Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations

Hadi Salehi; Subir Biswas; Rigoberto Burgueño

doi:10.1016/j.engappai.2019.08.004

Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations

Hadi Salehi
, Subir Biswas
, Rigoberto Burgueño

Michigan State University

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	136-153
Number of pages	18
Journal	Engineering Applications of Artificial Intelligence
Volume	86
DOIs	https://doi.org/10.1016/j.engappai.2019.08.004
State	Published - Nov 2019
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

ASJC Scopus Subject Areas

Control and Systems Engineering
Electrical and Electronic Engineering
Artificial Intelligence

Keywords

Energy harvesting
Incomplete signals
Low-rank matrix completion
Machine learning
Pattern recognition
Plate-like structures
Self-powered sensors
Structural health monitoring

Access to Document

10.1016/j.engappai.2019.08.004

Cite this

Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations. / Salehi, Hadi; Biswas, Subir; Burgueño, Rigoberto.
In: Engineering Applications of Artificial Intelligence, Vol. 86, 11.2019, p. 136-153.

Research output: Contribution to journal › Article › peer-review

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title = "Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations",

keywords = "Energy harvesting, Incomplete signals, Low-rank matrix completion, Machine learning, Pattern recognition, Plate-like structures, Self-powered sensors, Structural health monitoring",

author = "Hadi Salehi and Subir Biswas and Rigoberto Burgue{\~n}o",

note = "Publisher Copyright: {\textcopyright} 2019 Elsevier Ltd",

year = "2019",

month = nov,

doi = "10.1016/j.engappai.2019.08.004",

language = "English",

volume = "86",

pages = "136--153",

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AU - Salehi, Hadi

AU - Biswas, Subir

AU - Burgueño, Rigoberto

PY - 2019/11

Y1 - 2019/11

KW - Energy harvesting

KW - Incomplete signals

KW - Low-rank matrix completion

KW - Machine learning

KW - Pattern recognition

KW - Plate-like structures

KW - Self-powered sensors

KW - Structural health monitoring

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UR - https://www.scopus.com/pages/publications/85071862199#tab=citedBy

U2 - 10.1016/j.engappai.2019.08.004

DO - 10.1016/j.engappai.2019.08.004

M3 - Article

SN - 0952-1976

VL - 86

SP - 136

EP - 153

JO - Engineering Applications of Artificial Intelligence

JF - Engineering Applications of Artificial Intelligence

ER -

Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations

UN SDGs

ASJC Scopus Subject Areas

Keywords

Access to Document

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