161 research outputs found
Decoupled Reference Governors for Multi-Input Multi-Output Systems
In this work, a computationally efficient solution for constraint management of square
multi-input multi-output (MIMO) systems is presented. The solution, referred to as
the Decoupled Reference Governor (DRG), maintains the highly-attractive computational
features of scalar reference governors (SRG) compared to Vector Reference
Governor (VRG) and Command Governor (CG). This work focuses on square MIMO
systems that already achieve the desired tracking performance. The goal of DRG is to
enforce output constraints and simultaneously ensure that the degradation to tracking
performance is minimal. DRG is based on decoupling the input-output dynamics
of the system so that every channel of the system can be viewed as an independent
input-output relationship, followed by the deployment of a bank of scalar reference
governors for each decoupled channel. We present a detailed set-theoretic analysis of
DRG, which highlights its main characteristics. A quantitative comparison between
DRG, SRG, and the VRG is also presented in order to illustrate the computational
advantages of DRG. Finally, a distillation process is introduced as an example to
illustrate the applicability of DRG
Reliability -aware optimal checkpoint /restart model in high performance computing
Computational power demand for large challenging problems has increasingly driven the physical size of High Performance Computing (HPC) systems. As the system gets larger, it requires more and more components (processor, memory, disk, switch, power supply and so on). Thus, challenges arise in handling reliability of such large-scale systems. In order to minimize the performance loss due to unexpected failures, fault tolerant mechanisms are vital to sustain computational power in such environment. Checkpoint/restart is a common fault tolerant technique which has been widely applied in the single computer system. However, checkpointing in a large-scale HPC environment is much more challenging due to complexity, coordination, and timing issues. In this dissertation, we present a reliability-aware method for an optimal checkpoint/restart strategy. Our scheme aims to address the fault tolerance challenge, especially in a large-scale HPC system, by providing optimal checkpoint placement techniques derived from the actual system reliability. Unlike existing checkpoint models, which can only handle Poisson failure and a constant checkpoint interval, our model can perform a varying checkpoint interval and deal with different failure distributions. In addition, the approach considers optimality for both checkpoint overhead and rollback time. Our validation results suggest a significant improvement over existing techniques
Accurate Multi-physics Numerical Analysis of Particle Preconcentration Based on Ion Concentration Polarization
This paper studies mechanism of preconcentration of charged particles in a
straight micro-channel embedded with permselective membranes, by numerically
solving coupled transport equations of ions, charged particles and solvent
fluid without any simplifying assumptions. It is demonstrated that trapping and
preconcentration of charged particles are determined by the interplay between
drag force from the electroosmotic fluid flow and the electrophoretic force
applied trough the electric field. Several insightful characteristics are
revealed, including the diverse dynamics of co-ions and counter ions,
replacement of co-ions by focused particles, lowered ion concentrations in
particle enriched zone, and enhanced electroosmotic pumping effect etc.
Conditions for particles that may be concentrated are identified in terms of
charges, sizes and electrophoretic mobilities of particles and co-ions.
