Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining

Artificial neural network (ANN) is widely applied as a predictive tool to solve complex problems. The performance of an ANN model is significantly affected by the applied architectural parameters such as the node number in a hidden layer, which is largely determined by the complexity of cases, the quality of the dataset, and the sufficiency of variables. In the present study, the impact of variation/response space complexity and variable completeness on backpropagation (BP) ANN model establishment was investigated, with a steel ladle lining from secondary steel metallurgy as the case study. The variation dataset for analysis comprised 160 lining configurations of ten variables. Thermal and thermomechanical responses were obtained via finite element (FE) modeling with elastic material behavior. Guidelines were proposed to define node numbers in the hidden layer for each response as a function of the node number in the input layer weighted with the percent value of the significant variables contributing above 90% to the response, as well as the node number in the output layer. The minimum numbers of input variables required to achieve acceptable prediction performance were three, five, and six for the maximum compressive stress, the end temperature, and the maximum tensile stress.

Download Full-text

An IIoT-based architecture for decision support in the aeronautic industry

MATEC Web of Conferences ◽

10.1051/matecconf/201930404004 ◽

2019 ◽

Vol 304 ◽

pp. 04004

Author(s):

Roberto Vita ◽

Narciso Caldas ◽

João Basto ◽

Symone Alcalá ◽

Flavio Diniz

Keyword(s):

Decision Support ◽

Manufacturing Systems ◽

Analytical Framework ◽

Current Status ◽

Predictive Tool ◽

Simulation And Optimization ◽

Performance Improvements ◽

Equipment Failures ◽

The Impact

The Industry 4.0 movement is driving innovation in manufacturing through the application of digital technologies, leading to solid performance improvements. In this context, this paper introduces a real-time analytical framework based on predictive, simulation and optimization technologies applied to decision support in manufacturing systems, enabled by an underlying reference implementation of an open Industrial Internet of Things (IIoT) platform. This architecture integrates critical equipment, manufacturing and corporate systems through a Unified IIoT Cloud Platform. A real case study on the aeronautic industry demonstrates the proposal feasibility of this architecture to enhance productivity, predict equipment failures and bring agility to react to unexpected events. In this case study, the monitoring tool displays the current status of the critical resources and the predictive tool calculates a probability of failure. When this probability reaches a certain threshold, the simulation tool is triggered to evaluate the impact of the disruption in the system’s productivity. Results from the tools are displayed online through an alert system so that each stakeholder is informed timely and in a contextualized way.

Download Full-text

An Improved Artificial Neural Network Model for Effective Diabetes Prediction

Complexity ◽

10.1155/2021/5525271 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Muhammad Mazhar Bukhari ◽

Bader Fahad Alkhamees ◽

Saddam Hussain ◽

Abdu Gumaei ◽

Adel Assiri ◽

...

Keyword(s):

Neural Network ◽

Mean Squared Error ◽

Machine Intelligence ◽

Large Set ◽

Ann Model ◽

Medical Discipline ◽

Artificial Neural ◽

Validation Set ◽

Hidden Layer ◽

The Impact

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP-SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes.

Download Full-text