23 research outputs found
From Electrical Power Flows to Unsplittabe Flows: A QPTAS for OPF with Discrete Demands in Line Distribution Networks
The {\it AC Optimal Power Flow} (OPF) problem is a fundamental problem in
power systems engineering which has been known for decades. It is a notoriously
hard problem due mainly to two reasons: (1) non-convexity of the power flow
constraints and (2) the (possible) existence of discrete power injection
constraints. Recently, sufficient conditions were provided for certain convex
relaxations of OPF to be exact in the continuous case, thus allowing one to
partially address the issue of non-convexity. In this paper we make a first
step towards addressing the combinatorial issue. Namely, by establishing a
connection to the well-known {\it unsplittable flow problem} (UFP), we are able
to generalize known techniques for the latter problem to provide approximation
algorithms for OPF with discrete demands. As an application, we give a
quasi-polynomial time approximation scheme for OPF in line networks under some
mild assumptions and a single generation source. We believe that this
connection can be further leveraged to obtain approximation algorithms for more
general settings, such as multiple generation sources and tree networks
Enabling Self-aware Smart Buildings by Augmented Reality
Conventional HVAC control systems are usually incognizant of the physical
structures and materials of buildings. These systems merely follow pre-set HVAC
control logic based on abstract building thermal response models, which are
rough approximations to true physical models, ignoring dynamic spatial
variations in built environments. To enable more accurate and responsive HVAC
control, this paper introduces the notion of "self-aware" smart buildings, such
that buildings are able to explicitly construct physical models of themselves
(e.g., incorporating building structures and materials, and thermal flow
dynamics). The question is how to enable self-aware buildings that
automatically acquire dynamic knowledge of themselves. This paper presents a
novel approach using "augmented reality". The extensive user-environment
interactions in augmented reality not only can provide intuitive user
interfaces for building systems, but also can capture the physical structures
and possibly materials of buildings accurately to enable real-time building
simulation and control. This paper presents a building system prototype
incorporating augmented reality, and discusses its applications.Comment: This paper appears in ACM International Conference on Future Energy
Systems (e-Energy), 201
Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones
Autonomous drones (also known as unmanned aerial vehicles) are increasingly
popular for diverse applications of light-weight delivery and as substitutions
of manned operations in remote locations. The computing systems for drones are
becoming a new venue for research in cyber-physical systems. Autonomous drones
require integrated intelligent decision systems to control and manage their
flight missions in the absence of human operators. One of the most crucial
aspects of drone mission control and management is related to the optimization
of battery lifetime. Typical drones are powered by on-board batteries, with
limited capacity. But drones are expected to carry out long missions. Thus, a
fully automated management system that can optimize the operations of
battery-operated autonomous drones to extend their operation time is highly
desirable. This paper presents several contributions to automated management
systems for battery-operated drones: (1) We conduct empirical studies to model
the battery performance of drones, considering various flight scenarios. (2) We
study a joint problem of flight mission planning and recharging optimization
for drones with an objective to complete a tour mission for a set of sites of
interest in the shortest time. This problem captures diverse applications of
delivery and remote operations by drones. (3) We present algorithms for solving
the problem of flight mission planning and recharging optimization. We
implemented our algorithms in a drone management system, which supports
real-time flight path tracking and re-computation in dynamic environments. We
evaluated the results of our algorithms using data from empirical studies. (4)
To allow fully autonomous recharging of drones, we also develop a robotic
charging system prototype that can recharge drones autonomously by our drone
management system