A comparative
study has been made to relate the effect of different
addition strategies for the sintering aid of ZnO (∼1 wt %)
on the performance of the BaZr0.4Ce0.4Y0.2O3−δ (BZCY442) electrolyte in order
to improve the sinterability and conductivity of this material more
efficiently. Two samples, Zn-doped BaCe0.4Zr0.4Y0.15Zn0.05O3−δ (BZCYZn,
internal doping) and BZCY–ZnO (BZCY442 mixed with ZnO, external
addition), were synthesized. The BZCYZn crystallized at a single phase
after sintering at 1350 °C, and the segregation of Y2O3 was observed in BZCY–ZnO in the same conditions.
The relative densities of the BZCYZn and BZCY–ZnO samples sintered
at 1350 °C for 5 h were 98 and 94%, respectively. The high total
conductivity in BZCYZn benefited from the low barrier height at the
grain boundary core determined by the Mott–Schottky space charge
layer model, which was 0.15 V for BZCYZn and 0.26 V for BZCY–ZnO.
A cell with BZCYZn as the electrolyte generated a power density of
414.5 mW cm–2 at 700 °C. The total ionic transport
number of BZCYZn was determined to be ∼0.90 at 600 °C
under fuel cell conditions.
With advances in computer vision and artificial intelligence technology, facial expression recognition research has become a prominent topic. Current research is grappling with how to enable computers to fully understand expression features and improve recognition rates. Most single face image datasets are based on the psychological classification of the six basic human expressions used for network training. By outlining the problem of facial recognition by comparing traditional methods, deep learning, and broad learning techniques, this review highlights the remaining challenges and future directions of deep learning and broad learning research. The deep learning method has made it easier and more effective to extract expression features and improve facial expression recognition accuracy by end-to-end feature learning, but there are still many difficulties in robustness and real-time performance. The broad learning system (BLS) is a broad network structure that is expanded by increasing the number of feature nodes and enhancement nodes appropriately to reinforce the structure and is also effective in facial expression recognition. However, outliers and noises in unbalanced datasets need BLS to solve in the future. Finally, we present several problems that still need to be addressed in facial expression recognition.
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