Contra-rotating axial compressor/fan (CRAC) is a promising technology to meet the future goals aircraft industry. Massive time accurate simulations are performed to investigate rotating stall in CRAC containing two counter-rotating rotors. Particularly, the back pressure increasing with a very small step to avoid missing flow field transition from stability to instability. Due to the canceling of the stator, the instability of downstream rotor is more stronger. The present studies mostly focus on the downstream rotor. The tip leakage flow field is analyzed in detail under near stall condition, which indicates that a secondary leakage flow plays an important role in the unsteadiness of CRAC's unsteady flow field. The frequency analysis in the tip clearance of downstream rotor under multiple near stall conditions captured the transition of the second harmonic frequency which can be used as stall inception signal. Moreover, the rotating stall onset process in real CRAC is simulated on the numerical stall.
Anomaly detection in video surveillance is challenging due to the variety of anomaly types and definitions, which limit the use of supervised techniques. As such, auto-encoder structures, a type of classical unsupervised method, have recently been utilized in this field. These structures consist of an encoder followed by a decoder and are typically adopted to restructure a current input frame or predict a future frame. However, regardless of whether a 2D or 3D autoencoder structure is adopted, only single-scale information from the previous layer is typically used in the decoding process. This can result in a loss of detail that could potentially be used to predict or reconstruct video frames. As such, this study proposes a novel spatio-temporal U-Net for frame prediction using normal events and abnormality detection using prediction error. This framework combines the benefits of U-Nets in representing spatial information with the capabilities of ConvLSTM for modeling temporal motion data. In addition, we propose a new regular score function, consisting of a prediction error for not only the current frame but also future frames, to further improve the accuracy of anomaly detection. Extensive experiments on common anomaly datasets, including UCSD (98 video clips in total) and CUHK Avenue (30 video clips in total), validated the performance of the proposed technique and we achieved 96.5% AUC for the Ped2 dataset, which is much better than existing autoencoder-based and U-Net-based methods.
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