Telecommunication Systems - In a wireless sensor network (WSN) where positioning information is not assumed or is partially available, efficient data access is a very challenging issue especially... 相似文献
Organic-inorganic hybrid perovskite materials demonstrate promising applications in high-efficiency perovskite solar cells (PSCs) with a certified power conversion efficiency (PCE) of 25.5％ (https://www.nrel.gov/pv/cell-efficiency.html).However, intrinsically volatile and thermally unstable nature of the organic cations result in poor thermal stability of organic-inorganic hybrid perovskite materials, hampering the commercialization of organic-inorganic hybrid PSCs[1].All-inorganic CsPbl3-xBrx (x =0-3) perovskites have been attracting great attention in recent years because of their higher thermal stability[2].Among the reported CsPbl3-xBrx perovskites, CsPbl2Br bears a reasonable balance between bandgap and phase stability, thus becomes the most extensively studied material[3-15].Though there are many works aiming at achieving high-efficiency CsPbl2Br PSCs, improving the photostability of CsPbl2Br PSCs is another key for commercialization of all-inorganic PSCs.Intriguingly, it has been reported that CsPbl2Br is susceptible to make light-induced phase segregation, i.e.severe segregation of CsPbl2Br to low-bandgap I-rich and wide-bandgap Br-rich domains via ion diffusion, leading to obvious current-voltage hysteresis and decrease of stabilized power output (SPO)[16-20].Such a light-induced phase segregation can be suppressed by optimizing the interface between perovskite layer and charge-transport layer[18, 19]. 相似文献
The positioning technology based on receive signal strength (RSS) fingerprints has become one of the hottest research spots with its advantages of simple deployment, low cost, and single parameter. However, in the limited space, the multipath and shadowing, result in poor separability of the fingerprint data, and low accuracy of target localization. In this paper, a novel RSS fingerprints positioning algorithm that is based on fuzzy kernel clustering SVM is proposed to combat the multipath and shadowing effects. The first step of the proposed positioning algorithm is to use kernel function to map the traditional fingerprints sample data to high-dimensional feature space to generate fuzzy classes. The second step is to generate binary-class SVM of fuzzy class based on the relationship between classes and internal discrete information of each class. After that, we can use the binary fuzzy class SVM to dichotomize the classified fingerprints in the first step, and combine these dichotomous SVMs into a handstand classification binary tree. And thus, the proposed positioning algorithm achieves quick and accurate positioning. Experimental results show that the positioning accuracy and locating stability of proposed positioning algorithm are improved by 38.73% and 59.26%, respectively, compared with the traditional RSS fingerprints algorithm.
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