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Institution

Aalborg University

EducationAalborg, Denmark
About: Aalborg University is a education organization based out in Aalborg, Denmark. It is known for research contribution in the topics: Population & Wind power. The organization has 14395 authors who have published 45630 publications receiving 1257866 citations. The organization is also known as: AAU & Aalborg Universitet.


Papers
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Journal ArticleDOI
Theo Vos1, Christine Allen1, Megha Arora1, Ryan M Barber1  +696 moreInstitutions (260)
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) as discussed by the authors was used to estimate the incidence, prevalence, and years lived with disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015.

5,050 citations

Journal ArticleDOI
Haidong Wang1, Mohsen Naghavi1, Christine Allen1, Ryan M Barber1  +841 moreInstitutions (293)
TL;DR: The Global Burden of Disease 2015 Study provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015, finding several countries in sub-Saharan Africa had very large gains in life expectancy, rebounding from an era of exceedingly high loss of life due to HIV/AIDS.

4,804 citations

Journal ArticleDOI
TL;DR: An overview of the structures for the DPGS based on fuel cell, photovoltaic, and wind turbines is given and the possibility of compensation for low-order harmonics is discussed.
Abstract: Renewable energy sources like wind, sun, and hydro are seen as a reliable alternative to the traditional energy sources such as oil, natural gas, or coal. Distributed power generation systems (DPGSs) based on renewable energy sources experience a large development worldwide, with Germany, Denmark, Japan, and USA as leaders in the development in this field. Due to the increasing number of DPGSs connected to the utility network, new and stricter standards in respect to power quality, safe running, and islanding protection are issued. As a consequence, the control of distributed generation systems should be improved to meet the requirements for grid interconnection. This paper gives an overview of the structures for the DPGS based on fuel cell, photovoltaic, and wind turbines. In addition, control structures of the grid-side converter are presented, and the possibility of compensation for low-order harmonics is also discussed. Moreover, control strategies when running on grid faults are treated. This paper ends up with an overview of synchronization methods and a discussion about their importance in the control

4,655 citations

Book
01 Jan 2001
TL;DR: The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms.
Abstract: Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. present a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book.

4,566 citations

Journal ArticleDOI
Theo Vos1, Ryan M Barber1, Brad Bell1, Amelia Bertozzi-Villa1  +686 moreInstitutions (287)
TL;DR: In the Global Burden of Disease Study 2013 (GBD 2013) as mentioned in this paper, the authors estimated the quantities for acute and chronic diseases and injuries for 188 countries between 1990 and 2013.

4,510 citations


Authors

Showing all 14624 results

NameH-indexPapersCitations
Gregory Y.H. Lip1693159171742
Gang Chen1673372149819
Jens Nielsen1491752104005
Frede Blaabjerg1472161112017
Tomas Ganz14148073316
Anne Tjønneland139134591556
Kim Overvad139119686018
Rasmus Nielsen13555684898
Torben Jørgensen13588386822
Charis Eng13075464878
Michael Wagner12435154251
Henrik Toft Sørensen120159174943
Lars Arendt-Nielsen118141059474
Jørgen Christensen-Dalsgaard11458548272
Lars Køber114115577298
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023114
2022434
20213,494
20203,371
20193,251
20183,175