Optical production systems using neural networks and symbolic substitution

1988 ◽  
Vol 27 (24) ◽  
pp. 5185 ◽  
Author(s):  
Elizabeth Botha ◽  
David Casasent ◽  
Etienne Barnard
Keyword(s):  
Neural Networks ◽  
Production Systems ◽  
Optical Production ◽  
Symbolic Substitution

Artificial Neural Networks for the analysis of spread⿿mooring configurations for floating production systems

2016 ◽  
Vol 59 ◽  
pp. 254-264 ◽  
Author(s):  
Aline Aparecida de Pina ◽  
Bruno da Fonseca Monteiro ◽  
Carl Horst Albrecht ◽  
Beatriz Souza Leite Pires de Lima ◽  
Breno Pinheiro Jacob
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Production Systems ◽  
Artificial Neural

scholarly journals Methods for maintenance of neural networks in continual learning scenarios

2021 ◽  
Author(s):  
Bhasker Sri Harsha Suri ◽  
Manish Srivastava ◽  
Kalidas Yeturu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Production Systems ◽  
Image Data ◽  
Training Data ◽  
Learning Loss ◽  
Unseen Data ◽  
Scoring Schemes ◽  
The Difference ◽  
Continual Learning

Neural networks suffer from catastrophic forgetting problem when deployed in a continual learning scenario where new batches of data arrive over time; however they are of different distributions from the previous data used for training the neural network. For assessing the performance of a model in a continual learning scenario, two aspects are important (i) to compute the difference in data distribution between a new and old batch of data and (ii) to understand the retention and learning behavior of deployed neural networks. Current techniques indicate the novelty of a new data batch by comparing its statistical properties with that of the old batch in the input space. However, it is still an open area of research to consider the perspective of a deployed neural network’s ability to generalize on the unseen data samples. In this work, we report a dataset distance measuring technique that indicates the novelty of a new batch of data while considering the deployed neural network’s perspective. We propose the construction of perspective histograms which are a vector representation of the data batches based on the correctness and confidence in the prediction of the deployed model. We have successfully tested the hypothesis empirically on image data coming MNIST Digits, MNIST Fashion, CIFAR10, for its ability to detect data perturbations of type rotation, Gaussian blur, and translation. Upon new data, given a model and its training data, we have proposed and evaluated four new scoring schemes, retention score (R), learning score (L), Oscore and SP-score for studying how much the model can retain its performance on past data, how much it can learn new data, the combined expression for the magnitude of retention and learning and stability-plasticity characteristics respectively. The scoring schemes have been evaluated MNIST Digits and MNIST Fashion data sets on different types of neural network architectures based on the number of parameters, activation functions, and learning loss functions, and an instance of a typical analysis report is presented. Machine learning model maintenance is a reality in production systems in the industry, and we hope our proposed methodology offers a solution to the need of the day in this aspect.

Creep Rupture Forecasting

2014 ◽  
Vol 2 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Stylianos Chatzidakis ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Process Model ◽  
Production Systems ◽  
Learning Algorithms ◽  
Difficult Problem ◽  
Network Size ◽  
Creep Rupture ◽  
Machine Learning Algorithms ◽  
Training Data

Creep rupture is becoming increasingly one of the most important problems affecting behavior and performance of power production systems operating in high temperature environments and potentially under irradiation as is the case of nuclear reactors. Creep rupture forecasting and estimation of the useful life is required to avoid unanticipated component failure and cost ineffective operation. Despite the rigorous investigations of creep mechanisms and their effect on component lifetime, experimental data are sparse rendering the time to rupture prediction a rather difficult problem. An approach for performing creep rupture forecasting that exploits the unique characteristics of machine learning algorithms is proposed herein. The approach seeks to introduce a mechanism that will synergistically exploit recent findings in creep rupture with the state-of-the-art computational paradigm of machine learning. In this study, three machine learning algorithms, namely General Regression Neural Networks, Artificial Neural Networks and Gaussian Processes, were employed to capture the underlying trends and provide creep rupture forecasting. The current implementation is demonstrated and evaluated on actual experimental creep rupture data. Results show that the Gaussian process model based on the Matérn kernel achieved the best overall prediction performance (56.38%). Significant dependencies exist on the number of training data, neural network size, kernel selection and whether interpolation or extrapolation is performed.

