6,918 research outputs found
Physics-guided Residual Learning for Probabilistic Power Flow Analysis
Probabilistic power flow (PPF) analysis is critical to power system operation
and planning. PPF aims at obtaining probabilistic descriptions of the state of
the system with stochastic power injections (e.g., renewable power generation
and load demands). Given power injection samples, numerical methods repeatedly
run classic power flow (PF) solvers to find the voltage phasors. However, the
computational burden is heavy due to many PF simulations. Recently, many
data-driven based PF solvers have been proposed due to the availability of
sufficient measurements. This paper proposes a novel neural network (NN)
framework which can accurately approximate the non-linear AC-PF equations. The
trained NN works as a rapid PF solver, significantly reducing the heavy
computational burden in classic PPF analysis. Inspired by residual learning, we
develop a fully connected linear layer between the input and output in the
multilayer perceptron (MLP). To improve the NN training convergence, we propose
three schemes to initialize the NN weights of the shortcut connection layer
based on the physical characteristics of AC-PF equations. Specifically, two
model-based methods require the knowledge of system topology and line
parameters, while the purely data-driven method can work without power grid
parameters. Numerical tests on five benchmark systems show that our proposed
approaches achieve higher accuracy in estimating voltage phasors than existing
methods. In addition, three meticulously designed initialization schemes help
the NN training process converge faster, which is appealing under limited
training time.Comment: Probabilistic power flow, data-driven, residual learning, neural
network, physics-guided initializatio
Digging Deeper into Egocentric Gaze Prediction
This paper digs deeper into factors that influence egocentric gaze. Instead
of training deep models for this purpose in a blind manner, we propose to
inspect factors that contribute to gaze guidance during daily tasks. Bottom-up
saliency and optical flow are assessed versus strong spatial prior baselines.
Task-specific cues such as vanishing point, manipulation point, and hand
regions are analyzed as representatives of top-down information. We also look
into the contribution of these factors by investigating a simple recurrent
neural model for ego-centric gaze prediction. First, deep features are
extracted for all input video frames. Then, a gated recurrent unit is employed
to integrate information over time and to predict the next fixation. We also
propose an integrated model that combines the recurrent model with several
top-down and bottom-up cues. Extensive experiments over multiple datasets
reveal that (1) spatial biases are strong in egocentric videos, (2) bottom-up
saliency models perform poorly in predicting gaze and underperform spatial
biases, (3) deep features perform better compared to traditional features, (4)
as opposed to hand regions, the manipulation point is a strong influential cue
for gaze prediction, (5) combining the proposed recurrent model with bottom-up
cues, vanishing points and, in particular, manipulation point results in the
best gaze prediction accuracy over egocentric videos, (6) the knowledge
transfer works best for cases where the tasks or sequences are similar, and (7)
task and activity recognition can benefit from gaze prediction. Our findings
suggest that (1) there should be more emphasis on hand-object interaction and
(2) the egocentric vision community should consider larger datasets including
diverse stimuli and more subjects.Comment: presented at WACV 201
Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability
Limited presence of nodal and line meters in distribution grids hinders their
optimal operation and participation in real-time markets. In particular lack of
real-time information on the grid topology and infrequently calibrated line
parameters (impedances) adversely affect the accuracy of any operational power
flow control. This paper suggests a novel algorithm for learning the topology
of distribution grid and estimating impedances of the operational lines with
minimal observational requirements - it provably reconstructs topology and
impedances using voltage and injection measured only at the terminal (end-user)
nodes of the distribution grid. All other (intermediate) nodes in the network
may be unobserved/hidden. Furthermore no additional input (e.g., number of grid
nodes, historical information on injections at hidden nodes) is needed for the
learning to succeed. Performance of the algorithm is illustrated in numerical
experiments on the IEEE and custom power distribution models
Space Shuttle propulsion parameter estimation using optimal estimation techniques
The fifth monthly progress report includes corrections and additions to the previously submitted reports. The addition of the SRB propellant thickness as a state variable is included with the associated partial derivatives. During this reporting period, preliminary results of the estimation program checkout was presented to NASA technical personnel
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