Face aging tasks aim to simulate changes in the appearance of faces over time. However, due to the lack of data on different ages under the same identity, existing models are commonly trained using mapping between age groups. This makes it difficult for most existing aging methods to accurately capture the correspondence between individual identities and aging features, leading to generating faces that do not match the real aging appearance. In this paper, we re-annotate the CACD2000 dataset and propose a consensus-agent deep reinforcement learning method to solve the aforementioned problem. Specifically, we define two agents, the aging process agent and the aging personalization agent, and model the task of matching aging features as a Markov decision process. The aging process agent simulates the aging process of an individual, while the aging personalization agent calculates the difference between the aging appearance of an individual and the average aging appearance. The two agents iteratively adjust the matching degree between the target aging feature and the current identity through a form of synergistic cooperation. Extensive experimental results on four face aging datasets show that our model achieves convincing performance compared to the current state-of-the-art methods.
Proton exchange membrane (PEM) fuel cell faces the inevitable challenge of the cold start at a sub-freezing temperature. Understanding the underlying degradation mechanisms in the cold start and developing a better starting strategy to achieve a quick startup with no degradation are essential for the wide application of PEM fuel cells. In this study, the comprehensive in situ non-accelerated segmented techniques are developed to analyze the icing processes and obtain the degradation mechanisms under the conditions of freeze-thaw cycle, voltage reversal, and ice formation in different components of PEM fuel cells for different freezing time. A detailed degradation mechanism map in the cold start of PEM fuel cells is proposed to demonstrate how much degradation occurs under different conditions, whether the ice formation is acceptable under the actual operating conditions, and how to suppress the ice formation. Moreover, an ideal starting strategy is developed to achieve the cold start of PEM fuel cells without degradation. This map is highly valuable and useful for researchers to understand the underlying degradation mechanisms and develop the cold start strategy, thereby promoting the commercialization of PEM fuel cells.
