Improving Defect Localization by Classifying the Affected Asset Using Machine Learning

Lecture Notes in Business Information Processing - Software Quality: The Complexity and Challenges of Software Engineering and Software Quality in the Cloud ◽

10.1007/978-3-030-05767-1_8 ◽

2018 ◽

pp. 106-122

Author(s):

Sam Halali ◽

Miroslaw Staron ◽

Miroslaw Ochodek ◽

Wilhelm Meding

Keyword(s):

Machine Learning ◽

Defect Localization

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Automatic defect localization and characterization through machine learning based inversion for guided wave imaging in SHM

10.1063/1.5099771 ◽

2019 ◽

Cited By ~ 2

Author(s):

Roberto Miorelli ◽

Andrii Kulakovskyi ◽

Olivier Mesnil ◽

Oscar d’Almeida

Keyword(s):

Machine Learning ◽

Guided Wave ◽

Defect Localization ◽

Wave Imaging

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Integration of Building Information Modeling and machine learning for railway defect localization

IEEE Access ◽

10.1109/access.2021.3135451 ◽

2021 ◽

pp. 1-1

Author(s):

Jessada Sresakoolchai ◽

Sakdirat Kaewunruen

Keyword(s):

Machine Learning ◽

Building Information Modeling ◽

Information Modeling ◽

Defect Localization ◽

Building Information

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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