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02/2018
The utility model provides a weisheng attitude machine vision device, including shell, lighting module, image sensor and camera lens, the camera lens is settled on image sensor, and image sensor is fixed in on the... 
TESTING | INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES | MEASURING | PHYSICS
Patent
02/2018
The utility model provides a pair of weisheng attitude check out test set, including shell, flip, LED pilot lamp, machine vision device and incubation device, the LED pilot lamp is located shell... 
TESTING | INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES | MEASURING | PHYSICS
Patent
Journal of dynamics and differential equations, ISSN 1040-7294, 6/2018, Volume 30, Issue 2, pp. 501 - 519
...J Dyn Diff Equat (2018) 30:501–519 https://doi.org/10.1007/s10884-016-9556-0 On Non-dense Orbits of Certain Non-algebraic Dynamical Systems Weisheng Wu 1... 
Schmidt’s game | Hausdorff dimension | Ordinary Differential Equations | Mathematics | Applications of Mathematics | Non-dense orbits | Expanding endomorphism | Anosov diffeomorphism | Partial Differential Equations | Physical Sciences | Mathematics, Applied | Science & Technology | Orbits | Dynamical systems | Algebra
Journal Article
Energy (Oxford), ISSN 0360-5442, 10/2016, Volume 113, pp. 702 - 712
To deal with increasing energy consumption in the residential sector, distributed energy resources including photovoltaic (PV), fuel cell, etc. have been paid... 
Battery bank | Fuel cell | Optimization model | Hybrid energy system | Thermodynamics | Physical Sciences | Technology | Energy & Fuels | Science & Technology | Energy consumption | Fuel cells | Green technology | Batteries | Environmental auditing | Mechanical engineering | Natural gas
Journal Article
by Yue, Feng and Cheng, Yong and Breschi, Alessandra and Vierstra, Jeff and Wu, Weisheng and Ryba, Tyrone and Sandstrom, Richard and Ma, Zhihai and Davis, Carrie and Pope, Benjamin D and Shen, Yin and Pervouchine, Dmitri D and Djebali, Sarah and Thurman, Robert E and Kaul, Rajinder and Rynes, Eric and Kirilusha, Anthony and Marinov, Georgi K and Williams, Brian A and Trout, Diane and Amrhein, Henry and Fisher-Aylor, Katherine and Antoshechkin, Igor and DeSalvo, Gilberto and See, Lei-Hoon and Fastuca, Meagan and Drenkow, Jorg and Zaleski, Chris and Dobin, Alex and Prieto, Pablo and Lagarde, Julien and Bussotti, Giovanni and Tanzer, Andrea and Denas, Olgert and Li, Kanwei and Bender, M. A and Zhang, Miaohua and Byron, Rachel and Groudine, Mark T and McCleary, David and Pham, Long and Ye, Zhen and Kuan, Samantha and Edsall, Lee and Wu, Yi-Chieh and Rasmussen, Matthew D and Bansal, Mukul S and Kellis, Manolis and Keller, Cheryl A and Morrissey, Christapher S and Mishra, Tejaswini and Jain, Deepti and Dogan, Nergiz and Harris, Robert S and Cayting, Philip and Kawli, Trupti and Boyle, Alan P and Euskirchen, Ghia and Kundaje, Anshul and Lin, Shin and Lin, Yiing and Jansen, Camden and Malladi, Venkat S and Cline, Melissa S and Erickson, Drew T and Kirkup, Vanessa M and Learned, Katrina and Sloan, Cricket A and Rosenbloom, Kate R and Lacerda de Sousa, Beatriz and Beal, Kathryn and Pignatelli, Miguel and Flicek, Paul and Lian, Jin and Kahveci, Tamer and Lee, Dongwon and James Kent, W and Ramalho Santos, Miguel and Herrero, Javier and Notredame, Cedric and Johnson, Audra and Vong, Shinny and Lee, Kristen and Bates, Daniel and Neri, Fidencio and Diegel, Morgan and Canfield, Theresa and Sabo, Peter J and Wilken, Matthew S and Reh, Thomas A and Giste, Erika and Shafer, Anthony and Kutyavin, Tanya and Haugen, Eric and Dunn, Douglas and Reynolds, Alex P and Neph, Shane and Humbert, Richard and Scott Hansen, R and De Bruijn, Marella and ... and The Mouse ENCODE Consortium and Mouse ENCODE Consortium
Nature (London), ISSN 1476-4687, 11/2014, Volume 515, Issue 7527, pp. 355 - 364
Journal Article
Neurocomputing (Amsterdam), ISSN 0925-2312, 02/2017, Volume 224, pp. 184 - 194
This paper aims to develop distributed learning algorithms for feedforward neural networks with random weights (FNNRWs) by using event-triggered communication... 
Distributed optimization | Distributed learning | Event-triggered communication | Consensus | Neural networks with random weights | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology | Algorithms | Numerical analysis | Data mining | Neural networks
Journal Article