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The architectural drift problem defines the discrepancy between the architecture description and the resulting implementation.
Therefore, as a further contribution of this paper, a novel scheme is designed to avoid the drift problem.
This technique is designed to alleviate the drift problem by using an MIL based appearance model to represent training data in the form of bags.
As a result, the STC model can be updated in time, so the tracking drift problem can be solved and the robustness can be improved.
Computer-simulation results substantiate the theoretical analysis and also show the effective remedy of the joint angle drift problem of robot manipulators.
While a variety of algorithms has been proposed to cope with the sensor drift problem, sensor failures have received much less attention despite their recurrent appearance after a certain time of operation.
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This powerful technique frees the receiver of drift problems involving temperature and power supply voltage excursions.
Also, prolong operation at high temperatures has drift problems caused by sintering and diffusion process [12, 13].
Both of these algorithms can handle big stream data and concept drift problems, and each of them has its own properties.
So the dynamic ensemble learning models can cope with both unlimited increasing amounts of data and concept drift problems in data-stream mining.
In fact, given that no prediction loop is used in the encoding phase, the distributed coding scheme should be more resilient to channel errors since it is not affected by the typical drift problems which may occur in traditional predictive systems.
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