Annalen der PhysikVolume 534, Issue 1 2270002 MastheadFree Access Masthead: Ann. Phys. 1/2022 First published: 05 January 2022 https://doi.org/10.1002/andp.202270002AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text full-text accessPlease review our Terms and Conditions of Use check box below share version article.I have read acce...

hardware-implemented ANN presented framework realization: ! 2 ANN ASICs with binary neurons and analog synapses ! implementing a recurrent Perceptron model ! controlled by an FPGA (framework controller) ! trained by an evolutionary algorithm ! capabale of scaling mapping to resources desired network topology We propose a framework for hardware artificial neural networks (ANN) that allows to map...

B. Lemasson, S. Galbán, C. Tsien, C. R. Meyer, T. D. Johnson, T. L. Chenevert, A. Rehemtulla, B. D. Ross, and C. J. Galbán Radiology, University of Michigan, Ann Arbor, Michigan, United States, Radiation Oncology, University of Michigan, Center for Molecular Imaging, Ann Arbor, Michigan, United States, Biomedical, University of Michigan, Center for Molecular Imaging, Ann Arbor, Michigan, United...

The purpose of this study is to assess the ability of the artificial neural network (ANN) models in estimating river ice thickness using easy available climate data. A site specific ANN models were developed for two hydrometric stations at two rivers in Alberta (Canada). The ANN models were found to adequately estimate ice thickness. Ways to improve the performances of the ANN models are proposed.

Archana Kumari,* Alexandre N. Ermilov,* Benjamin L. Allen,* Robert M. Bradley,* Andrzej A. Dlugosz,* and Charlotte M. Mistretta* Department of Biologic and Materials Sciences, School of Dentistry, University of Michigan, Ann Arbor, Michigan; Department of Dermatology, Medical School, University of Michigan, Ann Arbor, Michigan; Department of Molecular and Integrative Physiology, Medical School,...

In this study, a three–layer artificial neural network (ANN) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. A total of 455 data points were collected from 13 data sources to develop the ANN model. Superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of ...

Image Analysis and Computer Simulation of Nanoparticle Clustering in Combustion Systems Y. H. Chen a , S. D. Bakrania a , M. S. Wooldridge a b & A. M. Sastry a c d a Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, USA b Department of Aerospace Engineering, University of Michigan, Ann Arbor, Michigan, USA c Department of Biomedical Engineering, University of Mi...

1 Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, MI, USA, 2 Great Lakes Environmental Research Laboratory, National Oceanic and Atmospheric Administration, Ann Arbor, MI, USA, 3 Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA, 4 Cooperative Institute for Limnology and Ecosystems Research, Ann Arbor, MI, USA, 5 Canada...

a LimnoTech, 501 Avis Drive, Ann Arbor, MI 48108, USA b School of Natural Resources and Environment, University of Michigan, 440 Church St., Ann Arbor, MI 48109, USA c Graham Sustainability Institute, University of Michigan, School of Natural Resources and Environment, 625 East Liberty Road, Ann Arbor, MI 48193, USA d Cooperative Institute for Limnology and Ecosystems Research, School of Natura...

From the University of Rochester, Rochester, New York (Dr G. C. Williams, Dr Patrick, Mr Niemiec); Henry Ford Hospital, Detroit, Michigan (Dr L. K. Williams, Dr Divine, Dr Lafata, Dr Tunceli, Dr Pladevall); Veterans Affairs Center for Clinical Practice Management Research, Ann Arbor Health System, Ann Arbor, Michigan (Dr Heisler); Department of Internal Medicine, University of Michigan, Ann Arb...