22 research outputs found
Planetary Scale Data Storage
The success of virtualization and container-based application deployment has fundamentally changed computing infrastructure from dedicated hardware provisioning to on-demand, shared clouds of computational resources. One of the most interesting effects of this shift is the opportunity to localize applications in multiple geographies and support mobile users around the globe. With relatively few steps, an application and its data systems can be deployed and scaled across continents and oceans, leveraging the existing data centers of much larger cloud providers.
The novelty and ease of a global computing context means that we are closer to the advent of an Oceanstore, an Internet-like revolution in personalized, persistent data that securely travels with its users. At a global scale, however, data systems suffer from physical limitations that significantly impact its consistency and performance. Even with modern telecommunications technology, the latency in communication from Brazil to Japan results in noticeable synchronization delays that violate user expectations. Moreover, the required scale of such systems means that failure is routine.
To address these issues, we explore consistency in the implementation of distributed logs, key/value databases and file systems that are replicated across wide areas. At the core of our system is hierarchical consensus, a geographically-distributed consensus algorithm that provides strong consistency, fault tolerance, durability, and adaptability to varying user access patterns. Using hierarchical consensus as a backbone, we further extend our system from data centers to edge regions using federated consistency, an adaptive consistency model that gives satellite replicas high availability at a stronger global consistency than existing weak consistency models.
In a deployment of 105 replicas in 15 geographic regions across 5 continents, we show that our implementation provides high throughput, strong consistency, and resiliency in the face of failure. From our experimental validation, we conclude that planetary-scale data storage systems can be implemented algorithmically without sacrificing consistency or performance
Real-time Optimal Energy Management System for Plug-in Hybrid Electric Vehicles
Air pollution and rising fuel costs are becoming increasingly important concerns for the transportation industry. Hybrid electric vehicles (HEVs) are seen as a solution to these problems as they off er lower emissions and better fuel economy compared to conventional internal combustion engine vehicles. A typical HEV powertrain consists of an internal combustion engine, an electric motor/generator, and a power storage device (usually a battery). Another type of HEV is the plug-in hybrid electric vehicle (PHEV), which is conceptually similar to the fully electric vehicle. The battery in a PHEV is designed to be fully charged using a conventional home electric plug or a charging station. As such, the vehicle can travel further in full-electric mode, which greatly improves the fuel economy of PHEVs compared to HEVs.
In this study, an optimal energy management system (EMS) for a PHEV is designed to minimize fuel consumption by considering engine emissions reduction. This is achieved by using the model predictive control (MPC) approach. MPC is an optimal model-based approach that can accommodate the many constraints involved in the design of EMSs, and is suitable for real-time implementations. The design and real-time implementation of such a control approach involves control-oriented modeling, controller design (including high-level and low-level controllers), and control scheme performance evaluation. All of
these issues will be addressed in this thesis.
A control-relevant parameter estimation (CRPE) approach is used to make the control-oriented model more accurate. This improves the EMS performance, while maintaining its real-time implementation capability.
To reduce the computational complexity, the standard MPC controller is replaced by its explicit form. The explicit model predictive controller (eMPC) achieves the same performance as the implicit MPC, but requires less computational effort, which leads to a fast and reliable implementation. The performance of the control scheme is evaluated through different stages of model-in-the-loop (MIL) simulations with an equation-based and validated high-fidelity simulation model of a PHEV powertrain.
Finally, the CRPE-eMPC EMS is validated through a hardware-in-the-loop (HIL) test. HIL simulation shows that the proposed EMS can be implemented to a commercial control hardware in real time and results in promising fuel economy figures and emissions performance, while maintaining vehicle drivability
Information resources management, 1984-1989: A bibliography with indexes
This bibliography contains 768 annotated references to reports and journal articles entered into the NASA scientific and technical information database 1984 to 1989
Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling
In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects
13th Annual Review of Progress in Applied Computational Electromagnetics at the Naval Postgraduate School, Monterey, CA, March 17-21, 1997, Conference Proceedings Volumes I & II
Includes Volumes 1 &
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Development of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imaging
Different medical imaging modalities provide complementary anatomical and
functional information. One increasingly important use of such information is in
the clinical management of cardiovascular disease. Multi-modality data is helping
improve diagnosis accuracy, and individualize treatment. The Clinical Research
Imaging Centre at the University of Edinburgh, has been involved in a number
of cardiovascular clinical trials using longitudinal computed tomography (CT) and
multi-parametric magnetic resonance (MR) imaging. The critical image processing
technique that combines the information from all these different datasets is known
as image registration, which is the topic of this thesis. Image registration, especially
multi-modality and multi-parametric registration, remains a challenging field in
medical image analysis. The new registration methods described in this work were
all developed in response to genuine challenges in on-going clinical studies. These
methods have been evaluated using data from these studies.
In order to gain an insight into the building blocks of image registration methods,
the thesis begins with a comprehensive literature review of state-of-the-art algorithms.
This is followed by a description of the first registration method I developed to help
track inflammation in aortic abdominal aneurysms. It registers multi-modality and
multi-parametric images, with new contrast agents. The registration framework uses a
semi-automatically generated region of interest around the aorta. The aorta is aligned
based on a combination of the centres of the regions of interest and intensity matching.
The method achieved sub-voxel accuracy.
The second clinical study involved cardiac data. The first framework failed to
register many of these datasets, because the cardiac data suffers from a common
artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I
developed a new preprocessing technique that is able to correct the artefacts in the
functional data using data from the anatomical scans. The registration framework,
with this preprocessing step and new particle swarm optimizer, achieved significantly
improved registration results on the cardiac data, and was validated quantitatively
using neuro images from a clinical study of neonates. Although on average
the new framework achieved accurate results, when processing data corrupted
by severe artefacts and noise, premature convergence of the optimizer is still a
common problem. To overcome this, I invented a new optimization method, that
achieves more robust convergence by encoding prior knowledge of registration. The
registration results from this new registration-oriented optimizer are more accurate
than other general-purpose particle swarm optimization methods commonly applied
to registration problems.
In summary, this thesis describes a series of novel developments to an image
registration framework, aimed to improve accuracy, robustness and speed. The
resulting registration framework was applied to, and validated by, different types of
images taken from several ongoing clinical trials. In the future, this framework could
be extended to include more diverse transformation models, aided by new machine
learning techniques. It may also be applied to the registration of other types and
modalities of imaging data
Numerical and Evolutionary Optimization 2020
This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications
A Polyhedral Study of Mixed 0-1 Set
We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set