Change search
Refine search result
1 - 4 of 4
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Liu, Hongyi
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Sustainable production development.
    Fang, Tongtong
    KTH.
    Zhou, Tianyu
    KTH.
    Wang, Yuquan
    KTH, School of Industrial Engineering and Management (ITM), Sustainable production development.
    Wang, Lihui
    Deep Learning-based Multimodal Control Interface for Human-Robot Collaboration2018In: 51st CIRP Conference on Manufacturing Systems, Elsevier, 2018, Vol. 72, p. 3-8Conference paper (Refereed)
    Abstract [en]

    In human-robot collaborative manufacturing, industrial robot is required to dynamically change its pre-programmed tasks and collaborate with human operators at the same workstation. However, traditional industrial robot is controlled by pre-programmed control codes, which cannot support the emerging needs of human-robot collaboration. In response to the request, this research explored a deep learning-based multimodal robot control interface for human-robot collaboration. Three methods were integrated into the multimodal interface, including voice recognition, hand motion recognition, and body posture recognition. Deep learning was adopted as the algorithm for classification and recognition. Human-robot collaboration specific datasets were collected to support the deep learning algorithm. The result presented at the end of the paper shows the potential to adopt deep learning in human-robot collaboration systems.

  • 2.
    Shahbazi, Sasha
    et al.
    Swerea IVF.
    Wiktorsson, Magnus
    KTH, School of Industrial Engineering and Management (ITM), Sustainable production development.
    Kurdve, Martin
    Chalmers Technical University.
    Using the Green Performance Map: Towards Material Efficiency Measurement2019In: Operations Management and Sustainability - New Research Perspectives / [ed] Luitzen de Boer, Poul Houman Andersen, Palgrave Macmillan, 2019Chapter in book (Refereed)
  • 3.
    Trucco, Paolo
    et al.
    Politecnico di Milano.
    Negri, Alexandra
    Politecnico di Milano.
    Birkie, Seyoum Eshetu
    KTH, School of Industrial Engineering and Management (ITM), Sustainable production development. KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Management. KTH Royal Institute of Technology.
    The influence of structural and dynamic complexity factors on supply chain resilience: a qualitative study2018In: Proceedings of 25th EurOMA Conference, 2018Conference paper (Refereed)
    Abstract [en]

    This study aims to investigate the implications of supply chain (SC) complexity (based on static and dynamic complexity drivers) on constituents of resilience capabilities to deal with disruptions. A systematic qualitative analysis based on critical incident technique has been applied on secondary data collected on disruption incidents. Findings indicate that most of the complexity drivers have positive influence on several resilience constit-uents; however, negative effects were observed as well. Static SC complexity drivers seem to have both positive and negative effects on resilience, while dynamic complexity drivers seem to reinforce SC resilience.

  • 4.
    Trucco, Paolo
    et al.
    Politecnico di Milano.
    Petrenj, Boris
    Politecnico di Milano.
    Birkie, Seyoum Eshetu
    KTH, School of Industrial Engineering and Management (ITM), Sustainable production development. KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Management. KTH Royal Institute of Technology.
    Assessing supply chain vulnerabilities upon critical infrastructure disruptions: a multilevel modelling approach2018In: Supply Chain Risk Management: Advanced Tools, Models, and Developments: / [ed] Yacob Khojasteh, Tokyo: Springer, 2018, p. 311-334Chapter in book (Refereed)
1 - 4 of 4
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf