Process enhancement of enzyme-catalyzed reactions based on micro- and nano-reactors

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Robust Correlation Tracking for UAV with Feature Integration and Response Map Enhancement

Recently, correlation filter (CF)-based tracking algorithms have attained extensive interest in the field of unmanned aerial vehicle (UAV) tracking. Nonetheless, existing trackers still struggle with selecting suitable features and alleviating the model drift issue for online UAV tracking. In this paper, a robust CF-based tracker with feature integration and response map enhancement is proposed. Concretely, we develop a novel feature integration method that comprehensively describes the target by leveraging auxiliary gradient information extracted from the binary representation. Subsequently, the integrated features are utilized to learn a background-aware correlation filter (BACF) for generating a response map that implies the target location. To mitigate the risk of model drift, we introduce saliency awareness in the BACF framework and further propose an adaptive response fusion strategy to enhance the discriminating capability of the response map. Moreover, a dynamic model update mechanism is designed to prevent filter contamination and maintain tracking stability. Experiments on three public benchmarks verify that the proposed tracker outperforms several state-of-the-art algorithms and achieves a real-time tracking speed, which can be applied in UAV tracking scenarios efficiently

Structural analysis of floating pipes of the fish cage in currents

A numerical model is developed to investigate the structural performance and stress distribution of floating pipes of fish cage subjected to the flow. The modeling approach is based on the joint use of the finite element method using the shell elements to simulate the floating pipes and the hydrodynamic force model improved from the Morison’s equation and lumped-mass method. The hydrodynamic response of the fish cage and forces on the floating pipes can be obtained by the Morison’s equation and lumped-mass method. The stress and deformation of the floating pipes can be evaluated using the finite element method. Using an appropriate iterative scheme, the stress distribution and maximum stress of the floating pipes can be obtained using the proposed model. To validate the numerical model, the numerical results were compared with the data obtained from corresponding physical model tests. The comparisons show the numerical results agree well with the experimental data. On that basis, the simulations of floating pipes in currents are performed to investigate the maximum stress and the critical locations. Simulations of the fish cage in different flow velocity are performed. The effect of the velocity on the deformations and stress of the floating pipes is analyzed. The simulations results show that the stress and deformations drastically increases with the increase of flow velocity. Comparing results of floating pipes with different mooring line arrangements indicates that increasing mooring lines can efficiently lower the stress of the floating pipes. The simulation of the SPM cage system with multiple net cages in current is preformed and the results show the middle cage is most dangerous for the tripartite-cage system

Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization

Mixed-precision quantization (MPQ) suffers from time-consuming policy search process (i.e., the bit-width assignment for each layer) on large-scale datasets (e.g., ISLVRC-2012), which heavily limits its practicability in real-world deployment scenarios. In this paper, we propose to search the effective MPQ policy by using a small proxy dataset for the model trained on a large-scale one. It breaks the routine that requires a consistent dataset at model training and MPQ policy search time, which can improve the MPQ searching efficiency significantly. However, the discrepant data distributions bring difficulties in searching for such a transferable MPQ policy. Motivated by the observation that quantization narrows the class margin and blurs the decision boundary, we search the policy that guarantees a general and dataset-independent property: discriminability of feature representations. Namely, we seek the policy that can robustly keep the intra-class compactness and inter-class separation. Our method offers several advantages, i.e., high proxy data utilization, no extra hyper-parameter tuning for approximating the relationship between full-precision and quantized model and high searching efficiency. We search high-quality MPQ policies with the proxy dataset that has only 4% of the data scale compared to the large-scale target dataset, achieving the same accuracy as searching directly on the latter, and improving the MPQ searching efficiency by up to 300 times