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