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Special Issue "Sensing Application for Non-intrusive Load Monitoring"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 15 October 2020.

Special Issue Editor

Dr. Howon Kim
Website
Guest Editor
School of Computer Science & Engineering, Pusan National University, Busan, Korea
Interests: deep learning; information security; blockchain; intelligent IoT

Special Issue Information

Dear Colleagues,

It is my pleasure to invite submissions to a Special Issue of the journal Sensors on the topic of “Non-Intrusive Load Monitoring Using Sensor Signal and Information Processing”.  Non-intrusive load monitoring (NILM) using sensor signal and information processing is an underlying field of research focusing on the mathematical foundations and practical appliances of signal processing algorithms that learn, reason, and act in smart energy monitoring. In NILM, energy disaggregation is evolving toward a more and more interesting topic. However, it requires an efficient technique to build real applications for control and manage efficient home energy consumption. In particular, the application of signal processing techniques yields an enhancement in the recognition of performance of household appliances based on standard hardware through the addition of computational capability. In order to achieve the goal, proper modeling of the devices or systems should be obtained, thus allowing to develop optimized algorithms which are suitable to the specific application. 

With this Special Issue, we would like to encourage original contributions regarding recent developments and ideas in non-intrusive load monitoring. Potential topics include but are not limited to: ·      

  • Data science and analytics for big data;·      
  • Event or non-event detection;·      
  • Signal processing for smart grid, load forecasting, and energy management;·      
  • Machine learning for signal and image processing;·      
  • Deep-learning-based neural network;·      
  • Generative neural network (GAN);·      
  • Transfer learning;Federate learning.

Prof. Howon Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Household appliance
  • Energy disaggregation
  • Monitoring systems
  • Image processing
  • Signal processing
  • Event and non-event detection
  • Machine learning and deep learning
  • GAN
  • Transfer learning
  • Federate learning

Published Papers

This special issue is now open for submission.
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