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A multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithms
YesThe launch of the Solar Dynamics Observatory (SDO) in early 2010 has provided the solar
physics community with the most detailed view of the Sun to date. However, this presents new
challenges for the analysis of solar data. Currently,
SDO sends over 1 terabyte of data per day back to Earth and methods for fast and reliable analysis are
more important than ever. This article details four algorithms developed separately at the Universities
of Bradford and Glasgow, the
Royal Observatory of Belgium and Trinity College Dublin for the purposes of automated detection of
solar active regions (ARs) and sunspots at different levels of the solar atmosphere
On Coupling FCA and MDL in Pattern Mining
International audiencePattern Mining is a well-studied field in Data Mining and Machine Learning. The modern methods are based on dynamically updating models, among which MDL-based ones ensure high-quality pattern sets. Formal concepts also characterize patterns in a condensed form. In this paper we study MDL-based algorithm called Krimp in FCA settings and propose a modified version that benefits from FCA and relies on probabilistic assumptions that underlie MDL. We provide an experimental proof that the proposed approach improves quality of pattern sets generated by Krimp
An Information-Centric Communication Infrastructure for Real-Time State Estimation of Active Distribution Networks
© 2010-2012 IEEE.The evolution toward emerging active distribution networks (ADNs) can be realized via a real-time state estimation (RTSE) application facilitated by the use of phasor measurement units (PMUs). A critical challenge in deploying PMU-based RTSE applications at large scale is the lack of a scalable and flexible communication infrastructure for the timely (i.e., sub-second) delivery of the high volume of synchronized and continuous synchrophasor measurements. We address this challenge by introducing a communication platform called C-DAX based on the information-centric networking (ICN) concept. With a topic-based publish-subscribe engine that decouples data producers and consumers in time and space, C-DAX enables efficient synchrophasor measurement delivery, as well as flexible and scalable (re)configuration of PMU data communication for seamless full observability of power conditions in complex and dynamic scenarios. Based on the derived set of requirements for supporting PMU-based RTSE in ADNs, we design the ICN-based C-DAX communication platform, together with a joint optimized physical network resource provisioning strategy, in order to enable the agile PMU data communications in near real-time. In this paper, C-DAX is validated via a field trial implementation deployed over a sample feeder in a real-distribution network; it is also evaluated through simulation-based experiments using a large set of real medium voltage grid topologies currently operating live in The Netherlands. This is the first work that applies emerging communication paradigms, such as ICN, to smart grids while maintaining the required hard real-time data delivery as demonstrated through field trials at national scale. As such, it aims to become a blueprint for the application of ICN-based general purpose communication platforms to ADNs
Mining itemsets in the presence of missing values
Missing values make up an important and unavoidable problem in data management and analysis. In the context of association rule and frequent itemset mining, however, this issue never received much attention. Nevertheless, the well known measures of support and confidence are misleading when missing values occur in the data, and more suitable definitions typically don't have the crucial monotonicity property of support. In this paper, we overcome this problem and provide an efficient algorithm, XMiner, for mining association rules and frequent itemsets in databases with missing values. XMiner is empirically evaluated, showing a clear gain over a straightforward baseline-algorithm
The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images
Context. Precise localization and characterization of active regions (AR) and coronal holes (CH) as observed by extreme ultra violet (EUV) imagers are crucial for a wide range of solar and helio-physics studies.
Aims. We introduce a set of segmentation procedures (known as the SPoCA-suite) that allows one to retrieve AR and CH properties on EUV images taken from SOHO-EIT, STEREO-EUVI, PROBA2-SWAP, and SDO-AIA.
Methods. We build upon our previous work on the Spatial Possibilistic Clustering Algorithm (SPoCA), that we have improved substantially in several ways.
Results. We apply our algorithm on the synoptic EIT archive from 1997 to 2011 and decompose this dataset into regions that can clearly be identified as AR, quiet Sun, and CH. An antiphase between AR and CH filling factor is observed, as expected. The SPoCA-suite is next applied to datasets from EUVI, SWAP, and AIA. The time series pertaining to ARs or CHs are presented.
Conclusions. The SPoCA-suite enables the extraction of several long time series of AR and CH properties from the data files of EUV imagers and also allows tracking individual ARs or CHs over time. For AIA images, AR and CH catalogs are available in near-real time from the Heliophysics Events Knowledgebase. The full code, which allows processing any EUV images, is available upon request to the authors
Mining itemsets in the presence of missing values
Missing values make up an important and unavoidable problem in data management and analysis. In the context of association rule and frequent itemset mining, however, this issue never received much attention. Nevertheless, the well known measures of support and confidence are misleading when missing values occur in the data, and more suitable definitions typically don't have the crucial monotonicity property of support. In this paper, we overcome this problem and provide an efficient algorithm, XMiner, for mining association rules and frequent itemsets in databases with missing values. XMiner is empirically evaluated, showing a clear gain over a straightforward baseline-algorithm
Modelling nitrous and nitric oxide emissions by autotrophic ammonia-oxidizing bacteria
<div><p>The emission of greenhouse gases, such as N<sub>2</sub>O, from wastewater treatment plants is a matter of growing concern. Denitrification by ammonia-oxidizing bacteria (AOB) has been identified as the main N<sub>2</sub>O producing pathway. To estimate N<sub>2</sub>O emissions during biological nitrogen removal, reliable mathematical models are essential. In this work, a mathematical model for NO (a precursor for N<sub>2</sub>O formation) and N<sub>2</sub>O formation by AOB is presented. Based on mechanistic grounds, two possible reaction mechanisms for NO and N<sub>2</sub>O formation are distinguished, which differ in the origin of the reducing equivalents needed for denitrification by AOB. These two scenarios have been compared in a simulation study, assessing the influence of the aeration/stripping rate and the resulting dissolved oxygen (DO) concentration on the NO and N<sub>2</sub>O emission from a SHARON partial nitritation reactor. The study of the simulated model behaviour and its comparison with previously published experimental data serves in elucidating the true NO and N<sub>2</sub>O formation mechanism.</p>
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