Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Ziteng Wen"'
Autor:
Sunandita Sarker, Adira Colton, Ziteng Wen, Xin Xu, Metecan Erdi, Anthony Jones, Peter Kofinas, Eleonora Tubaldi, Piotr Walczak, Miroslaw Janowski, Yajie Liang, Ryan D. Sochol
Publikováno v:
Advanced Materials Technologies. 8:2370021
Publikováno v:
Neurocomputing. 337:67-79
The radial basis function neural network (RBF-NN) integrating Takagi-Sugeno (TS) fuzzy model has been widely used in pattern recognition and intelligence system for its relatively simple structure, good local approximating capability, interpretably a
Publikováno v:
Applied Soft Computing. 76:251-264
A robust fusion algorithm based on Radial Basis Function (RBF) neural network with Takagi–Sugeno (TS) fuzzy model is proposed in view of the data loss, data distortion or signal saturation which is usually occurred in the process of infrared flame
Autor:
Ryan D. Sochol, Michael A. Restaino, Emmett Z. Freeman, Ziteng Wen, Noemi Gonzalez, Ian B. Rosenthal, Ruben Acevedo
Publikováno v:
2021 IEEE 34th International Conference on Micro Electro Mechanical Systems (MEMS).
Additive manufacturing (or "three-dimensional (3D) printing") technologies offer unique means to expand the architectural versatility with which microfluidic systems can be designed and constructed. In particular, "direct laser writing (DLW)" support
Autor:
Jennifer Landry, Abdullah T. Alsharhan, Saul Schaffer, Kristen M. Edwards, Ryan D. Sochol, Joshua D. Hubbard, Kejin Wang, Ziteng Wen, Ruben Acevedo
Publikováno v:
Science Advances
We 3D-print unified soft robots comprising fully integrated fluidic circuitry capable of sophisticated operations.
The emergence of soft robots has presented new challenges associated with controlling the underlying fluidics of such systems. Her
The emergence of soft robots has presented new challenges associated with controlling the underlying fluidics of such systems. Her
Publikováno v:
Electric Power Systems Research. 179:106106
It is of great demand in the power industry to use hourly information to establish the appropriate predictive model and improve the forecasting accuracy in the short/long term load forecasting processes. In this paper, we propose a new load forecasti
Autor:
Hubbard, Joshua D.1,2,3, Acevedo, Ruben2,3, Edwards, Kristen M.2,3,4, Alsharhan, Abdullah T.2, Ziteng Wen2,3, Landry, Jennifer2,3, Kejin Wang2, Schaffer, Saul2,3,5, Sochol, Ryan D.2,3,6,7 rsochol@umd.edu
Publikováno v:
Science Advances. 7/14/2021, Vol. 7 Issue 29, p1-12. 12p.