Dependences of enrichment factor on cross-membrane voltage, initial particle
concentration and buffer ion concentrations are analyzed and the underlying
reasons are elaborated. Finally, post priori a condition for validity of
decoupled simulation model is given based on charges carried by focused charge
particles and that by buffer co-ions. These results provide important guidance
in the design and optimization of nanofluidic preconcentration and other
related devices.Comment: 18 pages, 11 firgure
Limit theorems for functionals of long memory linear processes with infinite variance
Let be a long memory linear process in which the
coefficients are regularly varying and innovations are independent and
identically distributed and belong to the domain of attraction of an
-stable law with . Then, for any integrable and
square integrable function on , under certain mild conditions,
we establish the asymptotic distributions of the partial sum as tends to
infinity
Validation of recommended definition in identifying elevated blood pressure in adolescents
Recently, the American Academy of Pediatrics (AAP) recommended 120/80 mm Hg as thresholds for identifying elevated blood pressure (BP) in adolescents aged 13‐17 years. The authors aimed to compare the performance of the new definition in identifying elevated BP with traditional percentile‐based definition. Data were obtained from the National Health and Nutrition Examination Survey 1999‐2014, which included 7485 adolescents aged 13‐17 years. Elevated BP was defined using the recommended (≥120/80 mm Hg) and traditional definition (≥90th percentile for sex, age, and height or 120/80 mm Hg) presented in AAP guideline. The prevalence of elevated BP was 15.7% and 17.2% using the recommended and traditional definition, respectively (P < .001). The recommended definition had high sensitivity (90.9%), perfect specificity (100.0%), perfect positive predictive value (100.0%), and very high negative predictive value (98.1%) compared with the traditional definition. The Kappa correlation coefficient between two definitions was 0.94 (P < .001). Similar results can be observed in subgroups across sex, age, and sex‐ and age‐specific height percentile except for both sexes with young age and low height percentile. Generally, our results supported the use of the recommended definition for identifying elevated BP in adolescents.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151868/1/jch13640.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151868/2/jch13640_am.pd
Reference Governors for MIMO Systems and Preview Control: Theory, Algorithms, and Practical Applications
The Reference Governor (RG) is a methodology based on predictive control for constraint management of pre-stablized closed-loop systems. This problem is motivated by the fact that control systems are usually subject to physical restrictions, hardware protection, and safety and efficiency considerations. The goal of RG is to optimize the tracking performance while ensuring that the constraints are satisfied. Due to structural limitations of RG, however, these requirements are difficult to meet for Multi-Input Multi-Output (MIMO) systems or systems with preview information. Hence, in this dissertation, three extensions of RG for constraint management of these classes of systems are developed. The first approach aims to solve constraint management problem for linear MIMO systems based on decoupling the input-output dynamics, followed by the deployment of a bank of RGs for each decoupled channel, namely Decoupled Reference Governor (DRG). This idea was originally developed in my previous work based on transfer function decoupling, namely DRG-tf. This dissertation improves the design of DRG-tf, analyzes the transient performance of DRG-tf, and extends the DRG formula to state space representations. The second scheme, which is called Preview Reference Governor, extends the applicability of RG to systems incorporated with the preview information of the reference and disturbance signals. The third subject focuses on enforcing constraints on nonlinear MIMO systems. To achieve this goal, three different methods are established. In the first approach, which is referred to as the Nonlinear Decoupled Reference Governor (NL-DRG), instead of employing the Maximal Admissible set and using the decoupling methods as the DRG does, numerical simulations are used to compute the constraint-admissible setpoints. Given the extensive numerical simulations required to implement NL-DRG, the second approach, namely Modified RG (M-RG), is proposed to reduce the computational burden of NL-DRG. This solution consists of the sequential application of different RGs based on linear prediction models, each robustified to account for the worst-case linearization error as well as coupling behavior. Due to this robustification, however, M-RG may lead to a conservative response. To lower the computation time of NL-DRG while improving the performance of M-RG, the third approach, which is referred to as Neural Network DRG (NN-DRG), is proposed. The main idea behinds NN-DRG is to approximate the input-output mapping of NL-DRG with a well-trained NN model. Afterwards, a Quadratic Program is solved to augment the results of NN such that the constraints are satisfied at the next timestep. Additionally, motivated by the broad utilization of quadcopter drones and the necessity to impose constraints on the angles and angle rates of drones, the simulation and experimental results of the proposed nonlinear RG-based methods on a real quadcopter are demonstrated
Intrapancreatic Ganglia and Neural Regulation of Pancreatic Endocrine Secretion
Extrapancreatic nerves project to pancreatic islets directly or converge onto intrapancreatic ganglia. Intrapancreatic ganglia constitute a complex information-processing center that contains various neurotransmitters and forms an endogenous neural network. Both intrapancreatic ganglia and extrapancreatic nerves have an important influence on pancreatic endocrine function. This review introduces the histomorphology, innervation, neurochemistry, and electrophysiological properties of intrapancreatic ganglia/neurons, and summarizes the modulatory effects of intrapancreatic ganglia and extrapancreatic nerves on endocrine function
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