1,721,095 research outputs found
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
Detection and Classification of Faults in Residential PV Systems with a Synthetic PV Training Database: A Machine Learning-Based Approach Using the PVMD Toolbox to Generate Synthetic PV Yield Data
No full textIn this thesis, a new photovoltaic fault detection and classification method is proposed. It combines the generation of a synthetic photovoltaic training database and the use of a machine learning model to detect and classify faults in small-scale residential PV systems. The database was generated in Matlab, and the machine learning modeling was done with the scikit-learn library for Python. From the modeled PV systems, solely power yield is used as an indicator, combined with system age and meteorological conditions. Using these features, four types of machine learning models are used to detect malfunctioning PV systems and classify short-circuit faults and open-circuit faults. This thesis also shows the benefit of a synthetic PV training base as opposed to alternative methods, with increasing performance due to control of database balance. The result of this thesis is a method that can be used to construct a model for detection and classification of photovoltaic faults, specific to a single residential PV system. Malfunctioning systems can be detected with an accuracy of 80.4% using a random forest algorithm. For fault type classification, an F1-score of 0.759 was achieved, also using a random forest.Electrical Engineering | Sustainable Energy Technolog
Machine learning and digital twins: monitoring and control for dynamic security in power systems
No full textThe reader of the chapter will be able to connect techniques from machine learning (ML) and digital twins (DTs) to gain insights for monitoring and control of (dynamic) security for electrical power systems. DTs are validated and verified high-fidelity (hf) models providing high simulation accuracy. DTs can be used for simulation of the supervised process of system operation and are therefore able to provide synthetic studied data, where measurement data are scarce. However, for some real-time applications in monitoring and control, such high-fidelity simulation models are not appropriate due to the corresponding computational barrier. There, ML aims to create an application-specific, low-fidelity (lf) approximation of the digital twin. Such trained lf models are used in real-time applications where computational time is scarce and lf information is sufficient. The conceptual intersection of hf and lf models has been little explored and becomes increasingly complex. This chapter aims to provide a conceptual overview of how such hf and lf models can be combined. This chapter is split into two parts where the first part is to introduce ML, lf models, and digital twins, hf models, for power systems analysis, and the second chapter is to use these two types of models to form purpose-driven surrogate lf models, illustrated on the example of dynamic security assessment (DSA). In the first part, the concepts for using DTs as hf models for online power system studies and their corresponding tuning of model parameters are introduced. Subsequently, ML i.e., lf models, are introduced and their corresponding training frameworks. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Intelligent Electrical Power Grid
MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response
No full textDistribution System Operators (DSOs) are responsible preventing grid congestion, while accounting for growing demand and the intermittent nature of renewable energy resources. Incentive-based demand response programs promise real-time flexibility to relieve grid congestion. To include residential consumers in these programs, aggregators can financially incentivize participants to reduce their energy demand and make aggregated energy reduction available to DSOs. A key challenge for aggregators is to coordinate heterogeneous preferences from multiple participants while preserving their privacy. This thesis proposes MARL-iDR: a decentralized Multi-Agent Reinforcement Learning approach to an incentive-based demand response program. The approach respects participants' privacy and preferences and makes decisions in real-time when deployed. The aggregator and each participant are controlled by Deep Reinforcement Learning agents that learn to maximize their reward. The aggregator agent learns a policy that dispatches suitable incentives to participants based on total energy demand and a target reduction, while minimizing financial costs. The participant agent learns to respond to these incentives by reducing consumption to a fraction of the original demand. The participant agents curtail or shift requested household appliances based on the selected consumption reduction using a novel Disjunctively Constrained Knapsack Problem optimization, while minimizing residents' dissatisfaction. A case study with real-world electricity data from 25 households demonstrates the capability to induce demand-side flexibility. The approach is compared to the case without demand response and to a centralized myopic baseline approach. A 9% reduction of the Peak-to-Average ratio (PAR) was achieved compared to the original PAR (no demand response)
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
Retrospektive, vergleichende Beobachtungsstudie zur operativen Myokardrevaskularisation bei Patienten mit akutem Myokardinfarkt und kardiogenem Schock
Full text linkDer kardiogene Schock ist nach wie vor die häufigste Todesursache beim akuten Myokardinfarkt. Die Therapie der Wahl ist eine rasche Revaskularisierung durch eine Herzkatheterintervention. Alternativ steht bei frustranem Behandlungsversuch oder einer periinterventionellen Komplikation die chirurgische Bypassoperation zur Verfügung. Gegenwärtig ist allerdings unklar, wann der optimale Operationszeitpunkt dafür vorliegt.
Ziel der Arbeit war es Patienten, die innerhalb von 48 Stunden nach Myokardinfarkt mit komplizierendem kardiogenen Schock eine notfallmäßige aortokoronare Bypass Operation erhielten, hinsichtlich prä-, intra- und postoperativer Risikofaktoren für die 30-Tagesletalität zu untersuchen. Des Weiteren sollte der Frage nachgegangen werden inwiefern sich eine präoperative Reanimation oder unterschiedliche Infarktentitäten auf das Outcome auswirken.
Hierfür wurden 180 Patienten aus dem Infarktregister der Herzchirurgie des Universitätsklinikums Schleswig-Holsteins, Campus Kiel, einer retrospektiven Datenanalyse zugeführt. Jeder dieser Patienten hatte im Zeitraum von 2001 bis 2012 einen akuten Moykardinfarkt mit kardiogenem Schock erlitten und wurde innerhalb von 48 Stunden chirurgisch revaskularisiert.
Die 30-Tagesletalität betrug 27,9 %. Als unabhängige Prädiktoren für die 30-Tagesletalität kristallisierten sich präoperativ ein Alter >75 Jahren, ein Laktat >4 mmol/l und ein Kreatinin >1,5 mg/dl heraus. Postoperativ erhöhten darüber hinaus eine SIRS/Sepsis, die Notwendigkeit einer Dialyse und ein Laktat >8 mmol/l als unabhängige Risikofaktoren die Wahrscheinlichkeit, innerhalb von 30 Tagen zu versterben. Die intraoperative Verwendung eines arteriellen Graft sowie eine komplette Revaskularisierung wirkten sich hingegen positiv auf das 30-Tageüberleben aus.
Unsere Daten sprechen dafür, dass die notfallmäßige Bypassoperation für ausgewählte Patienten, die im kardiogenen Schock nicht interventionell behandelt werden konnten, ein der Koronarintervention vergleichbares Outcome bieten
- …
