Development of models and means of the server part of the system for passenger traffic registration of public transport in the "smart" city

Boreiko, Oleh; Teslyuk, Vasyl; Zelinskyy, Andriy; Berezsky, Oleh

doi:10.15587/1729-4061.2017.92831

Cited by 18 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are scientific papers [ 19 , 20 , 21 ] that describe the work of “smart” house systems using machine learning technologies, as well as some practical implementations of this idea among such developments as Nest Thermostat [ 22 ]. This product uses the machine learning algorithms to adapt settings to user preferences and seasons.…”

Section: Related Workmentioning

confidence: 99%

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Teslyuk

Kazarian

Kryvinska

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Teslyuk

Kazarian

Kryvinska

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…A key problem is to guarantee cryptographic security of data transmission in the management of UAV [1,2], intelligent robots [3], microsatellites [4] and various mobile transport systems [5]. Due to the security vulnerabilities of UAVs, and illegal and malicious attacks against UAVs, especially against communication data and UAV control, solutions to prevent such attacks are needed, and one of them is to encrypt UAV's communication data [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Technology for Cryptographic Protection of Data Transmission at UAV

Tsmots¹,

Teslyuk²,

Łukaszewicz³

et al. 2023

Preprint

View full text Add to dashboard Cite

The neural network technology for real-time cryptographic data protection with symmetric keys (masking codes, neural network architecture and weights matrix) for unmanned aerial vehicles (UAV) onboard communication systems has been developed. It provides hardware and software implementation with high technical and operational characteristics. The development of neural network technology for real-time cryptographic data protection was performed using an integrated approach based on theoretical foundations of neural network cryptographic data protection, new algorithms and structures of neural network data encryption and decryption, modern element base with the possibility of programming for the structure, and computer-aided design of hardware and software tools. The development and implementation of the on-board system of neural network cryptographic data protection in real-time are based on the following principles: variable composition of equipment; modularity; conveyorization and spatial parallelism; specialization and adaptation of hardware and software to data encryption and decryption. The tabular-algorithmic method of calculating the scalar product has been improved, it provides fast calculation of the scalar product of input data for both fixed and floating-point by bringing to the largest common order of weights and forming tables of macro-partial products for them. Components of neural network cryptographic data encryption and decryption have been developed on the processor core supplemented by specialized scalar product calculation modules. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries, and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family.

show abstract

“…The existing traditional methods of reliability calculation for devices and simple parallel-sequential systems [15][16][17][18][19][20] cannot satisfy requirements of reliability investigation for complicated and large systems [21], such as tree-like hierarchical ramified systems [22][23][24]. Today, a significant part of technical systems are multilevel, hierarchical, and branched systems, namely: computer networks [22], systems of "smart" city [25][26][27], "smart" house [28,29], smart-grid systems supporting IoT with the use of modern Blockchain technology [30,31], etc. Accordingly, the models developed in this article are intended just for analysis and investigation of complicated hierarchical systems [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Modelling Reliability Characteristics of Technical Equipment of Local Area Computer Networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

Technical systems in the modern global world are rapidly evolving and improving. In most cases, these are large-scale multi-level systems and one of the problems that arises in the design process of such systems is to determine their reliability. Accordingly, in the paper, a mathematical model based on the Weibull distribution has been developed for determining a computer network reliability. In order to simplify calculating the reliability characteristics, the system is considered to be a hierarchical one, ramified to level 2, with bypass through the level. The developed model allows us to define the following parameters: the probability distribution of the count of working output elements, the availability function of the system, the duration of the system’s stay in each of its working states, and the duration of the system’s stay in the prescribed availability condition. The accuracy of the developed model is high. It can be used to determine the reliability parameters of the large, hierarchical, ramified systems. The research results of modelling a local area computer network are presented. In particular, we obtained the following best option for connecting workstations: 4 of them are connected to the main hub, and the rest (16) are connected to the second level hub, with a time to failure of 4818 h.

show abstract

Development of models and means of the server part of the system for passenger traffic registration of public transport in the "smart" city

Cited by 18 publications

References 14 publications

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Neural Network Technology for Cryptographic Protection of Data Transmission at UAV

Modelling Reliability Characteristics of Technical Equipment of Local Area Computer Networks

Contact Info

Product

Resources

About