Abstract: A dual-stack air-cooled fuel cell includes two stack groups, a fan assembly, an air supply channel, a spoiler and a temperature control device. The fan assembly is centrally located in the air-cooled fuel cell, and is configured to generate an air flow field in the air-cooled fuel cell. The two stack groups are located at an air inlet side and an air outlet side of the fan assembly, respectively. The spoiler is arranged between the fan assembly and the two stack groups. The above components are connected through the air supply channel. The temperature control device is configured to detect surface temperature of the stack groups to adjust the rotation direction of the fan assembly, so as to alternately perform suction and blowing operation for the two stack groups to reach the heat equilibrium of the fuel cell.

Abstract: The disclosure provides a method for enhancing audio-visual association by adopting self-supervised curriculum learning. With the help of contrastive learning, the method can train the visual and audio model without human annotation and extracts meaningful visual and audio representations for a variety of downstream tasks in the context of a teacher-student network paradigm. Specifically, a two-stage self-supervised curriculum learning scheme is proposed to contrast the visual and audio pairs and overcome the difficulty of transferring between visual and audio information in the teacher-student framework. Moreover, the knowledge shared between audio and visual modality serves as a supervisory signal for contrastive learning. In summary, with the large-scale unlabeled data, the method can obtain a visual and an audio convolution encoder. The encoders are helpful for downstream tasks and cover the training shortage causing by limited data.