Journal ArticleDOI
Prediction of wind pressure coefficients on building surfaces using artificial neural networks
TLDR
A computational modeling approach to accurately predict the mean wind pressure coefficient on the surfaces of flat-, gable- and hip-roofed rectangular buildings using artificial neural network to estimate the surface-average pressure coefficient according to the building geometry and the wind angle is presented.About:
This article is published in Energy and Buildings.The article was published on 2018-01-01. It has received 129 citations till now. The article focuses on the topics: Pressure coefficient & Natural ventilation.read more
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Short-term wind speed prediction model based on GA-ANN improved by VMD
TL;DR: Variational mode decomposition (VMD) algorithm can use VMD to decompose the wind speed signal to obtain different scale fluctuations or trends, so as to fully exploit the potential information of wind speed, and obtain more accurate prediction results.
Journal ArticleDOI
Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination
TL;DR: It was concluded that the use of AI tools will undoubtedly pave the way in the water sector towards better operation, process automation, and water resources management in an increasingly volatile environment.
Journal ArticleDOI
Deep learning-based investigation of wind pressures on tall building under interference effects
TL;DR: In this article, the GANs model exhibited the best performance in predicting wind pressure coefficients on the principal building and was used to resolve the conflicting requirement between limited wind tunnel tests and a completed investigation of the interference effects that is costly.
Journal ArticleDOI
Wind speed prediction with RBF neural network based on PCA and ICA
TL;DR: The research work in the paper can help wind farm reasonably arrange the power dispatching plan, reduce the power operation cost and effectively boost the large-scale development and utilization of renewable energy.
Journal ArticleDOI
Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review.
TL;DR: In this article, the authors provided the first narrative deep learning review by considering all facets of image classification using AI and employed a PRISMA search strategy using Google Scholar, PubMed, IEEE, and Elsevier Science Direct, through which 127 relevant HDL studies were considered.
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Posted Content
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi,Ashish Agarwal,Paul Barham,Eugene Brevdo,Zhifeng Chen,Craig Citro,Greg S. Corrado,Andy Davis,Jeffrey Dean,Matthieu Devin,Sanjay Ghemawat,Ian Goodfellow,Andrew Harp,Geoffrey Irving,Michael Isard,Yangqing Jia,Rafal Jozefowicz,Lukasz Kaiser,Manjunath Kudlur,Josh Levenberg,Dan Mané,Rajat Monga,Sherry Moore,Derek G. Murray,Chris Olah,Mike Schuster,Jonathon Shlens,Benoit Steiner,Ilya Sutskever,Kunal Talwar,Paul A. Tucker,Vincent Vanhoucke,Vijay K. Vasudevan,Fernanda B. Viégas,Oriol Vinyals,Pete Warden,Martin Wattenberg,Martin Wicke,Yuan Yu,Xiaoqiang Zheng +39 more
TL;DR: The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.
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Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
Book
Neural Networks And Learning Machines
TL;DR: Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together.
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EnergyPlus: creating a new-generation building energy simulation program
Drury B. Crawley,Linda K. Lawrie,F.C. Winkelmann,Walter F. Buhl,Y.Joe Huang,C.O. Pedersen,Richard Karl Strand,R.J. Liesen,Daniel E. Fisher,Michael J. Witte,Jason Glazer +10 more
TL;DR: EnergyPlus as discussed by the authors is a building energy simulation tool that includes a number of innovative simulation features such as variable time steps, user-configurable modular systems that are integrated with a heat and mass balance-based zone simulation, and input and output data structures tailored to facilitate third party module and interface development.