The crucial issue in specific emitter identification (SEI) is the extraction of fingerprint features which can represent the differences among individual emitters of the same type. Considering that these emitters have the same intentional modulation on pulse, the fingerprint features originated from the unintentional modulation on pulse are extremely imperceptible and less detectable. However, existing feature extractions, either traditional handcrafted ones or deep learning based ones, have failed to ensure that their extracted features are rich in the unintentional modulation information (UMI) and not interfered by the intentional modulation information (IMI). To adequately take advantage of deep learning to address SEI, this Letter proposes a novel neural networks, named discriminative adversarial networks (DAN). By demarcating a clear boundary between IMI and UMI, DAN isolates IMI and thus reduces the burden of UMI mining during its feature extraction process. Experimental results demonstrate that DAN outperforms most methods in the literature.
Dempster–Shafer (D–S) evidence theory is more and more extensively applied in multi-sensor data fusion. However, it is still an open issue that how to effectively combine highly conflicting evidence in D–S evidence theory. In this article, a novel divergence measure, called pignistic probability transformation divergence, is proposed to measure the difference between evidences. The proposed pignistic probability transformation divergence can reflect the interaction between single-element and multi-element subsets by introducing the pignistic probability transformation, and satisfies the properties of boundedness, non-degeneracy, and symmetry. Moreover, the pignistic probability transformation divergence can degenerate as Jensen–Shannon divergence when mass function and the probability distribution are consistent. Based on the pignistic probability transformation divergence, a new multi-sensor data fusion method is presented. The proposed method takes advantage of pignistic probability transformation divergence to measure the discrepancy between evidences in order to obtain the credibility weights, and belief entropy to measure the uncertainty of the evidences in order to obtain the information volume weights, which can fully mine the potential information between evidences. Then, the credibility weights and the information volume weights are integrated to generate an appropriate weighted average evidence before using Dempster’s combination rule. The results of two application cases illustrate that the proposed method outperforms other related methods for combining highly conflicting evidences.
N-heterocyclic carbene and related complexes are a class of catalysts with excellent catalytic activity which are widely used in the chemical fixation of carbon dioxide. This review focuses on the application of a carbene-metal catalytic system, carbene-free-metal catalytic system and synergistic catalytic technology of various technologies in the conversion of carbon dioxide. The possible role of carbene materials in chemical carbon fixation is discussed by analyzing several types of typical chemical carbon fixation mechanisms.
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