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Transactions of the Institute of Systems, Control and Information Engineers Vol. 37 (2024), No. 9

ISIJ International
belloff
ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668
Publisher: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

Backnumber

  1. Vol. 37 (2024)

    1. No.9
    2. No.8
    3. No.7
    4. No.6
    5. No.5
    6. No.4
    7. No.3
    8. No.2
    9. No.1

  2. Vol. 36 (2023)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
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  3. Vol. 35 (2022)

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  4. Vol. 34 (2021)

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  5. Vol. 33 (2020)

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  6. Vol. 32 (2019)

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  7. Vol. 31 (2018)

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  8. Vol. 30 (2017)

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  9. Vol. 29 (2016)

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  10. Vol. 28 (2015)

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  11. Vol. 27 (2014)

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  12. Vol. 26 (2013)

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  13. Vol. 25 (2012)

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  14. Vol. 24 (2011)

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  15. Vol. 23 (2010)

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  16. Vol. 22 (2009)

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  17. Vol. 21 (2008)

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  18. Vol. 20 (2007)

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  19. Vol. 19 (2006)

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  20. Vol. 18 (2005)

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  21. Vol. 17 (2004)

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  22. Vol. 16 (2003)

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  23. Vol. 15 (2002)

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  24. Vol. 14 (2001)

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  25. Vol. 13 (2000)

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    5. No.8
    6. No.7
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  26. Vol. 12 (1999)

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    6. No.7
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  27. Vol. 11 (1998)

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    4. No.9
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  28. Vol. 10 (1997)

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  29. Vol. 9 (1996)

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  30. Vol. 8 (1995)

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    4. No.9
    5. No.8
    6. No.7
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  31. Vol. 7 (1994)

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  32. Vol. 6 (1993)

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    5. No.8
    6. No.7
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  33. Vol. 5 (1992)

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    6. No.7
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  34. Vol. 4 (1991)

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  35. Vol. 3 (1990)

    1. No.12
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    3. No.10
    4. No.9
    5. No.8
    6. No.7
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  36. Vol. 2 (1989)

    1. No.12
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    3. No.10
    4. No.9
    5. No.8
    6. No.7
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    11. No.2
    12. No.1

  37. Vol. 1 (1988)

    1. No.7
    2. No.6
    3. No.5
    4. No.4
    5. No.3
    6. No.2
    7. No.1

Keyword Ranking

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 37 (2024), No. 9

Recurrent Neural Networks Considering Natural Conditions for Determination of the Reinforcement Degrees when Constructing Infrastructure Structures

Haruhisa Miyahara, Keiji Tatsumi, Yeboon Yun

pp. 237-246

DOI:

10.5687/iscie.37.237

Abstract

In recent years, the use of AI has been promoted in the construction industry to solve labour shortages. This study focuses on determining the reinforcement degree when constructing a kind of infrastructure, which has required the empirical knowledge of experts, and proposes a machine learning method to automate the process. The target structure is divided into many sections, and the reinforcement degree for each section needs to be determined based on observation data.

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Recurrent Neural Networks Considering Natural Conditions for Determination of the Reinforcement Degrees when Constructing Infrastructure Structures

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Hierarchical Decentralized Management and Experimental Evaluation of Virtual Power Plants consisting of Multiple Facilities include Generation, Storage and Demand Equipment

Hikaru Akutsu, Kenji Hirata, Hayato Terasaki, Satoru Kakinoki, Takashi Kitamura, Akihiro Ohori, Nobuyuki Hattori, Yoshito Ohta

pp. 247-256

DOI:

10.5687/iscie.37.247

Abstract

This paper considers a hierarchical decentralized control for virtual power plants. Virtual power plants act as aggregator of electric energy resources, including distributed generator, battery storage/electric vehicle and controllable loads. A virtual power plant becomes a large-scale system containing a large number of electric power equipment. A centralized management method may not be suitable for operation of virtual power plants. We propose the individual optimization by each equipment and real-time pricing strategy. The proposed management methodology allows plug-and-play type operation and can mitigate the effects of uncertainties due to weather condition, load profiles, machine failure or installation of additional equipment. The effectiveness of the proposed hierarchical decentralized management method is evaluated through real physical experiments including real-scale electric power equipment and actual load changes.

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Hierarchical Decentralized Management and Experimental Evaluation of Virtual Power Plants consisting of Multiple Facilities include Generation, Storage and Demand Equipment

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