A test procedure optimization method was proposed in this paper to improve the test efficiency of the industrial robot servo system (IRSS) to be tested on the IRSS integrated testing platform (IITP). First, an ordered sequence was used to define the IRSS test project when tested on the IITP. The ordered sequence consisted of execution subelements, which were a combination of control variable parameters of the IITP. Second, the optimization relationships among the IRSS test projects were dug out according to the IRSS test project sequences. Finally, by using the traveling salesmen problem (TSP), an optimization model was established based on the optimization relationships of the IRSS test projects, and the optimal test order of the IRSS test projects to be tested on the IITP was obtained by solving the model. A case analysis showed that the proposed method optimized the test procedure on the IITP effectively, and the test time when implementing the test on the IITP according to the optimized order was nearly 50% shorter than before optimization. The optimization effect was thus found to be significant.
With the advances in genetic sequencing technology, the automated assignment of protein function has become a key challenge in bioinformatics and computational biology. In nature, many kinds of proteins consist of a variety of structural domains, and each domain almost holds its own function independently or implements a new function in cooperation with neighbors. Thus, a multi-domain protein function prediction problem can be converted into multi-instance multi-label (MIML) learning tasks. In this paper, we propose a novel ensemble MIML algorithm called multi-instance multi-label randomized clustering forest (MIMLRC-Forest) for protein function prediction. In MIMLRC-Forest, we develop a set of hierarchical clustering trees and conduct a label transfer mechanism to identify the relevant function labels in learning process. The clustering tree with a hierarchical structure can handle the multi-label problem by exploiting more discriminable label concepts at higher-level nodes and by transferring less discriminable labels into the lower-level nodes. Then, the label dependency can be computed by aggregating tree labels for protein function prediction. Extensive experiments on five real-world protein data sets show the effectiveness of the proposed algorithm compared with several state-of-the-art baselines, including MIMLSVM, MIMLNN, MIML-kNN, EnMIMLNN, and M3MIML.
A reliability evaluation method of pseudo-failure life distribution for Industrial Robot Servo System (IRSS) based on The Generalized Lambda Distribution (GLD) was proposed in the paper. The IRSS pseudo-failure life GLD distribution was established. Quantile estimation method was applied to estimate the GLD parameters. The proposed method had no need of presetting specific distribution morphology, and thus improve the accuracy of IRSS pseudo-failure reliability modeling. In the application experiment, the IRSS pseudo-failure life distribution reliability evaluation method based on GLD was used to obtain the IRSS failure life, the reliability evaluation result was consistent with the design failure life, which indicated the validity of the proposed method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.