Automatic imagery data analysis for diagnosing human factors in the outage of a nuclear plant

Pingbo Tang; Cheng Zhang; Alper Yilmaz; Nancy Cooke; Ronald Laurids Boring; Allan Chasey; Timothy Vaughn; Samuel Jones; Ashish Gupta; Verica Buchanan

doi:10.1007/978-3-319-40247-5_61

Automatic imagery data analysis for diagnosing human factors in the outage of a nuclear plant

Pingbo Tang, Cheng Zhang, Alper Yilmaz, Nancy Cooke, Ronald Laurids Boring, Allan Chasey, Timothy Vaughn, Samuel Jones, Ashish Gupta, Verica Buchanan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Nuclear power plant (NPP) outages involve maintenance and repair activities of a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working around the NPP. Extremely high outage costs and expensive delays in maintenance projects (around $1.5 million per day) require tight outage schedules (typically 20 days). In such packed workspaces, real-time human behavior monitoring is critical for ensuring safe collaboration among workers, minimal wastes of time and resources due to the lack of situational awareness, and timely project control. Current methods for detailed human behavior monitoring on construction sites rely on manual imagery data collection and analysis, which is tedious and error-prone. This paper presents a framework of automatic imagery data analysis that enables real-time detection and diagnosis of anomalous human behaviors during outages, through the integration of 4D construction simulation and object tracking algorithms.

Original language	English (US)
Title of host publication	Digital Human Modeling
Subtitle of host publication	Applications in Health, Safety, Ergonomics and Risk Management - 7th International Conference, DHM 2016 and Held as Part of HCI International 2016, Proceedings
Editors	Vincent G. Duffy
Publisher	Springer Verlag
Pages	604-615
Number of pages	12
ISBN (Print)	9783319402468
DOIs	https://doi.org/10.1007/978-3-319-40247-5_61
State	Published - 2016
Event	7th International Conference on Digital Human Modeling, DHM 2016 and Held as Part of 18th International Conference on Human-Computer Interaction, HCI International 2016 - Toronto, Canada Duration: Jul 17 2016 → Jul 22 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9745
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	7th International Conference on Digital Human Modeling, DHM 2016 and Held as Part of 18th International Conference on Human-Computer Interaction, HCI International 2016
Country	Canada
City	Toronto
Period	7/17/16 → 7/22/16

Keywords

Computer vision
Construction automation
Human factors
Nuclear plant
Project control

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-40247-5_61

Fingerprint Dive into the research topics of 'Automatic imagery data analysis for diagnosing human factors in the outage of a nuclear plant'. Together they form a unique fingerprint.

Cite this

Tang, P., Zhang, C., Yilmaz, A., Cooke, N., Boring, R. L., Chasey, A., Vaughn, T., Jones, S., Gupta, A., & Buchanan, V. (2016). Automatic imagery data analysis for diagnosing human factors in the outage of a nuclear plant. In V. G. Duffy (Ed.), Digital Human Modeling: Applications in Health, Safety, Ergonomics and Risk Management - 7th International Conference, DHM 2016 and Held as Part of HCI International 2016, Proceedings (pp. 604-615). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9745). Springer Verlag. https://doi.org/10.1007/978-3-319-40247-5_61

Automatic imagery data analysis for diagnosing human factors in the outage of a nuclear plant. / Tang, Pingbo; Zhang, Cheng; Yilmaz, Alper; Cooke, Nancy; Boring, Ronald Laurids; Chasey, Allan; Vaughn, Timothy; Jones, Samuel; Gupta, Ashish; Buchanan, Verica.

Digital Human Modeling: Applications in Health, Safety, Ergonomics and Risk Management - 7th International Conference, DHM 2016 and Held as Part of HCI International 2016, Proceedings. ed. / Vincent G. Duffy. Springer Verlag, 2016. p. 604-615 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9745).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tang, P, Zhang, C, Yilmaz, A, Cooke, N, Boring, RL, Chasey, A, Vaughn, T, Jones, S, Gupta, A & Buchanan, V 2016, Automatic imagery data analysis for diagnosing human factors in the outage of a nuclear plant. in VG Duffy (ed.), Digital Human Modeling: Applications in Health, Safety, Ergonomics and Risk Management - 7th International Conference, DHM 2016 and Held as Part of HCI International 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9745, Springer Verlag, pp. 604-615, 7th International Conference on Digital Human Modeling, DHM 2016 and Held as Part of 18th International Conference on Human-Computer Interaction, HCI International 2016, Toronto, Canada, 7/17/16. https://doi.org/10.1007/978-3-319-40247-5_61

Tang P, Zhang C, Yilmaz A, Cooke N, Boring RL, Chasey A et al. Automatic imagery data analysis for diagnosing human factors in the outage of a nuclear plant. In Duffy VG, editor, Digital Human Modeling: Applications in Health, Safety, Ergonomics and Risk Management - 7th International Conference, DHM 2016 and Held as Part of HCI International 2016, Proceedings. Springer Verlag. 2016. p. 604-615. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-40247-5_61

