Search results for "EURA"

Increasing sample efficiency in deep reinforcement learning using generative environment modelling

2020

Road Detection for Reinforcement Learning Based Autonomous Car

2020

Human mistakes in traffic often have terrible consequences. The long-awaited introduction of self-driving vehicles may solve many of the problems with traffic, but much research is still needed before cars are fully autonomous.In this paper, we propose a new Road Detection algorithm using online supervised learning based on a Neural Network architecture. This algorithm is designed to support a Reinforcement Learning algorithm (for example, the standard Proximal Policy Optimization or PPO) by detecting when the car is in an adverse condition. Specifically, the PPO gets a penalty whenever the virtual automobile gets stuck or drives off the road with any of its four wheels.Initial experiments …

Efficient pruning of multilayer perceptrons using a fuzzy sigmoid activation function

2006

This Letter presents a simple and powerful pruning method for multilayer feed forward neural networks based on the fuzzy sigmoid activation function presented in [E. Soria, J. Martin, G. Camps, A. Serrano, J. Calpe, L. Gomez, A low-complexity fuzzy activation function for artificial neural networks, IEEE Trans. Neural Networks 14(6) (2003) 1576-1579]. Successful performance is obtained in standard function approximation and channel equalization problems. Pruning allows to reduce network complexity considerably, achieving a similar performance to that obtained by unpruned networks.

Educational Software Based on Matlab GUIs for Neural Networks Courses

2016

Neural Networks (NN) are one of the most used machine learning techniques in different areas of knowledge. This has led to the emergence of a large number of courses of Neural Networks around the world and in areas where the users of this technique do not have a lot of programming skills. Current software that implements these elements, such as Matlab®, has a number of important limitations in teaching field. In some cases, the implementation of a MLP requires a thorough knowledge of the software and of the instructions that train and validate these systems. In other cases, the architecture of the model is fixed and they do not allow an automatic sweep of the parameters that determine the a…

Classical Training Methods

2006

This chapter reviews classical training methods for multilayer neural networks. These methods are widely used for classification and function modelling tasks. Nevertheless, they show a number of flaws or drawbacks that should be addressed in the development of such systems. They work by searching the minimum of an error function which defines the optimal behaviour of the neural network. Different standard problems are used to show the capabilities of these models; in particular, we have benchmarked the algorithms in a nonlinear classification problem and in three function modelling problems.

The use of artificial intelligence techniques to optimise and control injection moulding processes

1999

In the paper a typical injection moulding process on a single-screw extrusion machine aimed to the production of axisymmetric polypropylene dishes for alimentary use has been investigated. First of all the most important process parameters have been individuated; subsequently a wide testing hyperspace has been investigated, at varying the process parameters in a large range. For each combination both some geometrical characteristics of the obtained component have been measured and the occurrence of defects has been verified. The largest part of the available data have been used to train a neural network aimed to explain the process dynamics. Furthermore an offline control system, based on f…

Cloud screening with combined MERIS and AATSR images

2009

This paper presents a cloud screening algorithm based on ensemble methods that exploits the combined information from both MERIS and AATSR instruments on board ENVISAT in order to improve current cloud masking products for both sensors. The first step is to analyze the synergistic use of MERIS and AATSR images in order to extract some physically-based features increasing the separability of clouds and surface. Then, several artificial neural networks are trained using different sets of input features and different sets of training samples depending on acquisition and surface conditions. Finally, outputs of the trained neural networks are combined at the decision level to construct a more ac…

Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images

2021

Abstract Losses of electricity production in photovoltaic systems are mainly caused by the presence of faults that affect the efficiency of the systems. The identification of any overheating in a photovoltaic module, through the thermographic non-destructive test, may be essential to maintain the correct functioning of the photovoltaic system quickly and cost-effectively, without interrupting its normal operation. This work proposes a system for the automatic classification of thermographic images using a convolutional neural network, developed via open-source libraries. To reduce image noise, various pre-processing strategies were evaluated, including normalization and homogenization of pi…

Pose classification using support vector machines

2000

In this work a software architecture is presented for the automatic recognition of human arm poses. Our research has been carried on in the robotics framework. A mobile robot that has to find its path to the goal in a partially structured environment can be trained by a human operator to follow particular routes in order to perform its task quickly. The system is able to recognize and classify some different poses of the operator's arms as direction commands like "turn-left", "turn-right", "go-straight", and so on. A binary image of the operator silhouette is obtained from the gray-level input. Next, a slice centered on the silhouette itself is processed in order to compute the eigenvalues …

Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study

2019

Abstract Approximately 40% of the European energy consumption and a large proportion of environmental impacts are related to the building sector. However, the selection of adequate and correct designs can provide considerable energy savings and reduce environmental impacts. To achieve this objective, a simultaneous energy and environmental assessment of a building's life cycle is necessary. To date, the resolution of this complex problem is entrusted to numerous software and calculation algorithms that are often complex to use. They involve long diagnosis phases and are characterised by the lack of a common language. Despite the efforts by the scientific community in the building sector, th…