Imaging quality judgement provides useful meaning to benefit intelligent visual power systems. However, accurate identification of possible failures of the monitoring equipment remains challenging. This paper proposes a novel deep architecture to improve the assessment of abnormalities by considering a novel mutual-guided convolutional attention network (MGCAN) and a multi-region scheme. More specifically, the MGCAN exploits various adequate, low-level information to generate spatial and structural flows. Accordingly, it further extends attention mechanisms to model both inter-flow correlations and inter-channel relationships in the network design. The multi-region scheme features a spatial pyramid random-crop strategy and a region-fusion strategy to handle locally non-uniform characteristics among categories. In this way, the whole architecture provides an end-to-end and adaptive learning procedure relevant to quality perception that focuses on learning important features and mining discriminative regions. Experimental results demonstrate its superiority to prior methods for judging abnormalities. The proposed method can be easily extended to an entire surveillance system.INDEX TERMS Abnormal judgement, power systems, deep learning, attention networks, region fusion.
With the gradual increase in the number of GNSS systems and the improvement of functions, in addition to the singlesystem navigation and timing service, the integrated navigation and positioning service among multiple systems can provide users with more accurate and stable positioning results, arousing more attention from the workers in GNSS field. Compatibility and interoperability among different systems has become a trend in the development of GNSS. Compatibility and interoperability between systems require a uniform time scale. Therefore, the measurement and forecasting of time deviations in GNSS systems is particularly important. This paper first studies the multi-system fusion location model and proposes an adaptive GNSS fusion PPP algorithm based on parameter equivalent reduction. Then, the method of fusion PPP is used to monitor the time difference of GNSS. Finally, the effectiveness of the improved algorithm and time difference monitoring method is verified by practical examples.
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