Abstract: A vehicle for collecting image data of a target object for generating a classifier. The vehicle includes an image sensor and an electronic processor. The electronic processor is configured to determine a plurality of potential trajectories of the vehicle, determine, for each of the plurality of potential trajectories of the vehicle, a total number of views including the target object that would be captured by the image sensor as the vehicle moved along the respective trajectory, and determine a key trajectory of the vehicle from the plurality of potential trajectories based on the total number of views including the target of the key trajectory.

Abstract: Technical effects of the invention include use of a data-driven multivariate statistical method for the detection and isolation of sensor faults applied in a virtual flow metering context. In one implementation, the data-driven multivariate statistical method employs principal components analysis, weighted squared prediction error, and partial decomposition contribution plots.

Abstract: A vehicle for collecting image data of a target object for generating a classifier. The vehicle includes an image sensor and an electronic processor. The electronic processor is configured to determine a plurality of potential trajectories of the vehicle, determine, for each of the plurality of potential trajectories of the vehicle, a total number of views including the target object that would be captured by the image sensor as the vehicle moved along the respective trajectory, and determine a key trajectory of the vehicle from the plurality of potential trajectories based on the total number of views including the target of the key trajectory.

Abstract: The present approach relates to establishing metrics for the computation of uncertainty boundaries for mean values for pressure and temperature drop error and mean values for mass flow error. Using such metric, sensor inaccuracies may be accounted for in the calibration and/or estimation processes of a virtual flow meter. For example, these values may be employed in the assessment of improvement in a calibration process of virtual flow meters, which will facilitate maintaining the accuracy of such virtual flow meters.

Abstract: Technical effects of the invention include use of a data-driven multivariate statistical method for the detection and isolation of sensor faults applied in a virtual flow metering context. In one implementation, the data-driven multivariate statistical method employs principal components analysis, weighted squared prediction error, and partial decomposition contribution plots.

Abstract: The present approach relates to establishing metrics for the computation of uncertainty boundaries for mean values for pressure and temperature drop error and mean values for mass flow error. Using such metric, sensor inaccuracies may be accounted for in the calibration and/or estimation processes of a virtual flow meter.