scholarly journals Neural-network-assisted in situ processing monitoring by speckle pattern observation

2020 ◽  
Vol 28 (18) ◽  
pp. 26180
Author(s):  
Shuntaro Tani ◽  
Yutsuki Aoyagi ◽  
Yohei Kobayashi
Keyword(s):  
Neural Network ◽  
Speckle Pattern ◽  
In Situ Processing

scholarly journals Low-Latency In Situ Image Analytics With FPGA-Based Quantized Convolutional Neural Network

2021 ◽  
pp. 1-14
Author(s):  
Maolin Wang ◽  
Kelvin C. M. Lee ◽  
Bob M. F. Chung ◽  
Sharatchandra Varma Bogaraju ◽  
Ho-Cheung Ng ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Latency

In-situ capture of melt pool signature in selective laser melting using U-Net-based convolutional neural network

2021 ◽  
Vol 68 ◽  
pp. 347-355
Author(s):  
Qihang Fang ◽  
Zhenbiao Tan ◽  
Hui Li ◽  
Shengnan Shen ◽  
Sheng Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Selective Laser Melting ◽  
Laser Melting ◽  
Melt Pool

An Adaptive Elasticity Policy For Staging Based In-Situ Processing

2021 ◽  
Author(s):  
Zhe Wang ◽  
Matthieu Dorier ◽  
Pradeep Subedi ◽  
Philip E. Davis ◽  
Manish Parashar
Keyword(s):  
In Situ Processing ◽  
Adaptive Elasticity

Mechanically Aspirated Solar Radiation Shields: A CFD and Neural Network Design Analysis

2001 ◽  
Author(s):  
B. M. Fichera ◽  
R. L. Mahajan ◽  
T. W. Horst
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Temperature Sensor ◽  
Temperature Measurements ◽  
Analysis And Design ◽  
Computational Fluid Dynamics Analysis ◽  
Radiation Shields ◽  
Input Variables ◽  
The Relationship

Abstract Accurate air temperature measurements made by surface meteorological stations are demanded by climate research programs for various uses. Heating of the temperature sensor due to inadequate coupling with the environment can lead to significant errors. Therefore, accurate in-situ temperature measurements require shielding the sensor from exposure to direct and reflected solar radiation, while also allowing the sensor to be brought into contact with atmospheric air at the ambient temperature. The difficulty in designing a radiation shield for such a temperature sensor lies in satisfying these two conditions simultaneously. In this paper, we perform a computational fluid dynamics analysis of mechanically aspirated radiation shields (MARS) to study the effect of geometry, wind speed, and interplay of multiple heat transfer processes. Finally, an artificial neural network model is developed to learn the relationship between the temperature error and specified input variables. The model is then used to perform a sensitivity analysis and design optimization.

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