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Nevertheless, the use of model based sensors introduces unavoidable performance penalties.
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The latter one leads to a model based sensor-less correction [16] which often uses Zernike modes.
Two examples are provided for performance evaluation of the proposed method, with detailed comparative studies to the traditional PLS and co-training kNN model based soft sensors.
The discipline deals with high-end systems in the form of ground and satellite based sensors, computer modeling and simulation, and wind tunnel experiments.
As these physicochemical properties are not measurable on-line, a model based soft-sensor is developed.
The antibody based sensors are also known as immunosensors.
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.
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.
An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed.
Our approach employs a discrete Bayes filter with a sensor model based on whole image descriptors.
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.
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