234 research outputs found
DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels
The impact of soiling on solar panels is an important and well-studied
problem in renewable energy sector. In this paper, we present the first
convolutional neural network (CNN) based approach for solar panel soiling and
defect analysis. Our approach takes an RGB image of solar panel and
environmental factors as inputs to predict power loss, soiling localization,
and soiling type. In computer vision, localization is a complex task which
typically requires manually labeled training data such as bounding boxes or
segmentation masks. Our proposed approach consists of specialized four stages
which completely avoids localization ground truth and only needs panel images
with power loss labels for training. The region of impact area obtained from
the predicted localization masks are classified into soiling types using the
webly supervised learning. For improving localization capabilities of CNNs, we
introduce a novel bi-directional input-aware fusion (BiDIAF) block that
reinforces the input at different levels of CNN to learn input-specific feature
maps. Our empirical study shows that BiDIAF improves the power loss prediction
accuracy by about 3% and localization accuracy by about 4%. Our end-to-end
model yields further improvement of about 24% on localization when learned in a
weakly supervised manner. Our approach is generalizable and showed promising
results on web crawled solar panel images. Our system has a frame rate of 22
fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected
first of it's kind dataset for solar panel image analysis consisting 45,000+
images.Comment: Accepted for publication at WACV 201
Performance of Bursty World Wide Web (WWW) Sources over ABR
We model World Wide Web (WWW) servers and clients running over an ATM network
using the ABR (available bit rate) service. The WWW servers are modeled using a
variant of the SPECweb96 benchmark, while the WWW clients are based on a model
by Mah. The traffic generated by this application is typically bursty, i.e., it
has active and idle periods in transmission. A timeout occurs after given
amount of idle period. During idle period the underlying TCP congestion windows
remain open until a timeout expires. These open windows may be used to send
data in a burst when the application becomes active again. This raises the
possibility of large switch queues if the source rates are not controlled by
ABR. We study this problem and show that ABR scales well with a large number of
bursty TCP sources in the system.Comment: Submitted to WebNet `97, Toronto, November 9
Feedback Consolidation Algorithms for ABR Point-to-Multipoint Connections in ATM Networks
ABR traffic management for point-to-multipoint connections controls the
source rate to the minimum rate supported by all the branches of the multicast
tree. A number of algorithms have been developed for extending ABR congestion
avoidance algorithms to perform feedback consolidation at the branch points.
This paper discusses various design options and implementation alternatives for
the consolidation algorithms, and proposes a number of new algorithms. The
performance of the proposed algorithms and the previous algorithms is compared
under a variety of conditions. Results indicate that the algorithms we propose
eliminate the consolidation noise (caused if the feedback is returned before
all branches respond), while exhibiting a fast transient response.Comment: Proceedings of IEEE INFOCOM 1998, March 1998, volume 3, pp. 1004-101
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