Hexagonal close-packed Ni (h-Ni) nanocrystals and face-centered cubic Ni (c-Ni) nanoflowers with uniform size and high dispersion have been successfully assembled on graphene nanosheets (GN) via a facile one-step solution-phase strategy under different reaction conditions, where the reduction process of graphite oxide (GO) sheets into GN was accompanied by the generation of Ni nanocrystals. The reduction of GO by this method is effective, which was confirmed by X-ray diffraction (XRD), Fourier transform infrared (FTIR) and Raman spectroscopy and is comparable to conventional methods. The phase and morphology of nickel can be easily tuned by varying the reaction temperature and solvent. It was shown that the as-formed h-Ni nanocrystals with a diameter as small as 3 nm are grown densely and uniformly on the graphene sheets, and as a result the aggregation of the h-Ni nanocrystals was effectively prevented. In addition, c-Ni nanospheres assembled by c-Ni nanocrystals with a size of 15 nm were also uniformly deposited on the graphene sheets. The investigation of the microwave absorbability reveals that the three Ni/GN nanocomposites exhibit excellent microwave absorbability, which is stronger than the corresponding Ni nanostructures.

With growing dependence of industrial robots, a failure of an industrial robot may interrupt current operation or even overall manufacturing workflows in the entire production line, which can cause significant economic losses. Hence, it is very essential to maintain industrial robots to ensure high-level performance. It is widely desired to have a real-time technique to constantly monitor robots by collecting time series data from robots, which can automatically detect incipient failures before robots totally shut down. Model-based methods are typically used in anomaly detection for robots, yet explicit domain knowledge and accurate mathematical models are required. Data-driven techniques can overcome these limitations. However, a major difficulty for them is the lack of sufficient fault data of industrial robots. Besides, the used technique for anomaly detection of robots should be required to not only capture the temporal dependency in collected time series data, but also the inter-correlations between different metrics. In this paper, we introduce an unsupervised anomaly detection for industrial robots, sliding-window convolutional variational autoencoder (SWCVAE), which can realize real-time anomaly detection spatially and temporally by coping with multivariate time series data. This method has been verified by a KUKA KR6R 900SIXX industrial robot, and the results prove that the proposed model can successfully detect anomaly in the robot. Thus, this work presents a promising tool for condition-based maintenance of industrial robots.INDEX TERMS Anomaly detection, industrial robots, sliding window, variational autoencoder, convolutional neural network.

Selenium, as one of the chain-like materials, has attracted significant attention recently. Here, we investigated the photo-carrier dynamics in Se quantum dots and demonstrated its use for fast photo-detecting in visible regime.

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