Bayesian method for system reliability assessment of overlapping pass/fail data

For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve the precision of the system reliability assessment. If the multi-level pass/fail data are overlapping,one challenging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps： fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and finally simplifying the likelihood function. An example of a satellite rolling control system demonstrates the feasibility and the efficiency of the proposed approach.

Similarity measure design on overlapped and non-overlapped data

Similarity measure design on non-overlapped data was carried out and compared with the case of overlapped data.Unconsistant feature of similarity on overlapped data to non-overlapped data was provided by example.By the artificial data illustration,it was proved that the conventional similarity measure was not proper to calculate the similarity measure of the non-overlapped case.To overcome the unbalance problem,similarity measure on non-overlapped data was obtained by considering neighbor information.Hence,different approaches to design similarity measure were proposed and proved by consideration of neighbor information.With the example of artificial data,similarity measure calculation was carried out.Similarity measure extension to intuitionistic fuzzy sets(IFSs)containing uncertainty named hesitance was also followed.

Low Data Overlab Rate Graph-Based SLAM with Distributed Submap Strategy

Simultaneous localization and mapping(SLAM)is widely used in many robot applications to acquire the unknown environment's map and the robots location.Graph-based SLAM is demonstrated to be effective in large-scale scenarios,and it intuitively performs the SLAM as a pose graph.But because of the high data overlap rate,traditional graph-based SLAM is not efficient in some respects,such as real time performance and memory usage.To reduce1 data overlap rate,a graph-based SLAM with distributed submap strategy(DSS)is presented.In its front-end,submap based scan matching is processed and loop closing detection is conducted.Moreover in its back-end,pose graph is updated for global optimization and submap merging.From a series of experiments,it is demonstrated that graph-based SLAM with DSS reduces 51.79%data overlap rate,decreases 39.70%runtime and 24.60%memory usage.The advantages over other low overlap rate method is also proved in runtime,memory usage,accuracy and robustness performance.

Fast bowtie effect elimination for MODIS L1B data

The bowtie effect refers to geometry distortions for the moderate resolution imaging spectrum-radiometer （MODIS） level 1B （L1 B） data. Till now, to eliminate the bowtie effect, numerous methods are proposed. However, most of them have limitations in computation efficiency. Through a comparative study of existing methods, this article puts forward a fast method to eliminate the bowtie effect using the ephemeris data. In this method, the rough positions of overlapping data are first detected. Because of the influence caused by the instrarnent characters and the earth＇s curvature, the positions of overlapping data need to be rectified to obtain more precise results. The optimal rectification method used in this article is selected by comparing three methods. By using the optimal method, the rectified MODIS data can be obtained. The experiments demonstrate that the bowtie effect can be eliminated in less than 1 s. In contrast, other traditional methods spend at least 3 s, thus the proposed method is faster and more effective.