Search results for "Neural Networks"

Transient behavior of a population dynamical model

2005

The transient behavior of an ecosystem with N random interacting species in the presence of a multiplicative noise is analyzed. The multiplicative noise mimics the interaction with the environment. We investigate different asymptotic dynamical regimes and the role of the external noise on the probability distribution of the local field.

A neural network clustering algorithm for the ATLAS silicon pixel detector

2014

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. …

Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis

2021

Cardiac fibrosis and other scarring of the heart, arising from conditions ranging from myocardial infarction to ageing, promotes dangerous arrhythmias by blocking the healthy propagation of cardiac excitation. Owing to the complexity of the dynamics of electrical signalling in the heart, however, the connection between different arrangements of blockage and various arrhythmic consequences remains poorly understood. Where a mechanism defies traditional understanding, machine learning can be invaluable for enabling accurate prediction of quantities of interest (measures of arrhythmic risk) in terms of predictor variables (such as the arrangement or pattern of obstructive scarring). In this st…

Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR

2021

In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it into the loss function t…

The Effect of Cycle Time and Offset on Roadside Pollutant Concentrations

2009

Hourly Forecasting of SO2 Pollutant Concentration Using an Elman Neural Network

2006

In this paper the first results produced by an Elman neural network for hourly SO2 ground concentration forecasting are presented. Time series has been recorded between 1998 and 2001 and are referred to a monitoring station of SO2 in the industrial site of Priolo, Syracuse, Italy. Data has been kindly provided by CIPA (Consorzio Industriale per la Protezione dell'Ambiente, Siracusa, Italia). Time series parameters are the horizontal and vertical wind velocity, the wind direction, the stability classes of Thomas, the base level of the layer of the atmospheric stability, the gradient of the potential temperature and the difference of the potential temperature of reference.

Influence of coordinated traffic lights parameters on roadside pollutant concentrations

2009

Abstract The paper examines the effects of coordinated traffic lights on CO and C 6 H 6 roadside concentrations in an urban area of Palermo in Southern Italy. Traffic loop detectors and one pollution-monitoring are used to collect data for use in DRACULA traffic microsimulator software. CO and C 6 H 6 roadside concentrations associated with varying cycle and offset times of the coordinated traffic lights are estimated using a neural network. Two functions were set up describing the relations of pollutant concentrations in term of cycle and offset time.

Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks

2007

The analysis aims to evaluate which is the most important among traffic parameters (flows, queues length, occupancy degree, and travel time) to forecast CO and C6H6 concentrations. The study area was identified by Notarbartolo Road and bounded by Liberta Street and Sciuti Street in the urban area of Palermo in Southern Italy. In this area, various loop detectors and one pollution-monitoring site were located. Traffic data related to the pollution-monitoring site immediately near the road link were estimated by Simulation of Urban MObility (SUMO) traffic microsimulator software using as input the flows measured by loop detectors on other links of road network. Traffic and weather data were u…

Spatial heterogeneity in glassy polystyrene detected by deuteron NMR relaxation

1999

Using deuteron NMR, the dynamics of supercooled polystyrene-d 3 was investigated near the calorimetric glass transition. At these temperatures non-exponential spin lattice relaxation is found, indicating the presence of spatial heterogeneity. With increasing temperature, structural relaxation becomes fast enough to average efficiently over different spatial environments, leading to exponential magnetization decays. A qualitative comparison with toluene as a representative of a low molecular weight glass former is carried out. Indications are found that in polystyrene the observed averaging process is more effective at T g than it is in toluene.

H(II) centers in natural silica under repeated UV laser irradiations

2004

We investigated the kinetics of H(II) centers (=Ge'-H) in natural silica under repeated 266nm UV irradiations performed by a Nd:YAG pulsed laser. UV photons temporarily destroy these paramagnetic defects, their reduction being complete within 250 pulses. After re-irradiation, H(II) centers grow again, and the observed recovery kinetics depends on the irradiation dose; multiple 2000 pulses re-irradiations induce the same post-irradiation kinetics of H(II) centers after each exposure cycle. The analysis of these effects allows us to achieve a deeper understanding of the dynamics of the centers during and after laser irradiation.