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Stochastic and Dynamic Network Analytics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic and Dynamic Network Analytics./
作者:	Meyers, Adam.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	132 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
Contained By:	Dissertations Abstracts International83-10B.
標題:	Physiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29050182
ISBN:	9798209935605

Stochastic and Dynamic Network Analytics.
Meyers, Adam.

Stochastic and Dynamic Network Analytics. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 132 p.

Source: Dissertations Abstracts International, Volume: 83-10, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2021.

This item must not be sold to any third party vendors.

Networked systems are prevalent in both natural and engineered settings and their study and engineering have benefited many fields, including biology, healthcare, social systems, energy, manufacturing, technology, and more. Advanced sensing has begun to permeate networked systems, giving rise to torrents of data in real time. However, networked systems and their sensing data are becoming increasingly complex and thus present many challenges. Real world networks are increasing in scale and are typically dynamic in nature, while sensing data may have high dimensionality, stream rapidly, and maintain complex patterns. Methodologies that can model such networked systems and harness their big data will improve network performance and confer benefits to a variety of fields.The objective of this dissertation is to develop novel engineering aspects of complex networked systems and to realize their potential for smart capabilities. This research focuses on three key areas: resilience engineering of stochastic and dynamic networks, nonlinear analysis of multidimensional physiological signals in smart networked systems, and monitoring and control of dynamic image profiles in additive manufacturing. The key research accomplishments in this dissertation are as follows:• A fault-tolerance model is developed for stochastic, dynamic networks. Reliability engineering is merged with percolation theory to model stochastically-varying networks, compute quantitative reliability metrics, and permit asymptotic analysis of network behavior. • A methodology is developed for nonlinear analysis of multidimensional physiological signals. It enables a peer-to-peer network that visualizes and quantifies the distance between patients in terms of their cardiac electrical behavior.• A methodology is developed for anomaly detection in high-dimensional image streams. It is based on a novel multiplex network framework that integrates multiple image features and leverages the wavelet transform for efficient dimensionality reduction and detection of anomalies at multiple scales.

ISBN: 9798209935605Subjects--Topical Terms:

518431
Physiology.

Stochastic and Dynamic Network Analytics.
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