Search results for "Neural"

Stochastic models for wind speed forecasting

2011

Abstract This paper is concerned with the problem of developing a general class of stochastic models for hourly average wind speed time series. The proposed approach has been applied to the time series recorded during 4 years in two sites of Sicily, a region of Italy, and it has attained valuable results in terms both of modelling and forecasting. Moreover, the 24 h predictions obtained employing only 1-month time series are quite similar to those provided by a feed-forward artificial neural network trained on 2 years data.

Disorder and interactions in systems out of equilibrium : the exact independent-particle picture from density functional theory

2017

Density functional theory (DFT) exploits an independent-particle-system construction to replicate the densities and current of an interacting system. This construction is used here to access the exact effective potential and bias of non-equilibrium systems with disorder and interactions. Our results show that interactions smoothen the effective disorder landscape, but do not necessarily increase the current, due to the competition of disorder screening and effective bias. This puts forward DFT as a diagnostic tool to understand disorder screening in a wide class of interacting disordered systems.

Integrated fuzzy classification

2003

Improvement of Temperature Based ANN Models for ETo Prediction in Coastal Locations by Means of Preliminary Models and Exogenous Data

2008

This paper reports the application of artificial neural networks for estimating reference evapotranspiration (ETo) as a function of local maximum and minimum air temperatures and exogenous relative humidity and evapotranspiration in twelve coastal locations of the autonomous Valencia region, Spain. The Penman-Monteith model for ETo prediction, as been proposed by the Food and Agriculture Organization of the United Nations (FAO) as the standard method for ETo forecast, has been used to provide the ANN targets. The number of stations where reliable climatic data are available for the application of the Penman-Monteith equation is limited. Thus, the development of more precise predicting tools…

Changing paradigm in mild traumatic brain injury research

2016

Traumatic brain injury (TBI) is a major cause of death and disability among young adults. Recent data show that TBI affects about 1.7 million people annually in the United States (Faul and Coronado, 2015). After TBI, the primary injury produces almost irreparable brain damage. However, recent experimental studies have shown evidence for dynamic brain repair following TBI because endogenous progenitor cells may play regenerative roles in response to injuries (McGinn and Povlishock, 2015). In surviving patients, what plays a critical role in the clinical prognosis is the subsequent secondary injury; without effective treat- ment, cascades that include glutamatergic excitotoxicity and calcium …

The on-line curvilinear component analysis (onCCA) for real-time data reduction

2015

Real time pattern recognition applications often deal with high dimensional data, which require a data reduction step which is only performed offline. However, this loses the possibility of adaption to a changing environment. This is also true for other applications different from pattern recognition, like data visualization for input inspection. Only linear projections, like the principal component analysis, can work in real time by using iterative algorithms while all known nonlinear techniques cannot be implemented in such a way and actually always work on the whole database at each epoch. Among these nonlinear tools, the Curvilinear Component Analysis (CCA), which is a non-convex techni…

Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks

2020

In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. While these planes are used for transportation they could similarly be used for remote sensing applications by adding sensors to the planes. Hyperspectral imagers are one this kind of sensor types. There is need for the efficient methods to interpret hyperspectral data to the wanted water quality parameters. In this work we survey the performance of neural networks in the prediction of water quality parameters from remotely sensed hyperspectral data in freshwater basin…

A Mutually Stimulating Loop Involving Emx2 and Canonical Wnt Signalling Specifically Promotes Expansion of Occipital Cortex and Hippocampus

2005

The correct size of the different areas composing the mature cerebral cortex depends on the proper early allocation of cortical progenitors to their distinctive areal fates, as well as on appropriate subsequent tuning of their area-specific proliferation--differentiation profiles. Whereas much is known about the genetics of the former process, the molecular mechanisms regulating proliferation and differentiation rates within distinctive cortical proto-areas are still largely obscure. Here we show that a mutual stimulating loop, involving Emx2 and canonical Wnt signalling, specifically promotes expansion of the occipito-hippocampal anlage. Collapse of this loop occurring in Emx2 2/2 mutants …

Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality

2015

A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments perfo…

Semantic and action tool knowledge in the brain: Identifying common and distinct networks.

2021

Most cognitive models of apraxia assume that impaired tool use results from a deficit occurring at the conceptual level, which contains dedicated information about tool use, namely, semantic and action tool knowledge. Semantic tool knowledge contains information about the prototypical use of familiar tools, such as function (e.g., a hammer and a mallet share the same purpose) and associative relations (e.g., a hammer goes with a nail). Action tool knowledge contains information about how to manipulate tools, such as hand posture and kinematics. The present review aimed to better understand the neural correlates of action and semantic tool knowledge, by focusing on activation, stimulation an…