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A deep extraterrestrial drilling mission potentially adds a new level of complexity, and it is crucial to understand the associated challenges. To cope with China'sChang'E 5 mission to return subsurface regolith samples from the Moon, a series of laboratory tests were performed to validate the lunar robotic drilling from 2015 to 2018. The tests took place in a simulated thermal-vacuum regolith environment, a highly relevant lunar analog site. Force and temperature sensors were integrated into a 2-m class dry auger coring drill to assess the mechanical and thermophysical status of the sampling process. The operation of the entire testing system was automated, covering regolith penetration and data collection and storage. The science team used sensing data to characterize the subsurface geologic strata, assess the conditions of the drilling robot, and direct the sampling strategy. This experiment represents an essential first step in understanding the technology and operational requirements for lunar drilling and sampling mission. Many laboratory tests have helped guide the design and implementation of the highly successful lunar regolith-sampling task in the Chang'E 5 mission. This paper documents the experimental system design, highlighting some critical design criteria and design tradeoffs. It also discusses the results of laboratory testing and lists some of the key technological lessons learned.

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Dynamic Concept-cognitive Learning (CCL) is an active field in cognitive computing. Decremented concept cognition is an important topic in dynamic CCL. As an important feature of the dynamic CCL, attenuation characteristics have been successfully visualized by concept lattice and three-dimensional attribute topology. However, the existing attenuation characteristic analysis method has limitations to the description of interaction between attributes. A method of attenuation characteristics analysis of concept tree is proposed. The coupling between nodes is discussed from the concept tree, the nodes are decremented according to the coupling relationship, and the corresponding node attenuation rules are discussed according to the different types of nodes. In this paper, the news attention is the research object. The experimental results show that the attenuation characteristic analysis scheme of the concept tree is feasible. In the process of attenuation, the effect of attribute attenuation on the concept structure can be clearly demonstrated. At the same time, the concept tree can better visualize the process of decremented news attention than the concept lattice and three-dimensional attribute topology.

In Compressive Sensing MRI (CS-MRI), measurement matrix learning has been developed as a promising method for measurement matrix designing. Research on MRI measurement task suggests that Relative 2-Norm Error (RLNE) of measurement images is imbalanced. However, current learning-based investigations suffer from the lack of probing imbalanced characteristic on measurement matrix learning. In this paper, we propose a novel Measurement Matrix Learning via Correlation Reweighting (MML-CR) approach for exploring and solving this problem by optimizing reweighted model. Specifically, we introduce a reweighting expected minimization model to obtain an essential measurement matrix in k-space. Besides, we propose an example correlation regularizer to prevent trivial solution for learning weights. Furthermore, we present an alternating solution and perform convergence analysis for the optimization. We also demonstrate quantitative and qualitative experimental results which show that our algorithm outperforms several state-of-art measurements methods. Compared with conventional methods, MML-CR achieves better performance on universal task. CCS CONCEPTS • Computing methodologies → Reconstruction.