A semi-Markov model for an unreliable single-line loss queueing system with different restoration types

Peschansky, A. I.; Kovalenko, A. G.

doi:10.1134/s0005117916120080

Cited by 3 publications

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“…For example, in [9], a strategy to carry out preventive maintenance depending on the service time of a request was studied. In [10][11][12][13], a series of queueing system models with maintenance were constructed as follows: in [10], a model taking into account the server's operating time between failures and delayed maintenance; in [11], a model taking into account the system maintenance in case of hidden server failures; in [12], a model taking into account maintenance depending on the total operating time to failure; in [13], a model taking into account different types of server restoration. In this paper, another maintenance strategy of an unreliable restorable single-server queueing system with losses is explored: the preventive maintenance of the server begins as soon as the accumulated operating time (the service time of all requests after completion of restoration works) exceeds an upper permissible threshold.…”

Section: Introductionmentioning

confidence: 99%

Stationary Characteristics of an Unreliable Single-Server Queueing System with Losses and Preventive Maintenance

Peschansky

2020

Autom Remote Control

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This paper considers an unreliable restorable single-server queueing system with losses in which server failures may occur during operation. The random variables describing the system have general distributions. For increasing the efficiency of this system, it is proposed to carry out preventive maintenance of the server as soon as the accumulated operating time exceeds an upper permissible threshold. A semi-Markov model of the system's evolution over time is constructed. The explicit-form expressions for the final probabilities and mean sojourn times of the system in different physical states are derived using the stationary distribution of the embedded Markov chain. The frequency of preventive maintenance of the server is optimized via maximizing the average specific profit and minimizing the average specific costs of the system.

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Section: Introductionmentioning

confidence: 99%

Stationary Characteristics of an Unreliable Single-Server Queueing System with Losses and Preventive Maintenance

Peschansky

2020

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“…Autonomous driving has been implemented as part of the advanced driver assistance system (ADAS) to improve road safety as found in Tesla autopilot driving mode [3]. However, one of the well known issues associated with connected and autonomous vehicles is the security concerns related to vulnerability of getting wirelessly hacked, which would allow hackers to take full control of the vehicle [4,5]. Automatic driver identification can be utilized to detect whether an autonomous vehicle has been hacked.…”

Section: Introductionmentioning

confidence: 99%

Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

Tanprasert

Saiprasert

Thajchayapong

2017

Journal of Advanced Transportation

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This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks. The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers) and geolocation signals from GPS sensors. First anomaly detection is performed to assess if the current driver is whom he/she claims to be. If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions. Driver identification is performed on each of the sevensecond-long driving behavior signals and geolocation signals in a streaming manner. It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds. The proposed algorithm also outperforms the previously proposed driver identification algorithms. Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.

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Stationary Characteristics of an Unreliable Multi-Server Queueing System with Losses and Time Redundancy

Peschansky

2019

Autom Remote Control

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A semi-Markov model for an unreliable single-line loss queueing system with different restoration types

Cited by 3 publications

References 3 publications

Stationary Characteristics of an Unreliable Single-Server Queueing System with Losses and Preventive Maintenance

Stationary Characteristics of an Unreliable Single-Server Queueing System with Losses and Preventive Maintenance

Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

Stationary Characteristics of an Unreliable Multi-Server Queueing System with Losses and Time Redundancy

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