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Michael B. Kane

Researcher at Northeastern University

Publications -  24
Citations -  370

Michael B. Kane is an academic researcher from Northeastern University. The author has contributed to research in topics: Wireless sensor network & Model predictive control. The author has an hindex of 7, co-authored 24 publications receiving 206 citations. Previous affiliations of Michael B. Kane include University of Michigan.

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Proceedings ArticleDOI

Development of an extensible dual-core wireless sensing node for cyber-physical systems

TL;DR: A new wireless platform named Martlet is introduced with a dual-core microcontroller adopted in its design, which allows Martlet to dedicate one core to standard wireless sensor operations while the other core is reserved for embedded data processing and real-time feedback control law execution.
Journal ArticleDOI

Infrastructure resilience curves: Performance measures and summary metrics

TL;DR: In this paper, the authors define a common vocabulary for practitioners and researchers that will improve the use of resilience curves as a tool for assessing and designing resilient infrastructure This vocabulary includes a taxonomy of resilience curve performance measures as well as a taxonomic of summary metrics, and a framework for examining assumptions of resilience analysis that are often implicit or unexamined in the practice and literature.
Journal ArticleDOI

Three-Tier Modular Structural Health Monitoring Framework Using Environmental and Operational Condition Clustering for Data Normalization: Validation on an Operational Wind Turbine System

TL;DR: An intrinsic goal of the study is to explore the modularity of the three tier framework as a means of offering SHM system designers opportunity to explore and test different computational block sets at each layer to maximize the detection capability of theSHM system.
Proceedings ArticleDOI

Machine Learning Control for Floating Offshore Wind Turbine Individual Blade Pitch Control

TL;DR: The development, design, and simulation of machine learning control (MLC) for IPC of FOWTs are presented and a massively parallel genetic program is developed using MATLAB Simulink and OpenFAST that efficiently evaluates new individuals and selectively tests fitness of each generation in the most challenging design load case.