AN EXPERIMENTAL APPROACH TO CALIBRATE A FORCE-TORQUE SENSOR USING A NEURAL NETWORK

2019 ◽  
Vol 10 (4) ◽  
pp. 1-12
Author(s):  
Santos Daniel Assis dos ◽  
◽  
Almeida Luis Fernando de ◽  
Soares Álvaro Manoel de Souza ◽  
Gonçalves João Bosco ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Approach ◽  
Torque Sensor

A new design of liquid-cooled heat sink by altering the heat sink heat pipe application: Experimental approach and prediction via artificial neural network

2020 ◽  
Vol 206 ◽  
pp. 112485 ◽  
Author(s):  
Amin Taheri ◽  
Mohammadamir Ghasemian Moghadam ◽  
Majid Mohammadi ◽  
Mohammad Passandideh-Fard ◽  
Mohammad Sardarabadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Pipe ◽  
Heat Sink ◽  
Experimental Approach ◽  
Artificial Neural

scholarly journals Artificial Neural Network for Healthy Chicken Meat Identification

2018 ◽  
Vol 7 (1) ◽  
pp. 63
Author(s):  
Fajar Yumono ◽  
Imam Much Ibnu Subroto ◽  
Sri Arttini Dwi Prasetyowati
Keyword(s):  
Neural Network ◽  
Texture Analysis ◽  
Experimental Approach ◽  
Color Change ◽  
Chicken Meat ◽  
Color Analysis ◽  
Canny Edge ◽  
The Right ◽  
Very High ◽  
Better Than

Indonesia is the country with the largest number of Muslims in the world. Every Muslim is taught to consume thoyyiban halal meat or healthy chicken because it is slaughtered in the right way and stored in a good way too. But the reality in the market of many chicken meat on the market can not meet that criteria. Identification of healthy chicken meat can be done with laboratory experiments, but that is not simple and takes time. This experiment offers a cheaper, faster approach, with very high accuracy. The experimental approach is based on color and texture analysis on 5 types of meat quality based on healthy value. Color analysis was performed using artificail neural network (ANN) while texture analysis used Canny edge detection. Experimental results show that the color histogram approach with ANN is better than the texture approach, ie 94% versus 66%. It can be concluded that the freshness of a chicken does not have much effect on the texture of the meat but it has an effect on the color change in the meat.

Experimental Identification of a Flow Orifice Using a Neural Network and the Conjugate Gradient Method

1996 ◽  
Vol 118 (2) ◽  
pp. 272-277 ◽  
Author(s):  
X. P. Xu ◽  
R. T. Burton ◽  
C. M. Sargent
Keyword(s):  
Neural Network ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Experimental Approach ◽  
Stable Solution ◽  
Training Algorithm ◽  
The Neural Network ◽  
Trained Neural Network ◽  
Flow Valve

An experimental approach of using a neural network model to identifying a nonlinear non-pressure-compensated flow valve is described in this paper. The conjugate gradient method with Polak-Ribiere formula is applied to train the neural network to approximate the nonlinear relationships represented by noisy data. The ability of the trained neural network to reproduce and to generalize is demonstrated by its excellent approximation of the experimental data. The training algorithm derived from the conjugate gradient method is shown to lead to a stable solution.

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