Intelligent management systems in operations: a review

1998 ◽  
Vol 49 (7) ◽  
pp. 682-699 ◽  
Author(s):  
N C Proudlove ◽  
S Vaderá ◽  
K A H Kobbacy
1998 ◽  
Vol 49 (7) ◽  
pp. 682 ◽  
Author(s):  
N. C. Proudlove ◽  
S. Vadera ◽  
K. A. H. Kobbacy

2017 ◽  
Vol 106 ◽  
pp. 08051 ◽  
Author(s):  
Leonid Zelentsov ◽  
Liya Mailyan

Author(s):  
P Knight ◽  
J Cook ◽  
H Azzam

Helicopter health and usage management systems (HUMS) generate large amounts of data, which are downloaded to ground-based systems. The data are automatically examined on download for damage indications, which provide the immediate go/no-go response required by the aircraft operations management. This level of reactive fault detection and diagnosis is reasonably well understood and has been demonstrated to improve aircraft availability and airworthiness. To achieve further benefit and maintenance cost savings from HUMS, another level of analysis is required, leading to prognostics and predictive maintenance through intelligent management (IM) of the accumulated HUMS records. In collaboration with the Civil Aviation Authority (CAA), Smiths has developed a suite of IM methods and has successfully applied them to gearbox seeded fault data. Working closely with the UK Ministry of Defence (UK MOD), Smiths has tested these methods on Chinook HUMS data, including an in-flight transmission bearing failure incident described in this article. The result is a high degree of early anomaly detection and a clear view of the deterioration to failure. The objective of the MOD programme has been to apply IM tools to the enormous quantity of HUMS data being gathered, thereby enabling improved analysis capability, increased levels of automation, and more intelligent use of resources. The article presents the results of the work carried out under both the CAA and the MOD programmes.


Author(s):  
Gabriela Moise ◽  
Otilia Cangea

With the continuous development of the computers networks, new problems have been posed in the process of keys management in the cryptographic systems. The main element in the cryptographic technologies is the keys management, as the cryptographic algorithms are known, while the keys have to be either secret (for unauthorized users that do not need them), or public (for users that need them). With an efficient cryptographic keys management system and the existing encryption techniques, there may be implemented a proper security system in the informational systems of the organizations. The process of cryptographic keys management consists in the following operations: keys generation, distribution, update, revocation, storage, backup/ recovery, import and export, usage control, expiration, and destruction. The cryptographic keys management techniques depend on the type of the keys, i.e. symmetric or public. Nowadays, the efforts of the researches in the cryptographic keys management are focused on the standardization and interoperability of the keys management. In this paper, the authors analyze the existing keys management systems and standards available for the keys management techniques, emphasizing the advantages and disadvantages of different systems. They also propose a cryptographic keys management model based on the ideas and principles of the INTERRAP architecture (a conceptual model developed by Jőrg Műller for intelligent agents). Also, there are incorporated some intelligent techniques to manage emergency situations, such as keys losing or their improper usage.


2019 ◽  
Vol 4 (4) ◽  
pp. 148-154
Author(s):  
József Menyhárt

There have been several attempts during the last decades to extend the ranges of application of artificial intelligence. The aim of the development for AI is to replace human intelligence and experience. The ultimate aim for machines and vehicles is to run much more efficiently and with higher reliability than ever before. The Artificial Techniques (AI) used a wide range of expert systems to optimize problems. Hybrid intelligent management systems have become increasingly influential in artificial intelligence during the last decades. As a result, maintenance and fleet management systems have undergone significant development. By choosing adequate maintenance or operating strategy and taking user behaviour into consideration, these systems can not only increase the reliability and efficiency of vehicles but can also result in financial savings. The paper tries to discusses the applications of AI techniques in predictive maintenance and vehicle industry.


2015 ◽  
Vol 6 (9(78)) ◽  
pp. 17
Author(s):  
Денис Юрійович Зубенко ◽  
Андрій Віталійович Коваленко ◽  
Олександр Миколайович Кузнєцов

2014 ◽  
Vol 47 (1) ◽  
pp. 1-38 ◽  
Author(s):  
Alessandra De Paola ◽  
Marco Ortolani ◽  
Giuseppe Lo Re ◽  
Giuseppe Anastasi ◽  
Sajal K. Das

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