Design of RF MEMS Based Oscillatory Neural Network for Ultra High Speed Associative Memories

2013 ◽  
Vol 40 (1) ◽  
pp. 93-102 ◽  
Author(s):  
Masoud Baghelani ◽  
Afshin Ebrahimi ◽  
Habib Badri Ghavifekr
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Rf Mems ◽  
Associative Memories ◽  
Ultra High Speed ◽  
Oscillatory Neural Network

A novel hardware-oriented ultra-high-speed object detection algorithm based on convolutional neural network

2019 ◽  
Vol 17 (5) ◽  
pp. 1703-1714 ◽  
Author(s):  
Jianquan Li ◽  
Xianlei Long ◽  
Shenhua Hu ◽  
Yiming Hu ◽  
Qingyi Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
High Speed ◽  
Detection Algorithm ◽  
Ultra High Speed

Recent Advances in Ultra-High-Speed Waveguide Photodiodes for Optical Communication Systems

2009 ◽  
Vol E92-C (7) ◽  
pp. 922-928 ◽  
Author(s):  
Kikuo MAKITA ◽  
Kazuhiro SHIBA ◽  
Takeshi NAKATA ◽  
Emiko MIZUKI ◽  
Sawaki WATANABE
Keyword(s):  
Optical Communication ◽  
Communication Systems ◽  
High Speed ◽  
Recent Advances ◽  
Ultra High Speed ◽  
Optical Communication Systems

Interior Winding Synchronous Motor for Reducing Iron Loss at Ultra-High-Speed

2020 ◽  
Author(s):  
Ryoken Masuda ◽  
Manabu Horiuchi ◽  
Mitsuhide Sato ◽  
Yinggang Bu ◽  
Masami Nirei ◽  
...  
Keyword(s):  
High Speed ◽  
Synchronous Motor ◽  
Iron Loss ◽  
Ultra High Speed

Visualization of Melt-Flow Behavior Inside the Runner in Ultra High Speed Injection Molding

2006 ◽  
Vol 21 (5) ◽  
pp. 464-472 ◽  
Author(s):  
S. Hasegawa ◽  
H. Yokoi
Keyword(s):  
Injection Molding ◽  
High Speed ◽  
Flow Behavior ◽  
Melt Flow ◽  
Ultra High Speed

Research and implementation of ultra-high-speed parallel sampling technology

2009 ◽  
Vol 2009 (8) ◽  
pp. 67-71 ◽  
Author(s):  
Wuhuang Huang ◽  
Houjun Wang ◽  
Hao Zeng
Keyword(s):  
High Speed ◽  
Ultra High Speed ◽  
Parallel Sampling

Three-Dimensional Imaging through Shock-Waves at Ultra-High Speed.

2018 ◽  
Author(s):  
Yi Chen Mazumdar ◽  
Michael E. Smyser ◽  
Jeffery Dean Heyborne ◽  
Daniel Robert Guildenbecher
Keyword(s):  
Shock Waves ◽  
High Speed ◽  
Three Dimensional ◽  
Three Dimensional Imaging ◽  
Ultra High Speed

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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