6,715 research outputs found
Bounded rank optimization for effective and efficient emergency response
Effective placement of emergency response vehicles (such as ambulances, fire trucks, police cars) to deal with medical, fire or criminal activities can reduce the incident response time by few seconds, which in turn can potentially save a human life. Owing to its adoption in Emergency Medical Services (EMSs) worldwide, existing research on improving emergency response has focused on optimizing the objective of bounded time (i.e. number of incidents served in a fixed time). Due to the dependence of this objective on temporal uncertainty, optimizing the bounded time objective is challenging. In this paper, we propose a new objective referred to as the bounded rank (which is the number of incidents served by a base station whose rank is below a bounded rank value) that has nice theoretical properties and serves as an indirect substitute for the bounded time objective. To understand the theoretical properties of this new objective in the context of the spatio-temporal uncertainty associated with emergency incidents, we first provide a Poisson Point Process (PPP) model of the emergency response problem. We then formally define the bounded rank objective in the context of the model and demonstrate that the bounded rank metric is monotone submodular. Due to the monotone submodularity of the objective, we can propose a greedy approach that can provide an {\em a priori} guarantee of 50\% from optimal and a much tighter {\em posteriori} guarantee. Practically and more importantly, we demonstrate that optimizing this bounded rank objective on simulators validated on real data (and not just on the abstract PPP model) provides better results than the best known approach for optimizing bounded time objective
Online Learning of Power Transmission Dynamics
We consider the problem of reconstructing the dynamic state matrix of
transmission power grids from time-stamped PMU measurements in the regime of
ambient fluctuations. Using a maximum likelihood based approach, we construct a
family of convex estimators that adapt to the structure of the problem
depending on the available prior information. The proposed method is fully
data-driven and does not assume any knowledge of system parameters. It can be
implemented in near real-time and requires a small amount of data. Our learning
algorithms can be used for model validation and calibration, and can also be
applied to related problems of system stability, detection of forced
oscillations, generation re-dispatch, as well as to the estimation of the
system state.Comment: 7 pages, 4 figure
Resilience-oriented control and communication framework for cyber-physical microgrids
Climate change drives the energy supply transition from traditional fossil fuel-based power generation to renewable energy resources. This transition has been widely recognised as one of the most significant developing pathways promoting the decarbonisation process toward a zero-carbon and sustainable society. Rapidly developing renewables gradually dominate energy systems and promote the current energy supply system towards decentralisation and digitisation.
The manifestation of decentralisation is at massive dispatchable energy resources, while the digitisation features strong cohesion and coherence between electrical power technologies and information and communication technologies (ICT).
Massive dispatchable physical devices and cyber components are interdependent and coupled tightly as a cyber-physical energy supply system, while this cyber-physical energy supply system currently faces an increase of extreme weather (e.g., earthquake, flooding) and cyber-contingencies (e.g., cyberattacks) in the frequency, intensity, and duration. Hence, one major challenge is to find an appropriate cyber-physical solution to accommodate increasing renewables while enhancing power supply resilience.
The main focus of this thesis is to blend centralised and decentralised frameworks to propose a collaboratively centralised-and-decentralised resilient control framework for energy systems i.e., networked microgrids (MGs) that can operate optimally in the normal condition while can mitigate simultaneous cyber-physical contingencies in the extreme condition. To achieve this, we investigate the concept of "cyber-physical resilience" including four phases, namely prevention/upgrade, resistance, adaption/mitigation, and recovery. Throughout these stages, we tackle different cyber-physical challenges under the concept of microgrid ranging from a centralised-to-decentralised transitional control framework coping with cyber-physical out of service, a cyber-resilient distributed control methodology for networked MGs, a UAV assisted post-contingency cyber-physical service restoration, to a fast-convergent distributed dynamic state estimation algorithm for a class of interconnected systems.Open Acces
Barriers to industrial energy efficiency: a literature review
No description supplie
Beamforming Design for Joint Localization and Data Transmission in Distributed Antenna System
A distributed antenna system is studied whose goal is to provide data
communication and positioning functionalities to Mobile Stations (MSs). Each MS
receives data from a number of Base Stations (BSs), and uses the received
signal not only to extract the information but also to determine its location.
This is done based on Time of Arrival (TOA) or Time Difference of Arrival
(TDOA) measurements, depending on the assumed synchronization conditions. The
problem of minimizing the overall power expenditure of the BSs under data
throughput and localization accuracy requirements is formulated with respect to
the beamforming vectors used at the BSs. The analysis covers both
frequency-flat and frequency-selective channels, and accounts also for
robustness constraints in the presence of parameter uncertainty. The proposed
algorithmic solutions are based on rank-relaxation and Difference-of-Convex
(DC) programming.Comment: 15 pages, 9 figures, and 1 table, accepted in IEEE Transactions on
Vehicular Technolog
Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality
Motivated by an observational study of the effect of hospital ward versus
intensive care unit admission on severe sepsis mortality, we develop methods to
address two common problems in observational studies: (1) when there is a lack
of covariate overlap between the treated and control groups, how to define an
interpretable study population wherein inference can be conducted without
extrapolating with respect to important variables; and (2) how to use
randomization inference to form confidence intervals for the average treatment
effect with binary outcomes. Our solution to problem (1) incorporates existing
suggestions in the literature while yielding a study population that is easily
understood in terms of the covariates themselves, and can be solved using an
efficient branch-and-bound algorithm. We address problem (2) by solving a
linear integer program to utilize the worst case variance of the average
treatment effect among values for unobserved potential outcomes that are
compatible with the null hypothesis. Our analysis finds no evidence for a
difference between the sixty day mortality rates if all individuals were
admitted to the ICU and if all patients were admitted to the hospital ward
among less severely ill patients and among patients with cryptic septic shock.
We implement our methodology in R, providing scripts in the supplementary
material
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