presents the results of development of mathematical methods of modeling, which allow us to construct algorithms for solving optimization tasks, formulated as multi-criteria models and in the presence of a high degree of uncertainty in the interaction of the system with the environment.
Analysis of results of investigation tests of the developed information and measuring active safety system of the vehicle
Authors: Buznikov S E , Saykin A M , Elkin D S , Strukov V O , Loginov I D , Oskin A V 
Published: November 2021

Abstract
The research objective is the analysis of results of investigation tests of the information and measuring active safety system developed by FSUE “NAMI”. The practice of operation and performed studies show that modern foreign advanced driver assistance systems do not provide appropriate functioning under severe road and climate conditions of the Russian Federation creating unjustified risks and precluding from preventing a significant number of road accidents. This statement is applicable to driving on slippery surfaces, ice and snow, to the driving mode in traffic jams, in the absence of recognizable road marking, blinding of video cameras, insufficient visibility range and road lighting, etc. In order to ensure competitive ability and commercial attractiveness of the developed domestic information and measuring active safety system, within the scope of this work additional studies were performed regarding improvement of driver status monitoring functions with an expanded vector of the state of the controls and speed range, tire pressure monitoring with an expanded speed range and start-stop driving mode, dangerous obstacle approach warning and detection of deviation from the center of the lane with unrecognizable marking. In this paper, the results of investigation tests of improved functions are considered.
Precise Position Intelligent Matching System of Online Recruitment Platform Based on Data Mining Technology
Authors: Gao Zhen 
Published: November 2021

Abstract
With the rapid development of Internet technology and computer technology, network applications have been developed more and more, and have penetrated into all walks of life in society. The emergence of the networking of the talent market has made the scale of online recruitment increase, and the amount of data on the Internet has become larger and larger, and online recruitment has become the main channel for corporate recruitment. Therefore, how to use the massive online recruitment data to quickly and accurately find the corresponding information and explore the hidden knowledge mode is a very valuable research topic. Data mining (DM) is a technology for data analysis for large amounts of data. It can discover hidden, hidden, and potentially useful knowledge hidden in the data from the vague, noisy, and random mass data, and build relevant Model, realize prediction, etc. The characteristics of data mining technology (DMT) are very suitable for the analysis of online recruitment information, research on large amounts of information, and find out the knowledge in it for decision support. This article aims to study the accurate job matching system of the online recruitment platform based on DMT. Based on the analysis of the advantages of online recruitment, related DMT and the design principles of the online recruitment platform system, the data collected by Weka DM tools are analyzed. Analyzing and getting useful job positions is just to provide job seekers and corporate-related recruiters with useful job information. The experimental results show that the online recruitment platform system can complete the collection of online recruitment position information, and can realize the DM function, which has good practical application value.
Research on Image Recognition System Based on Computer Deep Learning Neural Network
Authors: Du Shuqiang 
Published: November 2021

Abstract
The selection and extraction of image recognition by artificial means needs more complicated work, which is not conducive to the recognition and extraction of important features. Deep learning and neural network represent the iterative expansion of computer intelligent tech, and bring significant results to image recognition. Based on this, this paper first gives the concept and model of neural network, then studies the utilization of deep learning neural network in image recognition, and finally analyses the picture recognition system on account of in-depth learning neural network.
Customer Service System Design Based on Big Data Machine Learning
Authors: Kong Yuqiang , He Yaoping 
Published: November 2021

Abstract
In recent years, with the rapid development of big data, traditional offline transactions have been moved to online in large numbers driven by the Internet. The virtual nature of online transactions has caused it to have problems such as difficulty in guaranteeing product quality and difficulty in user consultation. In addition, consumers are paying more and more attention to the quality of services, and the participation of customer service in the process of online transactions is very important. However, the current e-commerce market in our country is large and the number of online shopping users is extremely large. Customer service personnel are facing great work pressure. In addition, customer service has the characteristics of difficulty in recruiting, high labor costs, and high turnover rate. Such a dilemma is not conducive to our country. The sound development of e-commerce needs to be solved urgently. In order to solve these problems, it is a good method to apply related technologies to realize the automatic response of customer service. The purpose of this article is to design and research a customer service system based on big data machine learning. This article first through the understanding of the basic concepts of big data, and then extend the core technology of big data. Combining with the design ideas and concepts of contemporary customer service systems in our country, we will discuss the design and research of customer service systems based on big data machine learning. Research shows that traditional customer service in the era of big data can no longer meet people’s growing needs, and customer service systems based on big data machine learning are more efficient and convenient.