Person re-identification (ReID) is one of the commonly used criminal investigation methods in reconnaissance. Although the current ReID has achieved robust results on single domains, the focus of researches has shifted to cross-domain in recent years, which is caused by domain bias between different datasets. Generative Adversarial Networks (GAN) is used to realize the image style transfer of different datasets to alleviate the effect of cross-domain [9], [13]. However, the existing GAN-based models ignore complete expressions and occlusion of pedestrian characteristics, resulting in low accuracy in feature extraction [12], [22]. To address these issues, we introduce a cross domain model based on feature fusion (FFGAN) to fuse global, local and semantic features to extract more delicate pedestrian features. Before extracting pedestrian features, we preprocess feature maps with a feature erasure block to solve an occlusion issue. Finally, FFGAN enables a more complete visual description of pedestrian characteristics, thereby improving the accuracy of FFGAN in identifying pedestrians. Experimental results show that the effect of FFGAN is significantly improved compared with some advanced cross-domain ReID algorithms.
A green energy certificate transaction, as one of the ways to meet China’s renewable energy quota system, is a virtual transaction through the renewable energy trading system. Different from the market mechanism of only one transaction in the past audit cycle, this paper establishes an agent-based renewable energy system with multi-period trading according to the existing green energy certificate trading policy. With simulation of a period of audit of green spot multi-period transactions, the agent of limited rationality makes it in the pursuit of their utility under the premise of interaction. The experiment observes the utility level of agents under different trading strategies, verifies the influence of different policy behaviors on the market, and obtains the market equilibrium state. The conclusion shows that there is a serious green certificate premium in the early stage of the market. After adjustment by fines, the green certificate price of 0.05-0.08 yuan can maximize social welfare and stimulate the enthusiasm for individual purchases.
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