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Identification of rotating sources with non-uniform directivity has been paid much attention in the field of aeroacoustic measurements over recent years. Singularities may be produced on the source map by using the rotating source identifier based on the multipole model due to zeros of the directivity function. A correction method is proposed to remove the influence of source directivity on source imaging and restrain the singular problem. De-Dopplerized microphone signals are transformed to the frequency domain and deconvolved with transfer functions to compensate for directivity functions. Numerical simulations, as well as experiments using rotating dipole loudspeakers, were conducted to verify the proposed method. It is demonstrated that the method is suitable for rotating sources with arbitrary orientation and works well under a high level of background noise. Positions and strengths of sources are estimated more accurately than traditional algorithms.

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Software-defined networking (SDN) separates the control layer from the data layer, and decisions tomanage the network are issued through a controller. The distributed SDN architecture is an effectivesolution addressing modern WAN SDN architectures and allows multiple controllers to managedifferent parts of the network to ensure efficient and stable operation. To solve the problems of highswitch migration cost, load imbalance, and inefficient load balancing in SDN multi-controller environments,we propose a deep learning-based controller load prediction switch migration (LPSM) strategythat uses a migration switch selection algorithm, target controller selection algorithm, and switchmigration decision algorithm. Then, we propose a load balancing algorithm based on this decisionalgorithm. The final experimental results show that the LPSM reduces the migration cost by 16%and 8%, respectively, compared with time-sharing switch migration (TSSM) and distributed decisionmigration (DDM) strategies, reduces load variance from 0.02 to 0.004 compared with the DDMstrategy, and improves load balancing efficiency by 27.6% compared with the TSSM strategy.

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