Statistical Data Processing during Wind Generators Operation

Zaliskyi, Maksym; Petrova, Yuliia; Asanov, Marlen; Bekirov, Eskender

doi:10.18178/ijeetc.8.1.33-38

Cited by 48 publications

(11 citation statements)

References 11 publications

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“…Literature [16] Literature [17] Literature [18] Literature [19] Literature [20] Proposed Literature [16] Literature [17] Literature [18] Literature [19] Literature [20] Literature [16] Literature [17] Literature [18] Literature [19] Literature [20] Proposed Literature [16] Literature [17] Literature [18] Literature [19] Literature [20] Proposed e quality of life and study of teaching staff and students are gradually improved, which increases the workload and difficulty of logistic work in universities. e traditional logistics management in universities mostly adopts the Hadoop-based logistics data processing platform.…”

Section: Discussionmentioning

confidence: 99%

“…In the experiment, the optimized algorithm in this paper and five other algorithms in the literature [16][17][18][19][20] were used to conduct task set scheduling on the cloud platform to compare the scheduling performance of the six algorithms. Specific experimental parameters are shown in Table 2, in which the algorithm in the literature [16] adopts parameters in the original text.…”

Section: Experimental Environmentmentioning

confidence: 99%

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Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm

2022

Advances in Multimedia

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The traditional Hadoop-based logistic data processing platform of universities cannot fully collect and control remote data in the process of data processing and has defects such as poor efficiency, high error, and difficult query. In existing designs, devices often need to store complete block data. When retrieving or verifying specific data on the chain, a large number of blocks need to be traversed to find the corresponding data, which reduces the response speed on the user side. In addition, the traditional consensus algorithm is not suitable for resource-constrained terminal devices. Therefore, this paper proposes an intelligent data processing and optimization method of university logistics combined with block chain storage algorithm. For unstructured data on the cloud storage network, the storage information can be obtained through domain analysis, and the storage frequency can be dynamically adjusted based on the estimated data flapping. Moreover, a data storage audit scheme is designed on the cloud storage network to improve data storage efficiency. In order to ensure the integrity verification efficiency of unstructured big data cloud storage, a multilayer block chain network model suitable for university logistics data is designed by introducing block chain technology. The throughput and average occupancy under the same conditions in different environments are evaluated and analyzed, and it is verified that the proposed method is not affected by the initial priority distribution and has good stability and optimization performance. The results show that the proposed method can significantly improve the cloud storage efficiency of unstructured data, can effectively respond to a large number of requests to access data, and has good big data processing capability.

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Section: Discussionmentioning

confidence: 99%

Section: Experimental Environmentmentioning

confidence: 99%

Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm

2022

Advances in Multimedia

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“…This system has been developed since the 1980s. Benefiting from its high specific impulse and flexible working mode, DMPS has been widely applied in space missions [1][2][3][4]. DMPS was first used on the Mars Global Surveyor (MGS) launched by NASA in the 1990s [5].…”

Section: Introductionmentioning

confidence: 99%

A dual-mode hydrogen peroxide and kerosene space propulsion system pressurized by electric pumps

Zhou

et al. 2022

J. Phys.: Conf. Ser.

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In this study, the performance characteristics and previous application cases of the dual-mode propulsion system (DMPS) were discussed. Besides, a dual-mode hydrogen peroxide and kerosene space propulsion system pressurized by electric pumps was proposed via the analysis of the existing problems with respect to the dual-mode propulsion system. In addition, the working process of this system was analyzed by system modeling and simulation. Further, the feasibility of DMPS was verified by exploring the working characteristics of the bipropellant main thruster and the monopropellant attitude control thruster. As per the analysis results, the DMPS proposed in this study contributed to eliminating the problems in the application of the dual-mode propulsion system at the present stage, and it also provided effective guidance in improving the performance of the propulsion system, adjusting the thrust flow rate, and making the propellant non-toxic.

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“…Statistical data processing algorithms [7] can be used to improve the efficiency of aircraft operations given diagnostic variables and reliability parameters as initial data. In general, the trends of these variables and parameters are non-stationary random processes [8]. The trends contain quasi-stationary intervals for the period of normal operation of aircraft components and systems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Maintenance Task Interval of Aircraft Systems

Okoro¹,

Zaliskyi²,

Dmytriiev³

et al. 2022

IJCNIS

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Maintenance accounts for approximately 20% of the operational cost of aircraft; a margin higher than cost associated with fuel, crew, navigation, and landing fees. A significant percentage of maintenance cost is attributed to failures of aircraft components and systems. These failures are random and provide a database which can further be analyzed to aid decision-making for maintenance optimization. In this paper, stochastic mathematical models which can potentially be used to optimize maintenance task intervals of aircraft systems are developed. The initial data for this research are diagnostic variables and reliability parameters which formed the basis for selecting the probability density function for time between failures according to the exponential and Erlang models. Based on the probability density functions, the efficiency of the maintenance processes was calculated using average operational cost per unit time. The results of the analysis were further tested using the Monte Carlo simulation method and the findings are highlighted in this paper. The simulation results compared favorably with analytical results obtained using already existing Monte Carlo techniques to about 82% accuracy. The proposed mathematical optimization models determine the optimal aircraft maintenance task interval which is cost effective while considering safety and reliability requirements; our results can also be applied during the development, design, and operation phases of aircraft systems.

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Statistical Data Processing during Wind Generators Operation

Cited by 48 publications

References 11 publications

Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm

Intelligent Data Processing and Optimization of University Logistics Combined with Block Chain Storage Algorithm

A dual-mode hydrogen peroxide and kerosene space propulsion system pressurized by electric pumps

Optimization of Maintenance Task Interval of Aircraft Systems

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