Health Monitoring of Complex Systems using Parallel Neural Networks

2006 ◽  
Author(s):  
H. Marzi
Keyword(s):  
Neural Networks ◽  
Complex Systems ◽  
Health Monitoring

scholarly journals Emotion Detection using Facial Expressions with Convolution Neural Networks

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3612-3615
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Facial Expressions ◽  
Health Monitoring ◽  
Human Condition ◽  
Emotion Detection ◽  
Human Environment ◽  
Interaction Field ◽  
Research Theme ◽  
Artificial Intelligence Systems

Artificial intelligence systems to perceive human feeling have pulled in much research premium, and potential uses of such frameworks flourish, spreading over areas, for example, client mindful showcasing, health monitoring wellbeing observing, and genuinely shrewd robotic interfaces. Human are enthusiastic creatures and it assumes a significant job behind their thoughts and activity. In this way, it is important that emotion handling capacities are assimilated for planning of human condition. The investigation, recognition and synthesis of feelings can plan the human environment. In this procedure the data uses, for example, sound, visual, composed and mental data. An epic research theme to be developed in the Human Computer Interaction field is Emotion Recognition utilizing Facial Expressions.

Modularity in Artificial Neural Networks

2011 ◽  
pp. 1095-1101
Author(s):  
Ricardo Téllez ◽  
Cecilio Angulo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Complex Systems ◽  
Intelligent Systems ◽  
Point Of View ◽  
Main Concern ◽  
Complex Structures ◽  
Computational Point ◽  
Points Of View

The concept of modularity is a main concern for the generation of artificially intelligent systems. Modularity is an ubiquitous organization principle found everywhere in natural and artificial complex systems (Callebaut, 2005). Evidences from biological and philosophical points of view (Caelli and Wen, 1999) (Fodor, 1983), indicate that modularity is a requisite for complex intelligent behaviour. Besides, from an engineering point of view, modularity seems to be the only way for the construction of complex structures. Hence, whether complex neural programs for complex agents are desired, modularity is required. This article introduces the concepts of modularity and module from a computational point of view, and how they apply to the generation of neural programs based on modules. Two levels, strategic and tactical, at which modularity can be implemented, are identified. How they work and how they can be combined for the generation of a completely modular controller for a neural network based agent is presented.

The Role of Uncertainty in Machine Learning As an Element of Control for Material Systems and Structures

2018 ◽  
Author(s):  
Muthu Ram Prabhu Elenchezhian ◽  
Md Rassel Raihan ◽  
Kenneth Reifsnider
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Composite Material ◽  
Health Monitoring ◽  
Crack Density ◽  
Regression Function ◽  
Fatigue Loading ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Material State

Recurrent neural networks (RNN) have been used to interpret data in situations wherein our knowledge of the active physics is incomplete. The currency of these methods is the data that are generated by a physical system. Unfortunately, if we are uncertain about the physics of the system, we also do not know the level of uncertainty in the data that we use to represent it. Typically, data provided to an RNN is provided by measurements of system state information, e.g., data that define speed, position, accelerations, configurations of system elements (like the flaps and elevators on an airplane) etc. But recently, data are being collected that indicate the state of the materials themselves that are used to construct the system. Material state may include the defect state of the materials such as the crack density and patterns in composite material in structural elements (obtained from health monitoring data). In this paper, we address the question of teaching a control system (e.g., for testing equipment, aircraft control systems, health monitoring systems, etc.) to recognize composite material degradation during service and to adjust applied loads and fields as part of a control scheme to avoid failure of the material during service. Topics will include defining a proper cost function for the above objectives, formulation of a ‘failure hypothesis’ as a regression function, and the quantification of uncertainty when the physics of the situation is not completely defined. Examples of machine learning techniques for a uniaxial fatigue loading of composite coupons with a circular hole are presented. Example models are random forest regression algorithms and artificial neural networks for linear regression.

scholarly journals A variational Bayesian neural network for structural health monitoring and cost-informed decision-making in miter gates

2020 ◽  
pp. 147592172090454 ◽  
Author(s):  
Manuel A Vega ◽  
Michael D Todd
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Informed Decision ◽  
Informed Decision Making ◽  
Bayesian Neural Network ◽  
Bayesian Neural Networks ◽  
Structural Health

Many physics-based and surrogate models used in structural health monitoring are affected by different sources of uncertainty such as model approximations and simplified assumptions. Optimal structural health monitoring and prognostics are only possible with uncertainty quantification that leads to an informed course of action. In this article, a Bayesian neural network using variational inference is applied to learn a damage feature from a high-fidelity finite element model. Bayesian neural networks can learn from small and noisy data sets and are more robust to overfitting than artificial neural networks, which make it very suitable for applications such as structural health monitoring. Also, uncertainty estimates obtained from a trained Bayesian neural network model are used to build a cost-informed decision-making process. To demonstrate the applicability of Bayesian neural networks, an example of this approach applied to miter gates is presented. In this example, a degradation model based on real inspection data is used to simulate the damage evolution.

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