An Artificial Neural Network Model for Chilling Environment Control in Meat Production

2015 ◽  
Vol 781 ◽  
pp. 479-482
Author(s):  
Apirachai Wongsriworaphon ◽  
Arthit Apichottanakul ◽  
Teerawat Laonapakul ◽  
Supachai Pathumnakul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Weight Loss ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Learning Algorithm ◽  
Meat Production ◽  
Weight Class ◽  
Environment Control ◽  
Artificial Neural

In this study, artificial neural network with a supervised learning algorithm called vector-quantized temporal associative memory (VQTAM) is proposed to estimate chilled weight loss during chilling process of pig slaughtering plant. Four models based on carcass weights are developed. The results show that the proposed algorithms can accurately predict chilled weight loss with an error rate of less than 5% on average. The models are also employed to determine the suitable chilling times for each weight class.

scholarly journals Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3042
Author(s):  
Sheng Jiang ◽  
Mansour Sharafisafa ◽  
Luming Shen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Strain Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Rock Strength ◽  
Strain Rates ◽  
Filling Material ◽  
Artificial Neural

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

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