The Chinese lunar probe Chang'E-4 successfully landed in the Von Kármán crater on the far side of the Moon. This paper presents the topographic and geomorphological mapping and their joint analysis for selecting the Chang'E-4 landing site in the Von Kármán crater. A digital topographic model (<small>DTM</small>) of the Von Kármán crater, with a spatial resolution of 30 m, was generated through the integrated processing of Chang'E-2 images (7 m/pixel) and Lunar Reconnaissance Orbiter (<small>LRO</small>) Laser Altimeter (<small>LOLA</small>) data. Slope maps were derived from the <small>DTM</small>. Terrain occlusions to both the Sun and the relay satellite were studied. Craters with diameters ≥ 70 m were detected to generate a crater density map. Rocks with diameters ≥ 2 m were also extracted to generate a rock abundance map using an <small>LRO</small> narrow angle camera (<small>NAC</small>) image mosaic. The joint topographic and geomorphological analysis identified three subregions for landing. One of them, recommended as the highest-priority landing site, was the one in which Chang'E-4 eventually landed. After the successful landing of Chang'E-4, we immediately determined the precise location of the lander by the integrated processing of orbiter, descent and ground images. We also conducted a detailed analysis around the landing location. The results revealed that the Chang'E-4 lander has excellent visibility to the Sun and relay satellite; the lander is on a slope of about 4.5° towards the southwest, and the rock abundance around the landing location is almost 0. The developed methods and results can benefit future soft-landing missions to the Moon and other celestial bodies.

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Wireless intelligent health monitoring has been widely used in civil engineering. However, ill-conditioned data could be generated due to the vulnerability to external interference sometimes. The ill-conditioned data have great influence on damage identification and condition assessment. Hence, the prediction method of monitoring data based on data correlation was presented in this paper. The correlation degree between multi-channels data was established by BP neural network. Then the ill-conditioned data was predicted and corrected by the correlation degree between the data, and verified by the measured data. The results indicated that high accuracy and engineering requirement could be achieved using BP neural network prediction method considering the correlation degree between multi-channels data.

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