6,769 research outputs found
System configuration, fault detection, location, isolation and restoration: a review on LVDC Microgrid protections
Low voltage direct current (LVDC) distribution has gained the significant interest of research due to the advancements in power conversion technologies. However, the use of converters has given rise to several technical issues regarding their protections and controls of such devices under faulty conditions. Post-fault behaviour of converter-fed LVDC system involves both active converter control and passive circuit transient of similar time scale, which makes the protection for LVDC distribution significantly different and more challenging than low voltage AC. These protection and operational issues have handicapped the practical applications of DC distribution. This paper presents state-of-the-art protection schemes developed for DC Microgrids. With a close look at practical limitations such as the dependency on modelling accuracy, requirement on communications and so forth, a comprehensive evaluation is carried out on those system approaches in terms of system configurations, fault detection, location, isolation and restoration
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
This paper is about alerting acoustic event detection and sound source
localisation in an urban scenario. Specifically, we are interested in spotting
the presence of horns, and sirens of emergency vehicles. In order to obtain a
reliable system able to operate robustly despite the presence of traffic noise,
which can be copious, unstructured and unpredictable, we propose to treat the
spectrograms of incoming stereo signals as images, and apply semantic
segmentation, based on a Unet architecture, to extract the target sound from
the background noise. In a multi-task learning scheme, together with signal
denoising, we perform acoustic event classification to identify the nature of
the alerting sound. Lastly, we use the denoised signals to localise the
acoustic source on the horizon plane, by regressing the direction of arrival of
the sound through a CNN architecture. Our experimental evaluation shows an
average classification rate of 94%, and a median absolute error on the
localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of
2.5{\deg} when operating on frames of 2.5s. The system offers excellent
performance in particularly challenging scenarios, where the noise level is
remarkably high.Comment: 6 pages, 9 figure
An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability
We conduct an empirical study to test the ability of Convolutional Neural
Networks (CNNs) to reduce the effects of nuisance transformations of the input
data, such as location, scale and aspect ratio. We isolate factors by adopting
a common convolutional architecture either deployed globally on the image to
compute class posterior distributions, or restricted locally to compute class
conditional distributions given location, scale and aspect ratios of bounding
boxes determined by proposal heuristics. In theory, averaging the latter should
yield inferior performance compared to proper marginalization. Yet empirical
evidence suggests the converse, leading us to conclude that - at the current
level of complexity of convolutional architectures and scale of the data sets
used to train them - CNNs are not very effective at marginalizing nuisance
variability. We also quantify the effects of context on the overall
classification task and its impact on the performance of CNNs, and propose
improved sampling techniques for heuristic proposal schemes that improve
end-to-end performance to state-of-the-art levels. We test our hypothesis on a
classification task using the ImageNet Challenge benchmark and on a
wide-baseline matching task using the Oxford and Fischer's datasets.Comment: 10 pages, 5 figures, 3 tables -- CVPR 2016, camera-ready versio
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Prototyping a Context-Aware Framework for Pervasive Entertainment Applications
An Architecture for Information Commerce Systems
The increasing use of the Internet in business and commerce has created a number of new business opportunities and the need for supporting models and platforms. One of these opportunities is information commerce (i-commerce), a special case of ecommerce focused on the purchase and sale of information as a commodity. In this paper we present an architecture for i-commerce systems using OPELIX (Open Personalized Electronic Information Commerce System) [11] as an example. OPELIX provides an open information commerce platform that enables enterprises to produce, sell, deliver, and manage information products and related services over the Internet. We focus on the notion of information marketplace, a virtual location that enables i-commerce, describe the business and domain model for an information marketplace, and discuss the role of intermediaries in this environment. The domain model is used as the basis for the software architecture of the OPELIX system. We discuss the characteristics of the OPELIX architecture and compare our approach to related work in the field
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