High-performance control of dual-inertia servo-drive systems using low-cost integrated SAW torque transducers

Abstract—This paper provides a systematic comparative study of compensation schemes for the coordinated motion control of two-inertia mechanical systems. Specifically, classical proportional–integral (PI), proportional–integral–derivative (PID), and resonance ratio control (RRC) are considered, with an enhanced structure based on RRC, termed RRC+, being proposed. Motor-side and load-side dynamics for each control structure are identified, with the “integral of time multiplied by absolute error” performance index being employed as a benchmark metric. PID and RRC control schemes are shown to be identical from a closed-loop perspective, albeit employing different feedback sensing mechanisms. A qualitative study of the practical effects of employing each methodology shows that RRC-type structures provide preferred solutions if low-cost high-performance torque transducers can be employed, for instance, those based on surface acoustic wave tecnologies. Moreover, the extra degree of freedom afforded by both PID and RRC, as compared with the basic PI, is shown to be sufficient to simultaneously induce optimal closed-loop performance and independent selection of virtual inertia ratio. Furthermore, the proposed RRC+ scheme is subsequently shown to additionally facilitate independent assignment of closed-loop bandwidth. Summary attributes of the investigation are validated by both simulation studies and by realization of the methodologies for control of a custom-designed two-inertia system

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel

A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved