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Summary: Indoor location based on Wi‐Fi fingerprint has attracted extensive attention recently, since it only requires a mobile device and existing network; it does not require additional infrastructure and hardware. However, localization using only received signal strength fingerprint is susceptible to dynamic environments and device heterogeneity. Localization using signal strength differences fingerprint and hyperbolic location fingerprint which extracted from received signal strength fingerprint is a powerful measure to overcome the device heterogeneity. Existing fusion‐based methods do not fully use of the mutual fusion function of multiple fingerprints, which leads to low positioning accuracy. In order to further improve the positioning accuracy, we propose a multifingerprint multiclassifier and two‐layer fusion weight positioning model. The proposed approach first creates a multiple fingerprints group by gleaning signal strength differences fingerprint and hyperbolic location fingerprint from received signal strength fingerprint. In order to deal with the accuracy decline caused by a single classifier in complex scenes, three typical classifiers, random forest (RF), K‐nearest neighbor (KNN), and support vector machine (SVM), are adopted, and a fusion weight selection algorithm is proposed to improve the localization accuracy by choosing fusion weights intelligently from the two‐layer fusion weights. Experiment results show that the proposed scheme is effective to resolve device heterogeneity, and the position accuracy is improved about 16.9% and 5.8%, respectively, compared with single fingerprint and classifier schemes. The results show that, compared with the single fingerprint scheme, the position accuracy is improved at least 16.9% and 28.4% for the homogeneous and heterogeneous equipments, respectively; as for the proposed multiclassifier fusion method, it achieves at least 5.8% and 8.7% accuracy improvement for the cases with homogeneous and heterogeneous equipments, respectively, compared with other traditional classifiers such as RF, KNN, and SVM. [ABSTRACT FROM AUTHOR]

In recent years, the development of new scintillation materials with red and near-infrared light emission has had significant advantages based on the development of an avalanche photodiode with high quantum yield. This study used a hydrothermal method to synthesize rare-earth-doped Cs2NaScCl6 crystals, in which a red emission of Cs2NaScCl6:Sm was investigated with high X-ray sensitivity and low detection limit. In addition, a tunable wavelength range of radioluminescence can be achieved, in which a near-white light emission can be generated by precisely controlling the proportion of Sm3+ and Tb3+ doping concentrations. Meanwhile, a Cs2NaScCl6:7%Sm scintillation film has been successfully prepared by direct coating with a thickness of 150 μm, which achieves a spatial resolution of 11.9 lp mm−1 based on X-ray imaging experiments. Therefore, this perovskite material has shown potential application as an eco-friendly multifunctional imaging material. [ABSTRACT FROM AUTHOR]

In order to reduce the gear meshing impact,the method about how to determinate the three factors of gear modification is studied and the optimization of modification curve is discussed. The 3D meshing model of standard involute gear is set up,the finite element contact analysis is carried out. Regarding the integrated elastic deformation of the meshing tooth as the modification value of the gear pair,the contact stress analysis results between long-distance modification and short-distance modification are compared,it is found that the short-distance modification has a better effect. The quadric curve and the sine curve have their virtues and their faults,so combining them into a new curve and the modification effect and feature are discussed. Research shows that,the sudden change of stress with a lower impaction of tooth mesh in and out is eliminated by the new modification method. A new modification curve is provided for involute gear.