16 research outputs found
Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators
When robots operate in unknown environments small errors in postions can lead
to large variations in the contact forces, especially with typical
high-impedance designs. This can potentially damage the surroundings and/or the
robot. Series elastic actuators (SEAs) are a popular way to reduce the output
impedance of a robotic arm to improve control authority over the force exerted
on the environment. However this increased control over forces with lower
impedance comes at the cost of lower positioning precision and bandwidth. This
article examines the use of an iteratively-learned feedforward command to
improve position tracking when using SEAs. Over each iteration, the output
responses of the system to the quantized inputs are used to estimate a
linearized local system models. These estimated models are obtained using a
complex-valued Gaussian Process Regression (cGPR) technique and then, used to
generate a new feedforward input command based on the previous iteration's
error. This article illustrates this iterative machine learning (IML) technique
for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful
convergence of the IML approach to reduce the tracking error.Comment: 9 pages, 16 figure. Submitted to AMC Worksho
Individually-controllable magnetic artificial cilia for microfluidic manipulation tasks
Thesis (Ph.D.)--University of Washington, 2017-06This thesis presents the design, modeling, and control of a magnetic artificial cilia system in which the cilia are individually controllable. In nature, cilia exhibit metachronal waves, or a phase difference between adjacent cilia that results in a traveling wave, which may improve pumping performance or efficiency of biological cilia. However, existing magnetic artificial cilia devices typically use actuation by a rotating field generated by Helmholtz coils or by a rotating permanent magnet. These field sources cannot apply a phase shift to the cilia array and therefore cannot generate a metachronal wave. Nevertheless, magnetic actuation remains desirable for cilia devices as it allows for biocompatibility, precise control of sys- tem inputs, and low-cost fabrication of the cilia. In this thesis, a new design for magnetic artificial cilia is presented in which the actuating magnetic field is localized, enabling indi- vidual actuation. However, this design decision leads to challenging research problems in input-pattern identification, nonlinear systems modeling, and control. In addressing these challenges, the contributions of this thesis are to (i) demonstrate that individual control can improve performance in cilia-based devices, (ii) present accurate nonlinear models for pre- dicting the static response, and (iii) develop a machine-learning-based system identification and control strategy for output tracking
A Method to Account for Variation in Congenital Heart Surgery Length of Stay
Objectives: We sought to develop a risk-adjustment methodology for length of stay in congenital heart surgery, as none exist.
Design: Prospective cohort analysis combined with previously obtained retrospective cohort analysis of a Department of Cardiovascular Surgery clinical database.
Patients: Patients discharged from Boston Children's Hospital between October 1, 2006, and May 31, 2014, that underwent a congenital heart surgery procedure(s) linked to one of 103 surgical procedure types.
Measurements and Main Results: Six thousand two hundred nine discharges during the reporting period at Boston Children's Hospital comprised the cohort. Seven Surgical Length Categories were developed to group surgical procedure types. A multivariable model for outcome length of stay was built using a derivation cohort consisting of a 75% random sample, starting with Surgical Length Categories and considering additional a priori factors. Postoperative factors were then added to improve predictive performance. The remaining 25% of the cohort was used to validate the multivariable models. The coefficient of determination (R-2) was used to estimate the variability in length of stay explained by each factor. The Surgical Length Categories yielded an R-2 of 42%. Model performance increased when the a priori factors preoperative status, noncardiac abnormality, genetic anomaly, preoperative catheterization during episode of care, weight less than 3 kg, and preoperative vasoactive support medication were introduced to the model (R-2 = 60.8%). Model performance further improved when postoperative ventilation greater than 7 days, operating room time, postoperative catheterization during episode of care, postoperative reintubation, number of postoperative vasoactive support medications, postoperative ICU infection, and greater than or equal to one secondary surgical procedure were added (R-2 = 76.7%). The validation cohort yielded an R-2 of 76.5%.
Conclusions: We developed a statistically valid procedure-based categorical variable and multivariable model for length of stay of congenital heart surgeries. The Surgical Length Categories and important a priori and postoperative factors may be used to pursue a predictive tool for length of stay to inform scheduling and bed management practices
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Intravascularly infused extracellular matrix as a biomaterial for targeting and treating inflamed tissues
Decellularized extracellular matrix in the form of patches and locally injected hydrogels has long been used as therapies in animal models of disease. Here we report the safety and feasibility of an intravascularly infused extracellular matrix as a biomaterial for the repair of tissue in animal models of acute myocardial infarction, traumatic brain injury and pulmonary arterial hypertension. The biomaterial consists of decellularized, enzymatically digested and fractionated ventricular myocardium, localizes to injured tissues by binding to leaky microvasculature, and is largely degraded in about 3 d. In rats and pigs with induced acute myocardial infarction followed by intracoronary infusion of the biomaterial, we observed substantially reduced left ventricular volumes and improved wall-motion scores, as well as differential expression of genes associated with tissue repair and inflammation. Delivering pro-healing extracellular matrix by intravascular infusion post injury may provide translational advantages for the healing of inflamed tissues 'from the inside out'
Serum amyloid A induces G-CSF expression and neutrophilia via Toll-like receptor 2
The acute-phase protein serum amyloid A (SAA) is commonly considered a marker for inflammatory diseases; however, its precise role in inflammation and infection, which often result in neutrophilia, remains ambiguous. In this study, we demonstrate that SAA is a potent endogenous stimulator of granulocyte colony-stimulated factor (G-CSF), a principal cytokine-regulating granulocytosis. This effect of SAA is dependent on Toll-like receptor 2 (TLR2). Our data demonstrate that, in mouse macrophages, both G-CSF mRNA and protein were significantly increased after SAA stimulation. The induction of G-CSF was blocked by an anti-TLR2 antibody and markedly decreased in the TLR2-deficient macrophages. SAA stimulation results in the activation of nuclear factor–κB and binding activity to the CK-1 element of the G-CSF promoter region. In vitro reconstitution experiments also support that TLR2 mediates SAA-induced G-CSF expression. In addition, SAA-induced secretion of G-CSF was sensitive to heat and proteinase K treatment, yet insensitive to polymyxin B treatment, indicating that the induction is a direct effect of SAA. Finally, our in vivo studies confirmed that SAA treatment results in a significant increase in plasma G-CSF and neutrophilia, whereas these responses are ablated in G-CSF– or TLR2-deficient mice