Advanced software-technological approaches for mobile apps development

Luntovskyy, Andriy

doi:10.1109/tcset.2018.8336168

Cited by 10 publications

(8 citation statements)

References 3 publications

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“…6. Приклад програмування у платформі RoboPlus для Robotis BIOLOID [6][7][8]10] Роботи BIOLOID [6,10] належать корейській фірмі Robotis (1999). Мотивацією розробників є «прекрасне майбутнє як щасливий світ злиття роботів та людей», як сказав генеральний директор B.Soo Kim.…”

Section: зграйні роботи і біолоїдиunclassified

“…Naval Academy, Лейпцігська HTWK, Токійський університет, Індійський технологічний інститут Канпур, з метою вивчення університетських курсів з машинобудування, мехатроніки та роботехніки. Також наявні вони й у BA Dresden [6][7][8]. Фірма постачає «істоти» різного рівня складності, приклад програмування яких у платформі RoboPlus поданий на рис.…”

Section: зграйні роботи і біолоїдиunclassified

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Modern Apps and Platforms for the Robots

Luntovskyy¹

2019

Visnyk Universytetu “Ukraina”

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Nowadays, the number of robots that co-operate with the humans, machines and with each other in various fields of manufacturing and economics, everyday life, entertainment industry, social pedagogy is growing rapidly.Server-free mobile additions are widely used in robotics: applications that are built directly into the actuators and mechanisms; Cloud-oriented applications and others.As a result of the shifted emphasis on performance, energy and algorithms efficiency of addition and platforms for robotics in the future will develop with optimization of the following algorithms: unlimited movements of the robot and its orientation in 3D space; Detection of collisions of robot body (autonomous relative location) with prevention of dead end situation; Routing of autonomous ride and flight for unmanned devices, auto and drone in.ROS (Robot Operating System) for personal robots - known robotics platform today popular in industry and academic institutions. ROS includes the following architectural components: Package Management components; Abstract layer for hardware and hardware (Hardware Abstraction Layer) and device drivers; components with communication and messaging functions between Middleware programs or parts and Websites.Swarm works can be programmed to simulate 2d-forms, which are inherent in flocks of insects, fish, and birds. Researchers have managed to provide Swarm with their own coordination, which forms a significant step in the development of collective artificial intelligence Hundreds of robots cooperate in the field of cleaning the environment from the consequences of natural disaster, others model social processes.The paper is dedicated to the important scientific-practical problem of the use of modern mobile serverless apps, which are capable to autonomous operation, and robotics supporting platforms, in particular, to their classification and standardization. The scenarios for the use of robots that focus not only on manufacturing and modeling processes in the industry but also for behavioral modeling, amomg them on the use of robots in pedagogy and sociology, are considered.

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Section: зграйні роботи і біолоїдиunclassified

Modern Apps and Platforms for the Robots

Luntovskyy¹

2019

Visnyk Universytetu “Ukraina”

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“…The mobile networks and aps for the 5G will definitely take an active part in the process of receiving and processing of large data amounts [2,[5][6][7] too. By the year 2020, the new 5G networks will use more than 50-100 billion sensors to download comprehensive information about how we interact with things that surround us or even are inside of us?!…”

Section: Motivationmentioning

confidence: 99%

“…are used. These techniques can correspondingly be based on cloud analytics and disassemble the complexity and heterogeneity [1][2][3][4][5][6][7][8][9][10][11][12][13] Otherwise, such a large amount of data remains unstructuredand will be in future characterized by the overproportional management complexity. Additionally, significant increase of network traffic can be forecasted during the data processing under the use of typical modern solutions like Cloud Computing with heterogeneity of networks, replication across multiple geographically-distributed computing nodes [2,6,7,10].…”

Section: State-of-the-artmentioning

confidence: 99%

“…Unfortunately the depicted V-factors for Big Data are growing faster than the performance of their analysis (in Mbyte/s or in GFLOPS) via classical computational techniques. Therefore, for Industry 4.0, IoT systems and M2M not only improvements in networking but also in analytic blocks became very significant [1][2][3][4][5][6][7]27,29], which aimed at decreasing of data streams and their algorithmic complexity. The "heavy-weighting approaches" involving precise analytical techniques and expensive specialized software are used for this aim.…”

Section: The 6v Prevention Factorsmentioning

confidence: 99%

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Industry 4.0 and Modern Approaches for Big Data Analytics

Luntovskyy

Globa

2019

Information and Telecommunication Sciences

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Background. With the acceleration of industrial development, i.e. with the new "Industry 4.0", structuring and processing of acquired voluminous and heterogeneous data become considerably more complicated and represents an important scientificpractical problem. Cyber-PHY, IoT, sensor networks, robotics, multiple real-time applications can generate large arrays of unmanaged, weakly structured and non-configured data of various types, known as "Big Data". However, the problem of "Big Data" is very hard to solve nowadays. Objective. The purpose of the presented in this paper research is to analyze the sources of Big data, determine their main characteristics and suggest the ways for overcoming the growing dimension of Big data. Methods. In contradistinction to traditional ways of the Big data problem solving, when only dealing with certain empirical approaches and models, the paper proposes to introduce ontologies for describing data groups, use compression of data volumes into knowledge that significantly reduces their volumes and improves understanding of their sense. Results. The effectiveness of given solutions is confirmed by the best known practices and our own case studies aimed at overcoming this well-known complex problem. Conclusions. To overcome the problem of "Big Data" there is no single universal solution. The analysis shows that the solution can be found by introducing ontologies, determining the mutual influences and correlations between the data, thus gaining knowledge based on a huge amount of data.

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