Optimal Sensor Placement and Online Spatiotemporal Modeling for Parabolic Distributed Parameter System under Sparse Sensing
Clicks: 15
ID: 315173
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
4.2
/100
15 views
2 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #8 of 31 articles by views in journal of computational design and engineering
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Restrictions on the number and placement of sensors are common in the modeling of parabolic distributed parameter systems (DPSs). Since the information from measurements is incomplete, developing an accurate approximation model and capturing their dynamic behavior for time-varying DPSs under sparse sensing is a challenge. In this paper a novel online spatiotemporal modeling framework includ data completion and optimal sensor placement is developed. Firstly, in the offline initialization phase, a set of offline data collected under a full sensing environment is used to learn the initial full spatial basis functions (BFs) and establish an initial temporal model. Then, a completion algorithm is developed to reconstruct the sparse data into full data, and the optimal sensor position is selected by the offline data based on the underlying algebraic structure of the recovery error. Finally, in the online learning phase with few sensors, by utilizing incremental learning techniques, an online learning strategy is designed in which both spatial BFs and the temporal model can be recursively updated from new data. The performance and effectiveness of the proposed method are verified through an experimental study of a flat-plate lithium-ion battery thermal process. Precise temperature distribution of the battery is modeled through only 3 sensors. The model accuracy is very close to that achieved with full sensing data, and the processing speed is significantly faster.
| Reference Key |
openalex_W7162685981
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Xi Jin, Zhanhui Zhang, Chengjiu Zhu, Kangkang Xu |
| Journal | journal of computational design and engineering |
| Year | 2026 |
| DOI |
10.1093/jcde/qwag048
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.