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"A neural network compression method based on knowledge-distillation an" by Mengyu Ji, Gaoliang Peng et al.

Condition monitoring and fault diagnosis have been critical for the optimal scheduling of machines, improving the system reliability and the reducing maintenance cost. In recent years, various of methods based on the deep learning method have made the great progress in the field of the mechanical fault diagnosis. However, there is a conflict between the massive parameters of the fault diagnosis networks and the limited computing resource of the embedded platforms. It is difficult to deploy the trained network on the small scale embedded platforms (like field programmable gate array (FPGA)) in the actual industrial situations. This seriously hinders the practical process of the intelligent fault diagnosis method. To address this problem, a new neural network compression method based on knowledge-distillation (K-D) and parameter quantization is proposed in this paper. In the proposed method, a large scale deep neural network with multiple convolutional layers and fully-connected layers i ....

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Adaptive pushes low-latency open-source messaging - WatersTechnology.com

The London-based consultancy is looking to engage firms and exchanges on its Aeron messaging technology, which it acquired earlier this year. ....

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Lower cross-border latencies open HFT opportunities - WatersTechnology.com

Automated and high-frequency trading in Asia have generally lagged behind US and Europen markets. But as low-latency data availability increases, that may soon ....

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Waters Wrap: Mid-tier market data providers look to reinvent themselves

Anthony loves when his opinions spark debate. Following responses to a recent column on consolidation among mid-market data technology vendors, he provides ....

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