1,720,985 research outputs found
A Comparison framework for deep learning RFI detection algorithms in radio astronomy
No full text<p>These are the datasets used for the study titled: A Comparison framework for deep learning RFI detection algorithms in radio astronomy. These files are made publicly available as an additional resource to the submission of the author's Masters degree at Stellenbosch University. The detection is done in the field of radio astronomy. Each dataset consists of images/spectrograms/waterfall plots for baselines, and the corresponding binary mask for each image. The datasets can be used to train machine learning models, or for the case of this study, supervised fully convolutional neural networks.</p>
<p>The LOFAR datasets consists of real observations and was slightly modified from https://zenodo.org/record/6724065. See this resource regarding the observational parameters used to retrieve the data from the LOFAR Long Term Archive.The HERA dataset consists of simulated observations generated with hera_sim (https://readthedocs.org/projects/hera-sim/). The 28 March dataset consists of accurate pixel-perfect binary masks for each image. The 20 July dataset is identical to the first, except the binary masks are generated with AOFlagger. All three datasets have a test set stored with pixel-perfected simulation masks (HERA) or expert hand labeled masks (LOFAR).</p>
<p>The csv file contains the results of all trained models and and has fields for: model class, #filters, #FLOPS, #weights, preprocessing methods, train, validation and test accuracy scores as well as list of (threshold, FPR, TPR) values to generate receiver operating characteristic curves. See https://github.com/CharlDuToit/RFI-NLN to visualize the results, to train new models.</p>The financial assistance of the South African Radio Astronomy Observatory (SARAO) towards this research is hereby acknowledged (www.sarao.ac.za)
Fountain codes and their typical application in wireless standards like edge
Full text linkDissertation (MEng)--University of Pretoria, 2008.One of the most important technologies used in modern communication systems is channel coding. Channel coding dates back to a paper published by Shannon in 1948 [1] entitled “A Mathematical Theory of Communication”. The basic idea behind channel coding is to send redundant information (parity) together with a message to make the transmission more error resistant. There are different types of codes that can be used to generate the parity required, including block, convolutional and concatenated codes. A special subclass of codes consisting of the codes mentioned in the previous paragraph, is sparse graph codes. The structure of sparse graph codes can be depicted via a graphical representation: the factor graph which has sparse connections between its elements. Codes belonging to this subclass include Low-Density-Parity-Check (LDPC) codes, Repeat Accumulate (RA), Turbo and fountain codes. These codes can be decoded by using the belief propagation algorithm, an iterative algorithm where probabilistic information is passed to the nodes of the graph. This dissertation focuses on noisy decoding of fountain codes using belief propagation decoding. Fountain codes were originally developed for erasure channels, but since any factor graph can be decoded using belief propagation, noisy decoding of fountain codes can easily be accomplished. Three fountain codes namely Tornado, Luby Transform (LT) and Raptor codes were investigated during this dissertation. The following results were obtained: The Tornado graph structure is unsuitable for noisy decoding since the code structure protects the first layer of parity instead of the original message bits (a Tornado graph consists of more than one layer). The successful decoding of systematic LT codes were verified. A systematic Raptor code was introduced and successfully decoded. The simulation results show that the Raptor graph structure can improve on its constituent codes (a Raptor code consists of more than one code). Lastly an LT code was used to replace the convolutional incremental redundancy scheme used by the 2G mobile standard Enhanced Data Rates for GSM Evolution (EDGE). The results show that a fountain incremental redundancy scheme outperforms a convolutional approach if the frame lengths are long enough. For the EDGE platform the results also showed that the fountain incremental redundancy scheme outperforms the convolutional approach after the second transmission is received. Although EDGE is an older technology, it still remains a good platform for testing different incremental redundancy schemes, since it was one of the first platforms to use incremental redundancy.Electrical, Electronic and Computer EngineeringMEngunrestricte
A deep framework for predictive maintenance
Full text linkComputer ScienceENGLISH ABSTRACT: Predictive maintenance (PdM) is a well-known maintenance approach that comprises of two
problems, machine prognostic modelling and maintenance scheduling. The objective of prognostic
modelling is to predict faults in machine components such as aircraft engines, lithium-ion
batteries or bearings. The objective of maintenance scheduling is to reduce the cost of performing
maintenance once the future degradation behaviour of a component has been established.
Sensors are used to monitor the degradation behaviour of components as they change over time.
Supervised learning is a suitable solution for prognostic modelling problems, especially with the
increase in sensor readings being collected with Internet of Things (IoT) devices. Prognostic
modelling can be formulated as remaining useful life (RUL)- or machine state estimation. The
former is a regression- and the later is a classification problem.
Long short-term memory (LSTM) recurrent neural networks (RNNs) are an extension of traditional
RNNs that are effective at interpreting trends in the sensor readings and making longer
term estimations. An LSTM uses a window of sequential sensor readings when making prognostic
estimates which causes it to be less sensitive to local sensor variations, which results in
improved prognostic model performance.
