Self-Water-Removal and Voltage Behavior Improvement of Dead-Ended Proton Exchange Membrane Fuel Cell Stack at Steady-State and Dynamic Conditions

The proton exchange membrane fuel cell (PEMFC) demonstrates high commercial competitiveness due to its advantages: low operating temperature, high power/mass ratio, fast response, no emission, and low noise. Thermal and water management remains a challenging issue for ensuring the fuel cell’s performance at steady-state and dynamic conditions. The cathode moisture condensation uses a semiconductor cooler to effectively remove excess water from the PEMFCs and reduce the probability of flooding of the stack. The stack’s voltage uniformity is an essential factor that affects the performance and lifetime of PEMFC. This paper investigates the dynamic response characteristics of the voltage uniformity of a PEMFC stack under cathode moisture condensation conditions. The results show that the condensation temperature at 10°C during the steady-state or transient operation of the PEMFC can effectively optimize the stack’s performance. Compared to conventional PEMFC, applying the cathode moisture condensation technology to the PEMFC stack increases the stack voltage by 6% and decreases the voltage uniformity by up to 30%. This self-water-removal technology effectively improves the voltage uniformity of the stack, which then increase the stack durability

LT4REC:A Lottery Ticket Hypothesis Based Multi-task Practice for Video Recommendation System

Click-through rate prediction (CTR) and post-click conversion rate prediction (CVR) play key roles across all industrial ranking systems, such as recommendation systems, online advertising, and search engines. Different from the extensive research on CTR, there is much less research on CVR estimation, whose main challenge is extreme data sparsity with one or two orders of magnitude reduction in the number of samples than CTR. People try to solve this problem with the paradigm of multi-task learning with the sufficient samples of CTR, but the typical hard sharing method can't effectively solve this problem, because it is difficult to analyze which parts of network components can be shared and which parts are in conflict, i.e., there is a large inaccuracy with artificially designed neurons sharing. In this paper, we model CVR in a brand-new method by adopting the lottery-ticket-hypothesis-based sparse sharing multi-task learning, which can automatically and flexibly learn which neuron weights to be shared without artificial experience. Experiments on the dataset gathered from traffic logs of Tencent video's recommendation system demonstrate that sparse sharing in the CVR model significantly outperforms competitive methods. Due to the nature of weight sparsity in sparse sharing, it can also significantly reduce computational complexity and memory usage which are very important in the industrial recommendation system.Comment: 6 pages,4 figure