They found that a fault in data collection can significantly increase the risk on the usefulness of the outcome of image-based 3D reconstruction models.
However, the parties in charge of financial data are presently too busy lamenting the faults in current data structures to listen.
We propose an approach for tolerating faults in multisensor data fusion that is based on the more traditional method of duplication comparison, and that offers detection and recovery services.
So, it requires high velocity, scalability and fault tolerance in data processing, storage and visualization.
The application of the proposed h s graph approach (both methods) is specifically illustrated for classification of an emulated fault transient in data from the real prototype Pebble Bed Micro Model (PBMM) plant.
The final part of this work reports the application of the proposed approach for classification of an emulated fault transient in data from the prototype Pebble Bed Micro Model (PBMM) plant.
Recently, the Hadoop platform has emerged as an interesting option to address the challenges of increasingly large datasets with distributed storage, distributed processing, built-in data locality, fault tolerance, and an appealing programming methodology.
In order to enhance generalization level of data samples, multiple faults are simulated in data producing procedure to consider N − k (k ≤ 3) scenarios.
Figure 9 (right) shows the horizontal position error (HPE) in the case of a real (non-simulated) fault in measurement data during a coastal approach.
The ranked top 15 faults in the historical data could be selected as the preconceived accident list to represent the fault occurrence condition [17].
