1,049 research outputs found
The Contribution Of Occupancy Behavior To Energy Consumption In Low Income Residential Buildings
Energy consumption in residential buildings consumes 22% of the total US energy each year and is highly impacted by the occupant behavior. In order to model domestic demand profiles more accurately, it is important to understand occupancy behavior profile. Four low income houses in Texas are used as the test beds. The occupancy sensors are installed in every room. The real-life occupancy data from the occupancy sensors were compared with the American Time Use Survey (ATUS) data. The study period is from July 1 to August 31. The preliminary result shows that there is a similarity between ATUS data and actual occupancy profile. In addition, simulations in EnergyPlus were conducted to test how much energy consumption can be saved based on the thermostat control of real-life occupancy behavior patterns. The results show that such control can save cooling energy by 7%
Identify bottom contribution in non-photonic electron spectra and \vv\ from \AuAu collisions at RHIC
We present a study on the spectra and elliptic flow v2 for heavy flavor
(charm and bottom) decayed electrons provided the relative contributions of
charm and bottom hadrons from the PYTHIA calculations. We made a simultaneous
fit to both measured non-photonic electron spectra and v2 distributions. The
results suggest that the bottom contribution is not dominant for electron pt<5
GeV/c in the 200 GeV Au+Au collisions.Comment: 4 pages, 3 figures, proceedings for Hard Probe 2006, Asilomar; to be
published in Nuclear Physics, Section
Quasi-static Soft Fixture Analysis of Rigid and Deformable Objects
We present a sampling-based approach to reasoning about the caging-based
manipulation of rigid and a simplified class of deformable 3D objects subject
to energy constraints. Towards this end, we propose the notion of soft fixtures
extending earlier work on energy-bounded caging to include a broader set of
energy function constraints and settings, such as gravitational and elastic
potential energy of 3D deformable objects. Previous methods focused on
establishing provably correct algorithms to compute lower bounds or
analytically exact estimates of escape energy for a very restricted class of
known objects with low-dimensional C-spaces, such as planar polygons. We
instead propose a practical sampling-based approach that is applicable in
higher-dimensional C-spaces but only produces a sequence of upper-bound
estimates that, however, appear to converge rapidly to actual escape energy. We
present 8 simulation experiments demonstrating the applicability of our
approach to various complex quasi-static manipulation scenarios. Quantitative
results indicate the effectiveness of our approach in providing upper-bound
estimates for escape energy in quasi-static manipulation scenarios. Two
real-world experiments also show that the computed normalized escape energy
estimates appear to correlate strongly with the probability of escape of an
object under randomized pose perturbation.Comment: Paper submitted to ICRA 202
Target-Grounded Graph-Aware Transformer for Aerial Vision-and-Dialog Navigation
This report details the method of the winning entry of the AVDN Challenge in
ICCV 2023. The competition addresses the Aerial Navigation from Dialog History
(ANDH) task, which requires a drone agent to associate dialog history with
aerial observations to reach the destination. For better cross-modal grounding
abilities of the drone agent, we propose a Target-Grounded Graph-Aware
Transformer (TG-GAT) framework. Concretely, TG-GAT first leverages a
graph-aware transformer to capture spatiotemporal dependency, which is
beneficial for navigation state tracking and robust action planning. TG-GAT
first leverages a graph-aware transformer to capture spatiotemporal
dependencies for more robust action planning. In addition, an auxiliary visual
grounding task is devised to boost the agent's awareness of referred landmarks.
Moreover, a hybrid augmentation strategy based on large language models is
utilized to mitigate data scarcity limitations. Our TG-GAT framework won the
AVDN Challenge 2023, with 2.2% and 3.0% absolute improvements over the baseline
on SPL and SR metrics, respectively. The code is available at
https://github.com/yifeisu/avdn-challenge
Chat-PM: A Class of Composite Hybrid Aerial/Terrestrial Precise Manipulator
This paper concentrates on the development of Chat-PM, a class of composite
hybrid aerial/terrestrial manipulator, in concern with composite configuration
design, dynamics modeling, motion control and force estimation. Compared with
existing aerial or terrestrial mobile manipulators, Chat-PM demonstrates
advantages in terms of reachability, energy efficiency and manipulation
precision. To achieve precise manipulation in terrestrial mode, the dynamics is
analyzed with consideration of surface contact, based on which a cascaded
controller is designed with compensation for the interference force and torque
from the arm. Benefiting from the kinematic constraints caused by the surface
contact, the position deviation and the vehicle vibration are effectively
decreased, resulting in higher control precision of the end gripper. For
manipulation on surfaces with unknown inclination angles, the moving horizon
estimation (MHE) is exploited to obtain the precise estimations of force and
inclination angle, which are used in the control loop to compensate for the
effect of the unknown surface. Real-world experiments are performed to evaluate
the superiority of the developed manipulator and the proposed controllers
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