Methods for maintenance of neural networks in continual learning scenarios

2021 ◽  
Author(s):  
Bhasker Sri Harsha Suri ◽  
Manish Srivastava ◽  
Kalidas Yeturu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Production Systems ◽  
Image Data ◽  
Training Data ◽  
Learning Loss ◽  
Unseen Data ◽  
Scoring Schemes ◽  
The Difference ◽  
Continual Learning

Neural networks suffer from catastrophic forgetting problem when deployed in a continual learning scenario where new batches of data arrive over time; however they are of different distributions from the previous data used for training the neural network. For assessing the performance of a model in a continual learning scenario, two aspects are important (i) to compute the difference in data distribution between a new and old batch of data and (ii) to understand the retention and learning behavior of deployed neural networks. Current techniques indicate the novelty of a new data batch by comparing its statistical properties with that of the old batch in the input space. However, it is still an open area of research to consider the perspective of a deployed neural network’s ability to generalize on the unseen data samples. In this work, we report a dataset distance measuring technique that indicates the novelty of a new batch of data while considering the deployed neural network’s perspective. We propose the construction of perspective histograms which are a vector representation of the data batches based on the correctness and confidence in the prediction of the deployed model. We have successfully tested the hypothesis empirically on image data coming MNIST Digits, MNIST Fashion, CIFAR10, for its ability to detect data perturbations of type rotation, Gaussian blur, and translation. Upon new data, given a model and its training data, we have proposed and evaluated four new scoring schemes, retention score (R), learning score (L), Oscore and SP-score for studying how much the model can retain its performance on past data, how much it can learn new data, the combined expression for the magnitude of retention and learning and stability-plasticity characteristics respectively. The scoring schemes have been evaluated MNIST Digits and MNIST Fashion data sets on different types of neural network architectures based on the number of parameters, activation functions, and learning loss functions, and an instance of a typical analysis report is presented. Machine learning model maintenance is a reality in production systems in the industry, and we hope our proposed methodology offers a solution to the need of the day in this aspect.

Application of neural networks to improve forging technology of crankshaft forgings to ensure their balancibility

2021 ◽  
pp. 549-556
Author(s):  
A.V. Martyugin ◽  
I.M. Volodin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Production Systems ◽  
Forging Process ◽  
Die Forging ◽  
Digital Production ◽  
Trained Neural Network ◽  
Hot Die Forging

The using results of neural network to analyze the balancing of R4 crankshaft forgings using elements of digital production systems based on 3D parametric evaluation of forging imbalance parameters depending on the keywоrd parameters of the forging and the hot die forging process are presented. Specially created and trained neural network is used to approximate the results of the researches. The boundaries of keywоrds parameters of R4 crankshaft forgings are determined based on the analysis. The technological drawing of the forging and all stamping tooling are changed. The solution is implemented into production.

Artificial Neural Networks Applied to Bandsawing Process Control

2014 ◽  
Vol 590 ◽  
pp. 458-462
Author(s):  
Waldemar Gehring Junior ◽  
Fábio Henrique Antunes Vieira ◽  
Carlos Affonso ◽  
Manoel Sampaio Alves ◽  
Marcos Tadeu Gonçalves
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Noise Suppression ◽  
Production Systems ◽  
Autonomous Systems ◽  
Computational Techniques ◽  
Neuro Fuzzy ◽  
Productivity Increase ◽  
Artificial Neural ◽  
Controlling Processes

In the search for productivity increase, industry has invested on the development of intelligent, flexible and self-adjusting method, capable of controlling processes through the assistance of autonomous systems, independently whether they are hardware or software. Notwithstanding, simulating conventional computational techniques is rather challenging, regarding the complexity and non-linearity of the production systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) performs well as noise suppression and treatment of non-linear data. Therefore, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, consequently, add value to the final product. Furthermore, Artificial Intelligence techniques such as Neuro-Fuzzy Networks (NFNs) have proven effective, since NFNs combine the ability to learn from previous examples and generalize the acquired information from the ANNs with the capacity of Fuzzy Logic to transform linguistic variables in rules.

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