Mobile notification systems play a major role in a variety of applications to communicate, send alerts and reminders to the users to inform them about news, events or messages. In this paper, we formulate the near-real-time notification decision problem as a Markov Decision Process where we optimize for multiple objectives in the rewards. We propose an end-to-end offline reinforcement learning framework to optimize sequential notification decisions. We address the challenge of offline learning using a Double Deep Q-network method based on Conservative Q-learning that mitigates the distributional shift problem and Q-value overestimation. We illustrate our fully-deployed system and demonstrate the performance and benefits of the proposed approach through both offline and online experiments.

This paper focuses on the sensorless high-precision position correction strategy for a 100 kW@20 000 r/min brushless dc (BLdc) motor with low stator inductance (0.051 mH). Two key points related to the sensorless were studied. The one is the rotor position detection with high-frequency interference. The other one is the commutation compensation for the high-speed BLdc motor with low stator inductance in whole speed range. In order to filter out the high-frequency interference, the main factors, which that can influence the rotor position detection and the commutation accuracy were analyzed. In order to compensate the commutation error in high-precision and whole speed range, the relationship between the commutation error angle and the phase current deviation was derived. It is noted that the commutation time and the phase current error can exactly reflect the commutation error angle. Then, a novel sensorless high-precision position correction strategy based on the uncommutation current was proposed. Finally, the experiment platform based on a 100 kW@20 000 r/min BLdc motor was built, and the validity and efficiency of the proposed strategy were proved.

In heterogeneous wireless networks, rank aggregation based on multiple decision factors has recently been proposed as a useful approach for network selection. For each decision factor, a rank of the candidate networks is derived according to the value of this decision factor. Then these decision factor dependent ranks are aggregated into a single rank and the top one is considered as a favorite network. However in the realistic scenario, the measurement on decision factor is often inaccurate so that the candidate networks are possibly not ranked appropriately. As a result, if there only exists the slight differences between the decision factor values of several candidate networks, same rank is preferable to these networks. The set of networks is therefore separated into several groups and these groups are ranked accordingly. Moreover the networks tied in the same group have the same rank. In this paper, a new approach namelymedianbasednetworkselectionmethodis therefore proposed to handle such partial tied rank aggregation problem.