Tang, Pingbo ; Zhang, Cheng ; Yilmaz, Alper ; Cooke, Nancy ; Boring, Ronald Laurids ; Chasey, Allan ; Vaughn, Timothy ; Jones, Samuel ; Gupta, Ashish ; Buchanan, Verica. / Automatic imagery data analysis for diagnosing human factors in the outage of a nuclear plant. Digital Human Modeling: Applications in Health, Safety, Ergonomics and Risk Management - 7th International Conference, DHM 2016 and Held as Part of HCI International 2016, Proceedings. editor / Vincent G. Duffy. Springer Verlag, 2016. pp. 604-615 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{72489f92eaca43eeb0baf5488c57de88,

title = "Automatic imagery data analysis for diagnosing human factors in the outage of a nuclear plant",

abstract = "Nuclear power plant (NPP) outages involve maintenance and repair activities of a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working around the NPP. Extremely high outage costs and expensive delays in maintenance projects (around $1.5 million per day) require tight outage schedules (typically 20 days). In such packed workspaces, real-time human behavior monitoring is critical for ensuring safe collaboration among workers, minimal wastes of time and resources due to the lack of situational awareness, and timely project control. Current methods for detailed human behavior monitoring on construction sites rely on manual imagery data collection and analysis, which is tedious and error-prone. This paper presents a framework of automatic imagery data analysis that enables real-time detection and diagnosis of anomalous human behaviors during outages, through the integration of 4D construction simulation and object tracking algorithms.",

keywords = "Computer vision, Construction automation, Human factors, Nuclear plant, Project control",

author = "Pingbo Tang and Cheng Zhang and Alper Yilmaz and Nancy Cooke and Boring, {Ronald Laurids} and Allan Chasey and Timothy Vaughn and Samuel Jones and Ashish Gupta and Verica Buchanan",

year = "2016",

doi = "10.1007/978-3-319-40247-5_61",

language = "English (US)",

isbn = "9783319402468",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "604--615",

editor = "Duffy, {Vincent G.}",

booktitle = "Digital Human Modeling",

note = "7th International Conference on Digital Human Modeling, DHM 2016 and Held as Part of 18th International Conference on Human-Computer Interaction, HCI International 2016 ; Conference date: 17-07-2016 Through 22-07-2016",

}

TY - GEN

T1 - Automatic imagery data analysis for diagnosing human factors in the outage of a nuclear plant

AU - Tang, Pingbo

AU - Zhang, Cheng

AU - Yilmaz, Alper

AU - Cooke, Nancy

AU - Boring, Ronald Laurids

AU - Chasey, Allan

AU - Vaughn, Timothy

AU - Jones, Samuel

AU - Gupta, Ashish

AU - Buchanan, Verica

PY - 2016

Y1 - 2016

N2 - Nuclear power plant (NPP) outages involve maintenance and repair activities of a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working around the NPP. Extremely high outage costs and expensive delays in maintenance projects (around $1.5 million per day) require tight outage schedules (typically 20 days). In such packed workspaces, real-time human behavior monitoring is critical for ensuring safe collaboration among workers, minimal wastes of time and resources due to the lack of situational awareness, and timely project control. Current methods for detailed human behavior monitoring on construction sites rely on manual imagery data collection and analysis, which is tedious and error-prone. This paper presents a framework of automatic imagery data analysis that enables real-time detection and diagnosis of anomalous human behaviors during outages, through the integration of 4D construction simulation and object tracking algorithms.

AB - Nuclear power plant (NPP) outages involve maintenance and repair activities of a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working around the NPP. Extremely high outage costs and expensive delays in maintenance projects (around $1.5 million per day) require tight outage schedules (typically 20 days). In such packed workspaces, real-time human behavior monitoring is critical for ensuring safe collaboration among workers, minimal wastes of time and resources due to the lack of situational awareness, and timely project control. Current methods for detailed human behavior monitoring on construction sites rely on manual imagery data collection and analysis, which is tedious and error-prone. This paper presents a framework of automatic imagery data analysis that enables real-time detection and diagnosis of anomalous human behaviors during outages, through the integration of 4D construction simulation and object tracking algorithms.

KW - Computer vision

KW - Construction automation

KW - Human factors

KW - Nuclear plant

KW - Project control

UR - http://www.scopus.com/inward/record.url?scp=84978829515&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84978829515&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-40247-5_61

DO - 10.1007/978-3-319-40247-5_61

M3 - Conference contribution

AN - SCOPUS:84978829515

SN - 9783319402468

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 604

EP - 615

BT - Digital Human Modeling

A2 - Duffy, Vincent G.

PB - Springer Verlag

T2 - 7th International Conference on Digital Human Modeling, DHM 2016 and Held as Part of 18th International Conference on Human-Computer Interaction, HCI International 2016

Y2 - 17 July 2016 through 22 July 2016

ER -