Accuracy improvement of laser tracker registration based on enhanced reference system points weighting self-calibration and thermal compensation.

Improving the accuracy of large measurement systems consisting of multiple laser trackers and Enhanced Reference System (ERS) points is technically challenging. In practice, standard devices with precise distance limits are often used to improve the registration accuracy of laser trackers. However, these standard devices are expensive and need to be calibrated by the Coordinate Measuring Machine (CMM). In addition, the stability of ERS points can significantly affect registration errors. Therefore, this paper proposes a laser tracker registration method based on ERS point-weighted self-calibration and thermal deformation compensation. First, a self-calibration method for simple standard devices based on multilateration measurements is presented, which only utilizes large measurement systems without additional high-precision measurement instruments. Based on this, a weighted registration optimization algorithm for the registration process of a relocation laser tracker is proposed. Then, the position errors of ERS points caused by temperature changes are calculated and compensated based on the thermal deformation coefficient of large structural components. The compensated ERS points are used for the registration of the laser trackers. Finally, the effectiveness of the proposed method is demonstrated by a field measurement experiment on a large spherical shell. Compared with the most widely used benchmark method, the proposed method reduces the average registration error of all ERS points from 0.103 to 0.02 mm.

Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy.

Identification of drug targets and mechanism of action (MoA) for new and uncharacterized anticancer drugs is important for optimization of treatment efficacy. Current MoA prediction largely relies on prior information including side effects, therapeutic indication, and chemoinformatics. Such information is not transferable or applicable for newly identified, previously uncharacterized small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary toward development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose here a new integrative computational pharmacogenomic approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that rely only on basic drug characteristics toward elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation, and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. DNF taxonomy succeeded in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. The scalability of DNF facilitated identification of key drug relationships across different drug categories, providing a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on compound MoA and potential insights for drug repurposing. Cancer Res; 77(11); 3057-69. ©2017 AACR.