Transition processes have been studied in gas sensor models based on catalytically active electrodes (cae) and hydrogenated solid proton electrolyte in reaching current-free potentials and the current of chlorine and ozone reduction.
To do it, we develop a sensor node model based on virtual buffer sharing and present a two-layer scheduling model using the network calculus.
Our preliminary results indicate that a fall risk model based on accelerometer sensor data performs almost as well as a model that is derived from conventional geriatric assessment data.
Therefore, the goal of this experimental research was to develop a drill wear classification model based on multi-sensor approach and artificial neural network algorithm.
The results demonstrate that the Partial Least Squares (PLS) regression model based on cavity pressure sensor data could be successfully used to monitor the quality (weight, dimensions) of the final product in plastic injection moulding.
The decision tree model based on CART algorithm was used to execute on-line vehicle classification in the sensor node.
Develop a model based on theory.
In sensor technology, analytical modeling based on experimental finding is still ongoing.
Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched.
