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Accurate real-time displacement field reconstruction based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a data-driven displacement field reconstruction method called stacked convolutional autoencoder with denoising autoencoder and filter. Precise reconstruction of the structural displacement from a small number of local strains was made possible by the two primary components of the method: low-resolution displacement field reconstruction and result optimization. Given the significant imbalance between the limited strain information input and the structural displacement field output, a deep learning model with multiple deconvolution layers was built in the low-resolution displacement field reconstruction part using the layer-wise training property of a stacked autoencoder and the sparse mapping property of a convolutional neural network. The result optimization part utilized a denoising autoencoder and a linear density filter to effectively alleviate the checkerboard phenomenon and displacement field discontinuity caused by the deconvolution operation. The results of the case study indicate that the proposed method can accurately reconstruct the structural displacement field of both simple regular geometric structures and irregular geometric structures with complex boundaries without prior information. Additionally, the method exhibits excellent robustness to unavoidable measurement noise, providing a new implementation approach for real-time monitoring of spacecraft. [ABSTRACT FROM AUTHOR]

Accurate and rapid thermal load identification based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a stepwise identification method based on deep learning for identifying structural thermal loads that efficiently map the local responses and overall thermal load of a box structure. To determine the location and magnitude of the thermal load accurately, the proposed method segments a structure into several subregions and applies a cascade of deep learning models to gradually reduce the solution domain. The generalization ability of the model is significantly enhanced by the inclusion of boundary conditions in the deep learning models. In this study, a large simulated dataset was generated by varying the load application position and intensity for each sample. The input variables encompass a small set of structural displacements, while the outputs include parameters related to the thermal load, such as the position and magnitude of the load. Ablation experiments are conducted to validate the effectiveness of this approach. The results show that this method reduces the identification error of the thermal load parameters by more than 45% compared with a single deep learning network. The proposed method holds promise for optimizing the design and analysis of spacecraft structures, contributing to improved performance and reliability in future space missions. [ABSTRACT FROM AUTHOR]

Objective: To analyze the genetic data of 30 insertion and deletion polymorphisms (InDel) loci included in an InvestigatorR DIPplex diagnostic kit, and to evaluate the forensic application in ethnic Tibetan population from China., Methods: By detecting 226 unrelated individuals with the Investigator(R) DIPplex kit, allelic frequencies and population genetics parameters of the 30 InDels were statistically analyzed and compared with available data derived from other populations from various regions., Results: After the Bonferroni correction at a 95% significance level (P=0.0017), no significant departures from the Hardy-Weinberg equilibrium were observed except for the HLD114 locus. Linkage disequilibrium test showed no significant allelic association between all 30 loci after the Bonferroni's correction. The average heterozygosity (Ho) of all loci was 0.4125, the mean discrimination power (DP) was 0.5618, the mean polymorphism information content (PIC) was 0.3280, and the combined discrimination power (TDP) was 0.999999999990. The combined power of exclusion of all loci was 0.987 849 91 in trio cases and 0.94977125 in duo cases. Genetic distance between Tibetan and Han from Beijing was minimum (0.0068) in the 5 populations, while genetic distance between Tibetan and Uygur was maximal (0.0215)., Conclusion: Multiplex detection has revealed that these 30 InDel loci have a moderate distribution of genetic polymorphism among ethnic Tibetan group residing in Tibet, China.