2010
DOI: 10.1016/j.engappai.2009.09.014
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Capacitive sensor-based fluid level measurement in a dynamic environment using neural network

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Cited by 36 publications
(28 citation statements)
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“…Here we have demonstrated that a volumetric array consisting of thermistors and capacitive sensors can be mounted on a flexible polyimide substrate measuring 18 cm x 12 cm and can be completely incorporated into an ostomy appliance measuring 23 cm x 15 cm. The size and appearance of the device is comparable to currently marketed stoma appliances, as can be seen in wall of a rigid structure [21]. A combination of thermal and capacitive sensing has not previously been described in volumetric sensing applications.…”
Section: Discussionmentioning
confidence: 84%
“…Here we have demonstrated that a volumetric array consisting of thermistors and capacitive sensors can be mounted on a flexible polyimide substrate measuring 18 cm x 12 cm and can be completely incorporated into an ostomy appliance measuring 23 cm x 15 cm. The size and appearance of the device is comparable to currently marketed stoma appliances, as can be seen in wall of a rigid structure [21]. A combination of thermal and capacitive sensing has not previously been described in volumetric sensing applications.…”
Section: Discussionmentioning
confidence: 84%
“…A charge and discharge technique of cylindrical capacitive sensor is developed by G. Behzadi et al [11] to measure the liquid level of different liquids like distilled water, mineral water, tap water and salt water. E. Terzic et al [12] have proposed a neural network based capacitive sensor to measured fluid level under dynamic condition and have obtained very low measurement error by using static feed-forward back propagation neural network. F. N. Toth et al [13] have developed a microcontroller based capacitive liquid level measurement and leakage detection system in which the sensing capacitor consists of two parallel electrodes with one electrode grounded and other electrode divided into insulated segments .…”
Section: Introductionmentioning
confidence: 99%
“…In neural network theory, dynamic neural networks are generally more powerful than static networks since they have memory that can remember the past values and states of the network [20]. The output of the dynamic network depends on the current input values as well as on the previous inputs, outputs or states of the network.…”
Section: Experiments Conductingmentioning
confidence: 99%