| 期刊全稱 | A Neural Network Approach to Fluid Quantity Measurement in Dynamic Environments | | 影響因子2023 | Edin Terzic,Jenny Terzic,Muhammad Alamgir | | 視頻video | http://file.papertrans.cn/142/141515/141515.mp4 | | 發(fā)行地址 | Investigates the effectiveness of signal enhancement on the neural network based signal classification system.Compares results obtained from the investigation with traditionally used statistical avera | | 圖書封面 |  | | 影響因子 | .Sloshing causes liquid to fluctuate, making accurate level readings difficult to obtain in dynamic environments. The measurement system described uses a single-tube capacitive sensor to obtain an instantaneous level reading of the fluid surface, thereby accurately determining the fluid quantity in the presence of slosh. A neural network based classification technique has been applied to predict the actual quantity of the fluid contained in a tank under sloshing conditions. .?.In .A neural network approach to fluid quantity measurement in dynamic environments., effects of temperature variations and contamination on the capacitive sensor are discussed, and the authors propose that these effects can also be eliminated with the proposed neural network based classification system. To examine the performance of the classification system, many field trials were carried out on a running vehicle at various tank volume levels that range from 5 L to 50 L. The effectiveness of signal enhancement on the neural network based signal classification system is also investigated. Results obtained from the investigation are compared with traditionally used statistical averaging methods, and proves th | | Pindex | Book 2012 |
The information of publication is updating
|
|
|Archiver|手機(jī)版|小黑屋|
派博傳思國際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-22 08:49
發(fā)表于 2025-3-21 17:38:25
