Sustaining implicit learning in locomotive operation
Abstracts
Document identifier: oai:DiVA.org:ltu-76529
Keyword: Engineering and Technology,
Civil Engineering,
Other Civil Engineering,
Teknik och teknologier,
Samhällsbyggnadsteknik,
Annan samhällsbyggnadsteknik,
Implicit learning,
Locomotive operation,
Intuition,
Drift och underhållsteknik,
Operation and MaintenancePublication year: 2018Relevant Sustainable Development Goals (SDGs):
The SDG label(s) above have been assigned by OSDG.aiAbstract: Modern trains are capable of monitoring health status in real time and infer behaviour of various systems. This trend will grow with advancements of machine learning those will produce feedback for continuously improving the prediction models. Despite reduced physical connectivity of human with locomotive systems, human interference will be required for critical decision-making. Human implicit learning involves the largely unconscious learning of dynamic statistical patterns and features, which leads to the development of tacit knowledge1. Pirsig2 argued that “each machine has its own, unique personality which probably could be defined as the intuitive sum total of everything you know and feel about it”. Theses suggest that humans employ an intuitive cognition ability that leads to developing implicit knowledge and interactions with machines. In this study, we focus on signifying the implicit knowledge in locomotive operation context and seek ways to facilitate effective decision-making
Authors
Prasanna Illankoon
Luleå tekniska universitet; Drift, underhåll och akustik
Other publications
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Phillip Tretten
Luleå tekniska universitet; Drift, underhåll och akustik
Other publications
>>
Uday Kumar
Luleå tekniska universitet; Drift, underhåll och akustik
Other publications
>>
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identifier: oai:DiVA.org:ltu-76529
datestamp: 2021-04-19T12:54:39Z
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recordCreationDate: 2019-10-28
identifier: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76529
titleInfo:
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lang: eng
title: Sustaining implicit learning in locomotive operation
abstract: Modern trains are capable of monitoring health status in real time and infer behaviour of various systems. This trend will grow with advancements of machine learning those will produce feedback for continuously improving the prediction models. Despite reduced physical connectivity of human with locomotive systems human interference will be required for critical decision-making. Human implicit learning involves the largely unconscious learning of dynamic statistical patterns and features which leads to the development of tacit knowledge1. Pirsig2 argued that “each machine has its own unique personality which probably could be defined as the intuitive sum total of everything you know and feel about it”. Theses suggest that humans employ an intuitive cognition ability that leads to developing implicit knowledge and interactions with machines. In this study we focus on signifying the implicit knowledge in locomotive operation context and seek ways to facilitate effective decision-making
subject:
@attributes:
lang: eng
authority: uka.se
topic:
Engineering and Technology
Civil Engineering
Other Civil Engineering
@attributes:
lang: swe
authority: uka.se
topic:
Teknik och teknologier
Samhällsbyggnadsteknik
Annan samhällsbyggnadsteknik
@attributes:
lang: eng
topic: implicit learning
@attributes:
lang: eng
topic: locomotive operation
@attributes:
lang: eng
topic: intuition
@attributes:
lang: swe
authority: ltu
topic: Drift och underhållsteknik
genre: Research subject
@attributes:
lang: eng
authority: ltu
topic: Operation and Maintenance
genre: Research subject
language:
languageTerm: eng
genre:
conference/other
ref
note:
Published
3
name:
@attributes:
type: personal
authority: ltu
namePart:
Illankoon
Prasanna
1977-
role:
roleTerm: aut
affiliation:
Luleå tekniska universitet
Drift underhåll och akustik
nameIdentifier:
praill
0000-0001-8693-3431
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type: personal
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Tretten
Phillip
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roleTerm: aut
affiliation:
Luleå tekniska universitet
Drift underhåll och akustik
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phitre
0000-0003-3827-0295
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type: personal
authority: ltu
namePart:
Kumar
Uday
role:
roleTerm: aut
affiliation:
Luleå tekniska universitet
Drift underhåll och akustik
nameIdentifier:
uday
0000-0001-8111-6918
originInfo:
dateIssued: 2018
publisher: Chalmers University of Technology
place:
placeTerm: Gothenburg Sweden
relatedItem:
@attributes:
type: host
titleInfo:
title: 20th Nordic Seminar on Railway Technology
subTitle: Abstracts
part:
extent:
start: 59(65)
end: 59(65)
location:
url: http://www.charmec.chalmers.se/NJS18/NJS18_Abstracts_180607.pdf
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typeOfResource: text