.png){kind=icon}
.png){kind=icon}
Return
Multi-scale diffusion model for gas–solid flow mass flow rate prediction based on electrostatic sensing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.measurement.2026.122860.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
<ul class="list">
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="d1e679">
Ring electrostatic sensor optimized by simulation for gas–solid flow sensitivity.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="d1e684">
New decomposition–diffusion–reconstruction separates trend, transient, and residual.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="d1e689">
Adaptive gating and cross-modal attention fuse conditions during diffusion.
</div></span></li>
<li class="react-xocs-list-item"><span class="list-label">•</span><span class="list-content">
<div class="u-margin-s-bottom" id="d1e694">
Model achieves MSE 0.0298 and MAPE 5.43%.
</div></span></li>
</ul>
Keywords:
Gas–solid mass flow rate prediction
Electrostatic sensor
Multi-scale diffusion model
Journal
IF:
5.6
Papers:
1.9W
Citations:
5.4W