SpringerLink
Log in

3D Cable Intelligent Management Platform Based on Parametric Modeling Technology

  • Conference paper
  • First Online:
Proceedings of Innovative Computing 2024 Vol. 1 (IC 2024)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1214))

Included in the following conference series:

  • 18 Accesses

Abstract

With the continuous development of smart grid technology, a higher standard of power quality is proposed. In order to meet the power demand of users, it is of great significance to study the 3D cable intelligent management platform. this paper first analyzes the relevant research results and application status quo based on parametric modeling and object-oriented methods, then combines UML, FDS and other simulation software to establish a three-dimensional cable multi branch electromagnetic state data model and uses grid division to achieve system control scheme design. Finally, the platform is tested by using virtual assembly technology, parameter matching and fluid coupling ideas, the 3D cable intelligent management platform based on parametric modeling technology performs well in monitoring cable transmission time test, and the platform compatibility is more than 80%. This shows that it can provide users with a good management platform to complete the interaction between the functional modules of the device platform system and the database.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (Canada)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (Canada)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aso, N., Yanami, H., Ogawa, M.: Automatic parametric modeling technique for structural design standardization. IEEE Access 10, 81031–81041 (2022)

    Article  Google Scholar 

  2. Singhadia, A., Pati, P.S., Singhal, C., Chakrabarti, I.: Efficient HEVC encoding to meet bitrate and PSNR requirements using parametric modeling. Circuits Syst. Signal Process. 41(8), 4479–4511 (2022)

    Article  Google Scholar 

  3. Allerbo, O., Jörnsten, R.: Flexible, non-parametric modeling using regularized neural networks. Comput. Stat. 37(4), 2029–2047 (2022)

    Article  MathSciNet  Google Scholar 

  4. El Motaki, S., Yahyaouy, A., Gualous, H.: Modeling the correlation between the workload and the power consumed by a server using stochastic and non-parametric approaches. Softw. Pract. Exp. 52(10), 2177–2190 (2022)

    Article  Google Scholar 

  5. Evers, E., de Jager, B., Oomen, T.: Incorporating prior knowledge in local parametric modeling for frequency response measurements: applied to thermal/mechanical systems. IEEE Trans. Control Syst. Technol. 30(1), 142–152 (2022)

    Article  Google Scholar 

  6. D’Alterio, P., Garibaldi, J.M., Wagner, C.: A constrained parametric approach for modeling uncertain data. IEEE Trans. Fuzzy Syst. 30(9), 3967–3978 (2022)

    Article  Google Scholar 

  7. Rosenberg, L.: Parametric modeling of sea clutter doppler spectra. IEEE Trans. Geosci. Remote Sens. 60, 1–9 (2022)

    Google Scholar 

  8. Ncwane, S., Folly, K.A.: Modeling wind speed using parametric and non-parametric distribution functions. IEEE Access 9, 104501–104512 (2021)

    Article  Google Scholar 

  9. Crescentini, M., et al.: Online EIS and diagnostics on Lithium-Ion batteries by means of low-power integrated sensing and parametric modeling. IEEE Trans. Instrum. Meas. 70, 1–11 (2021)

    Google Scholar 

  10. Santesteban, I., Garces, E., Otaduy, M.A., Casas, D.: SoftSMPL: data-driven modeling of nonlinear soft-tissue dynamics for parametric humans. Comput. Graph. Forum 39(2), 65–75 (2020)

    Article  Google Scholar 

  11. Okyar, F., Guldeniz, O., Atalay, B.: A holistic parametric design attempt towards geometric modeling of the lumbar spine. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 8(1), 65–75 (2020)

    Google Scholar 

  12. Piraud, M., et al.: Towards quantitative imaging biomarkers of tumor dissemination: a multi-scale parametric modeling of multiple myeloma. Med.Image Anal. 57, 214–225 (2019)

    Article  Google Scholar 

Download references

Acknowledgment

Young and Middle-aged Teachers in Guangxi Universities (2020KY23021); Guangxi Higher Education Undergraduate Teaching Reform Project (2022JGA375); School-level scientific research fund projects (GXKS2022QN006).

Author information

Authors and Affiliations

  1. College of Vocational and Technical Education, Guangxi Science and Technology Normal University, Laibin, Guangxi, China

    Tao Luo & **ayun Liu

Authors
  1. Tao Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. **ayun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to **ayun Liu .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, University of Aizu, Aizuwakamatsu Shi, Japan

    Yan Pei

  2. Department of Computer Science and Information Engineering, National Taichung University of Science, North District, Taiwan

    Hao Shang Ma

  3. Department of Computer Science and Information Management, Providence University, Taichung, Taiwan

    Yu-Wei Chan

  4. Humanitas College, Kyung Hee University, Dongdaemun-gu, Korea (Republic of)

    Hwa-Young Jeong

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Luo, T., Liu, X. (2024). 3D Cable Intelligent Management Platform Based on Parametric Modeling Technology. In: Pei, Y., Ma, H.S., Chan, YW., Jeong, HY. (eds) Proceedings of Innovative Computing 2024 Vol. 1. IC 2024. Lecture Notes in Electrical Engineering, vol 1214. Springer, Singapore. https://doi.org/10.1007/978-981-97-4193-9_36

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-4193-9_36

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-4192-2

  • Online ISBN: 978-981-97-4193-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation