A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network.

The safety and reliability of rotating machinery hinge significantly on the proper functioning of rolling bearings. In the last few years, there have been significant advances in the algorithms for intelligent fault diagnosis of bearings. However, the vibration signals collected by machines are inevitably affected by irrelevant noise because of the complex working environments of bearings.

Geometric Engineering of Porous PtCu Nanotubes with Ultrahigh Methanol Oxidation and Oxygen Reduction Capability.

Platinum (Pt), as a commonly used electrocatalyst in direct methanol fuel cells (DMFCs), suffers from sluggish kinetics of both the methanol oxidation reaction (MOR) and oxygen reduction reaction (ORR). Geometric engineering has been proven effective for improving the MOR and ORR activities. Thus, by modulating the Pt precursor and poly(vinylpyrrolidone) (PVP) dosages, different porous PtCu nanotubes constructed by hollow nanospheres, solid alloy, and Pt-rich skinned nanoparticles, respectively, are successfully synthesized.

A data-driven model for real-time water quality prediction and early warning by an integration method.

Due to increasingly serious deterioration of surface water quality, effective water quality prediction technique for real-time early warning is essential to guarantee the emergency response ability in advance for sustainable water management. In this study, an effective data-driven model for surface water quality prediction is developed to analyze the inherent water quality variation tendencies and provide real-time early warnings according to the historical observation data. The developed data-driven model is integrated by an improved genetic algorithm (IGA) for selecting optimal initial weight parameters of neural a network and a back-propagation neural network (BPNN) for adjusting appropriate connection architectures of neural network.

The Application of Social Characteristic and L1 Optimization in the Error Correction for Network Coding in Wireless Sensor Networks.

One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the network coding usually propagates a single original error over the whole network.