Search results for "neural net"

Theoretical and experimental study of two discrete coupled Nagumo chains

2001

We analyze front wave (kink and antikink) propagation and pattern formation in a system composed of two coupled discrete Nagumo chains using analytical and numerical methods. In the case of homogeneous interaction among the chains, we show the possibility of the effective control on wave propagation. In addition, physical experiments on electrical chains confirm all theoretical behaviors.

Seizure Prediction Using EEG Channel Selection Method

2022

Seizure prediction using intracranial electroencephalogram (iEEG) is still challenging because of complicated signals in spatial and time domains. Feature selection in the spatial domain (i.e., channel selection) has been largely ignored in this field. Hence, in this paper, a novel approach of iEEG channel selection strategy combined with one-dimensional convolutional neural networks (1D-CNN) was presented for seizure prediction. First, 15-sec and 30-sec iEEG segments with an increasing number of channels (from one channel to all channels) were sequentially fed into 1D-CNN models for training and testing. Then, the channel case with the best classification rate was selected for each partici…

Assessing the Open Trenches in Screening Railway Ground-Borne Vibrations by Means of Artificial Neural Network

2009

Reducing ground borne vibrations in urban areas is a very challenging task in railway transportation. Many mitigation measures can be considered and applied; among these open trenches are very effective. This paper deals with the study of the effect, in terms of reduction of vertical and horizontal displacements and velocities, of the open trenches. 2D FEM simulations have been performed and several open trench configurations have been analysed varying the main geometric features such as width and depth, distance from the rail, thickness of the soil layer over the rigid bedrock, type of the ground, ratio between the depth of the trench, and the thickness of the soil layer. For quantifying t…

Study of the effectiveness of the open trenches in reducing railway ground-borne vibrations: sensitivity analysis of its geometric features using art…

2009

Estimating Programming Exercise Difficulty using Performance Factors Analysis

2020

This Work in Progress Paper studies student and exercise modelling based on pass/fail log data gathered from an introductory programming course. Contemporary education capitalizes on the communications technology and remote study. This can create distance between the teacher and students and the resulting lack of awareness of the difficulties students encounter can lead to low student satisfaction, dropout and poor grades. In many cases, various technological solutions are used to collect individual exercise submissions, but there are little resources for indexing or modelling the exercises in depth. Exercise specific feedback from students may not be easily obtainable either. In the presen…

A CNN Adaptive Model to Estimate PM10 Monitoring

2006

In this work we introduce a model for studying the distribution and control of atmospheric pollution from PM10. The model is based on the use of a cellular neural network (CNN) and more precisely on the integration of the mass-balance equation; at the same time it simulates the scenario regarding a planar grid describing the whole studied area (the city of Palermo) by means of a CNN and a set of Bayesian networks. The CNN allows us to define a grid system whose dynamic evolution is a redefinition of the diffusion equation that considers contributions coming from near cells for each element of the grid. Dynamics of each cell is influenced by meteorological effects and by parameters related t…

Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network

2022

To monitor and handle big data obtained from electrical, electronic, electro-mechanical, and other equipment linked to the power grid effectively and efficiently, it is important to monitor them continually to gather information on power line integrity. We propose that data transmission analysis and data collection from tools like digital power meters may be used to undertake predictive maintenance on power lines without the need for specialized hardware like power line modems and synthetic data streams. Neural network models such as deep learning may be used for power line integrity analysis systems effectively, safely, and reliably. We adopt Q-learning based data analysis network for anal…

PROPAGATING INTERFACES IN A TWO-LAYER BISTABLE NEURAL NETWORK

2006

The dynamics of propagating interfaces in a bistable neural network is investigated. We consider the network composed of two coupled 1D lattices and assume that they interact in a local spatial point (pin contact). The network unit is modeled by the FitzHugh–Nagumo-like system in a bistable oscillator mode. The interfaces describe the transition of the network units from the rest (unexcited) state to the excited state where each unit exhibits periodic sequences of excitation pulses or action potentials. We show how the localized inter-layer interaction provides an "excitatory" or "inhibitory" action to the oscillatory activity. In particular, we describe the interface propagation failure a…

Pinning of a kink in a nonlinear diffusive medium with a geometrical bifurcation: Theory and experiments

2004

International audience; We study the dynamics of a kink propagating in a Nagumo chain presenting a geometrical bifurcation. In the case of weak couplings, we define analytically and numerically the coupling conditions leading to the pinning of the kink at the bifurcation site. Moreover, real experiments using a nonlinear electrical lattice confirm the theoretical and numerical predictions.

Causality-Aware Convolutional Neural Networks for Advanced Image Classification and Generation

2023

Smart manufacturing uses emerging deep learning models, and particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), for different industrial diagnostics tasks, e.g., classification, detection, recognition, prediction, synthetic data generation, security, etc., on the basis of image data. In spite of being efficient for these objectives, the majority of current deep learning models lack interpretability and explainability. They can discover features hidden within input data together with their mutual co-occurrence. However, they are weak at discovering and making explicit hidden causalities between the features, which could be the reason behind the parti…