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Titlebook: System Signatures and their Applications in Engineering Reliability; Francisco J. Samaniego Book 2007 Springer-Verlag US 2007 calculus.clo

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书目名称System Signatures and their Applications in Engineering Reliability
编辑Francisco J. Samaniego
视频video
概述Based on both practical and research success in building reliability into systems with system signatures.Provides guidance on how reliability problems can be structured, modeled and solved.Compares ac
丛书名称International Series in Operations Research & Management Science
图书封面Titlebook: System Signatures and their Applications in Engineering Reliability;  Francisco J. Samaniego Book 2007 Springer-Verlag US 2007 calculus.clo
描述.Since the introduction of system signatures in Francisco Samaniego’s 1985 paper, the properties of this technical concept have been examined, tested and proven in a wide variety of systems applications. Based on the practical and research success in building reliability into systems with system signatures, this is the first book treatment of the approach. It is, therefore, the purpose of this book to provide guidance on how reliability problems might be structured, modeled and solved. Over the past ten years the broad applicability of system signatures has become apparent and the tool’s utility in coherent systems and communications networks firmly established. The book compared actual system lifetimes where the tool has been and has not been used. These comparisons - which have been done over the years - demonstrate the practical, feasible and fruitful use of the tool in building reliable systems. Finally, new results and future directions for system signatures are also explored..
出版日期Book 2007
关键词calculus; closure; coherent system; communication; failure rate; network; optimality; preservation; reliabil
版次1
doihttps://doi.org/10.1007/978-0-387-71797-5
isbn_softcover978-1-4419-4414-6
isbn_ebook978-0-387-71797-5Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightSpringer-Verlag US 2007
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ng customer feedback loops, minimizing the time between customer proof points and learn from customer usage data will be able to accelerate innovation and improve the accuracy of their development investments. While contemporary research reports on a number of well-established techniques for activel
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ng on the machine learning and data mining techniques. However, the performance of these techniques is degrading due to this twofold growth that further adds to the complexity of the data. The need of the hour is to leave up with the complexity of such a datasets and to focus on improving the accura
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