In this study we create a framework to implement PdM approaches. The work consists of a
codebase which can be used to create testable, comparable and repeatable prognostic modelling
results and maintenance scheduling simulations. The codebase is designed to be extensible, to
allow future researchers to standardise prognostic modelling results. The codebase is used to
compare the prognostic modelling performance of an LSTM with tradition supervised prognostic
modelling approaches such as Random Forests (RF)s, Gradient boosted (GB) trees and
Support Vector Machines (SVM)s. The prognostic models are tested on three well-known
prognostic datasets, the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS)
engine aircraft-, Center for Advanced Life Cycle Engineering (CALCE) battery- and Intelligent
Maintenance Systems (IMS) bearing datasets. During the study we highlight factors that influence prognostic model performance, such as the effect of de-noising sensor readings and the size
of the sample window used by the LSTM when making estimations. The results of the prognostic
models are compared with previous studies and the LSTM shows improved performance
on considered cases.
The developed prognostic models are used to perform preventative maintenance scheduling with
assumed costs in two simulations. The objective is first to compare the efficacy of traditional
maintenance approaches, such as a mean time between failure (MTBF) strategy, with a PdM
strategy, and second to investigate the effect of using a better performing prognostic model
(such as the LSTM) in a PdM strategy. The improvements are measured by the reduction in
costs.
Key words:
Predictive maintenance; remaining useful life; machine state estimation; preventative maintenance
scheduling.AFRIKAANSE OPSOMMING: Voorspellende instandhouding (PdM) is ’n bekende instandhoudingsbenadering wat bestaan uit
twee probleme, naamlik masjienprognostiese modellering en instandhoudingskedulering. Die
doel van prognostiese modellering is om foute in masjienkomponente soos vliegtuigenjins, litiumioonbatterye
of laers te voorspel. Die doel van instandhoudingskedulering is om die koste van
die uitvoering van instandhouding te verminder sodra die toekomstige degradasiegedrag van ’n
komponent vasgestel is.
Sensors word monitor die degradasiegedrag van komponente soos hulle verander oor tyd. Toesigleer
is ’n geskikte oplossing vir prognostiese modelleringsprobleme, veral met die toename
in sensorlesings wat met Internet of Things (IoT) toestelle ingesamel word. Prognostiese modellering
kan geformuleer word as oorblywende nuttige lewensduur (RUL)- of masjientoestandberaming.
Eersgenoemde is ’n regressie- en die latere is ’n klassifikasieprobleem.
Langtermyngeheue (LSTM) herhalende neurale netwerke (RNN) is ’n uitbreiding van ’n tradisionele
RNN wat effektief is om tendense in die sensorlesings te interpreteer en langertermynskattings
te maak. ’n LSTM gebruik ’n venster van opeenvolgende sensorlesings wanneer
prognostiese skattings gemaak word, wat veroorsaak dat dit minder sensitief is vir plaaslike
sensorvariasies, wat lei tot verbeterde prognostiese modelwerkverrigting.
In hierdie studie skep ons ’n raamwerk om PdM-benaderings te implementeer. Die werk bestaan
uit ’n kodebasis wat gebruik kan word om toetsbare, vergelykbare en herhaalbare prognostiese
modelleringsresultate en onderhoudskeduleringssimulasies te skep. Die kodebasis is ontwerp om
uitbreidbaar te wees, sodat toekomstige navorsers prognostiese modelleringsresultate kan standaardiseer.
Die kodebasis word gebruik om die prognostiese modelleringsprestasie van ’n LSTM
te vergelyk met tradisionele prognostiese modelleringsbenaderings soos Random Forests (RF)’e,
Gradient boosted (GB) trees en Support Vector Machines (SVM)’s. Die prognostiese modelle
word getoets op drie bekende prognostiese datastelle, die Commercial Modular Aero-Propulsion
System Simulation (C-MAPSS) enjinvliegtuie, Sentrum vir Gevorderde Lewensiklusingenieurswese (CALCE) battery en Intelligente Onderhoudstelsels (IMS) dradatastelle. Tydens die
studie beklemtoon ons faktore wat prognostiese modelprestasie beïnvloed, soos die effek van
die ruisonderdrukking van sensorlesings en die grootte van die monstervenster wat deur die
LSTM gebruik word wanneer ramings gemaak word. Die resultate van die prognostiese modelle
word vergelyk met vorige studies en die LSTM toon verbeterde prestasie op die oorwoë
gevalle.
Die ontwikkelde prognostiese modelle word gebruik om voorkomende instandhoudingskedulering
uit te voer met veronderstelde koste in twee simulasies. Die doelwit is eerstens om die
doeltreffendheid van tradisionele-instandhoudingsbenaderings, vb. ’n gemiddelde tyd tussen
mislukking (MTBF)-strategie, met ’n PdM-strategie te vergelyk en tweedens om die effek van
die gebruik van ’n beter presterende prognostiese model (soos die LSTM) in ’n PdM strategie
te ondersoek. PdM strategie. Die verbeterings word gemeet aan die vermindering in koste.
Sleutelwoorde:
Voorspellende instandhouding; oorblywende nuttige lewensduur; masjien toestand skatting;
voorkomende onderhoudskedulering
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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