1,720,954 research outputs found
Event detection and classification for disaggregation of energy consumption metering data
No full textUma solução potencial para lidar com a urgente questão do aquecimento global reside na eficiência energ´ etica e no consumo de energia de edifícios residenciais. O monitoramento não intrusivo de cargas (NILM) surge como uma abordagem promissora para otimizar o consumo de energia, fornecendo informações detalhadas sobre o uso individual de eletrodomésticos. Esta dissertação de mestrado tem como foco explorar e avaliar diversas abordagens para detecção e classificação de eventos no contexto do NILM. A pesquisa começa testando a biblioteca NILMTK, que oferece um conjunto abrangente de algoritmos e ferramentas para desagregação de energia. A partir dessa investigação inicial, é examinada uma abordagem mais recente baseada em redes neurais profundas. Al ém disso, é desenvolvida e apresentada uma nova metodologia centrada na utilização das capacidades do scikit-learn. A dissertação destaca as principais descobertas de cada abordagem, discutindo suas promessas e limitações. São fornecidas contas detalhadas dos obstáculos encontrados ao longo do desenvolvimento, permitindo uma compreensão abrangente dos desafios enfrentados no campo do NILM. Al ém disso, melhorias e aprimoramentos potenciais são propostos para realizar com sucesso a desagregação de energia. Nesta pesquisa, uma infinidade de algoritmos de aprendizado de máquina, tanto nas categorias de classificação quanto de regressão , são explorados como soluções potenciais para o NILM. Uma coleção de soluções propostas nesta dissertação - como a Regressão Random Forest, Regressão por Gradient Boosting e SVR - demonstra um potencial significativo para avançar no campo do NILM e na desagregação de energia. Al ém disso, essas descobertas oferecem insights promissores sobre a viabilidade da implementação de abordagens diferentes, bem como a eficácia dos métodos estudados ao longo desta dissertaçãoOne potential solution for addressing the pressing issue of global warming lies in the energy effi- ciency and power consumption of residential buildings. Non-intrusive load monitoring (NILM) emerges as a promising approach to optimize energy consumption by providing detailed insights into individual appliance usage. This master’s thesis focuses on exploring and evaluating various approaches for event detection and classification within NILM. The research begins by testing the NILMTK toolkit, which offers a compre- hensive set of algorithms and tools for energy disaggregation. Building upon this initial investigation, a more recent approach based on deep neural networks is examined. Additionally, a novel methodology centered on leveraging the capabilities of scikit-learn is designed, developed, and presented. The thesis highlights key findings from each approach, discussing their promises and limitations. Detailed accounts of the encountered obstacles throughout the development pathway are provided, allowing for a comprehensive understanding of the challenges faced in the field of NILM. Furthermore, potential improvements and enhancements are proposed in order to successfully perform the energy disaggregation. In this research, a plethora of machine learning algorithms, both in the categories of classification and regression, are explored as potential solutions for NILM. A collection of solutions proposed in this dissertation - i.e. Random Forest Regression, Gradient Boosting Regression, and SVR - demonstrates a significant potential for advancing the NILM field and energy disaggregation. Moreover, these findings offer promising insights on feasibility of implementation of different approaches as well as the effective- ness of the methods that have been studied in the extent of this dissertatio
Event detection and classification for disaggregation of energy consumption metering data
No full textUma solução potencial para lidar com a urgente questão do aquecimento global reside na eficiência energ´ etica e no consumo de energia de edifícios residenciais. O monitoramento não intrusivo de cargas (NILM) surge como uma abordagem promissora para otimizar o consumo de energia, fornecendo informações detalhadas sobre o uso individual de eletrodomésticos. Esta dissertação de mestrado tem como foco explorar e avaliar diversas abordagens para detecção e classificação de eventos no contexto do NILM. A pesquisa começa testando a biblioteca NILMTK, que oferece um conjunto abrangente de algoritmos e ferramentas para desagregação de energia. A partir dessa investigação inicial, é examinada uma abordagem mais recente baseada em redes neurais profundas. Al ém disso, é desenvolvida e apresentada uma nova metodologia centrada na utilização das capacidades do scikit-learn. A dissertação destaca as principais descobertas de cada abordagem, discutindo suas promessas e limitações. São fornecidas contas detalhadas dos obstáculos encontrados ao longo do desenvolvimento, permitindo uma compreensão abrangente dos desafios enfrentados no campo do NILM. Al ém disso, melhorias e aprimoramentos potenciais são propostos para realizar com sucesso a desagregação de energia. Nesta pesquisa, uma infinidade de algoritmos de aprendizado de máquina, tanto nas categorias de classificação quanto de regressão , são explorados como soluções potenciais para o NILM. Uma coleção de soluções propostas nesta dissertação - como a Regressão Random Forest, Regressão por Gradient Boosting e SVR - demonstra um potencial significativo para avançar no campo do NILM e na desagregação de energia. Al ém disso, essas descobertas oferecem insights promissores sobre a viabilidade da implementação de abordagens diferentes, bem como a eficácia dos métodos estudados ao longo desta dissertaçãoOne potential solution for addressing the pressing issue of global warming lies in the energy effi- ciency and power consumption of residential buildings. Non-intrusive load monitoring (NILM) emerges as a promising approach to optimize energy consumption by providing detailed insights into individual appliance usage. This master’s thesis focuses on exploring and evaluating various approaches for event detection and classification within NILM. The research begins by testing the NILMTK toolkit, which offers a compre- hensive set of algorithms and tools for energy disaggregation. Building upon this initial investigation, a more recent approach based on deep neural networks is examined. Additionally, a novel methodology centered on leveraging the capabilities of scikit-learn is designed, developed, and presented. The thesis highlights key findings from each approach, discussing their promises and limitations. Detailed accounts of the encountered obstacles throughout the development pathway are provided, allowing for a comprehensive understanding of the challenges faced in the field of NILM. Furthermore, potential improvements and enhancements are proposed in order to successfully perform the energy disaggregation. In this research, a plethora of machine learning algorithms, both in the categories of classification and regression, are explored as potential solutions for NILM. A collection of solutions proposed in this dissertation - i.e. Random Forest Regression, Gradient Boosting Regression, and SVR - demonstrates a significant potential for advancing the NILM field and energy disaggregation. Moreover, these findings offer promising insights on feasibility of implementation of different approaches as well as the effective- ness of the methods that have been studied in the extent of this dissertatio
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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