Search results for "Machine learning."
showing 10 items of 1455 documents
Analysis of compatibility between lighting devices and descriptive features using Parzen’s kernel: application to flaw inspection by artificial vision
2000
We present a supervised method, developed for industrial inspections by artificial vision, to obtain an adapted combination of descriptive features and a lighting device. This method must be implemented under real-time constraints and therefore a minimal number of features must be selected. The method is based on the assessment of the discrimination power of many descriptive features. The objective is to select the combination of descriptive features and lighting system best able to discriminate flawed classes from defect-free classes. In the first step, probability densities are computed for flawed and defect-free classes and for each tested combination. The discrimination power of the fea…
Comparison of different predictive models for nutrient estimation in a sequencing batch reactor for wastewater treatment
2006
Abstract In this paper different predictive models for nutrient estimation in a sequencing batch reactor (SBR) for wastewater treatment are compared: principal component regression (PCR), partial least squares (PLS), and artificial neural networks (ANNs). Two unfolding procedures were used: batch-wise and variable-wise. For the latter unfolding method, X and Y matrix augmentation with lagged variables were used in some models to incorporate process dynamics. The results have shown that batch-wise unfolding PLS models outperform the other approaches. The ANN models are good predictive models, but in this particular case-study, they do not outperform those multivariate projection models that …
Algorithms for the inference of causality in dynamic processes: Application to cardiovascular and cerebrovascular variability
2015
This study faces the problem of causal inference in multivariate dynamic processes, with specific regard to the detection of instantaneous and time-lagged directed interactions. We point out the limitations of the traditional Granger causality analysis, showing that it leads to false detection of causality when instantaneous and time-lagged effects coexist in the process structure. Then, we propose an improved algorithm for causal inference that combines the Granger framework with the approach proposed by Pearl for the study of causality among multiple random variables. This new approach is compared with the traditional one in theoretical and simulated examples of interacting processes, sho…
Detecting nonlinear causal interactions between dynamical systems by non-uniform embedding of multiple time series.
2010
This study introduces a new approach for the detection of nonlinear Granger causality between dynamical systems. The approach is based on embedding the multivariate (MV) time series measured from the systems X and Y by means of a sequential, non-uniform procedure, and on using the corrected conditional entropy (CCE) as unpredictability measure. The causal coupling from X to Y is quantified as the relative decrease of CCE measured after allowing the series of X to enter the embedding procedure for the description of Y. The ability of the approach to quantify nonlinear causality is assessed on MV time series measured from simulated dynamical systems with unidirectional coupling (the Rössler-…
Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping
2018
Abstract Gully erosion has been identified as an important soil degradation process and sediment source, especially in arid and semiarid areas. Thus, it is useful to identify the spatial occurrence of this form of water erosion in the landscape and the most vulnerable areas. In this study, we explored the effects of different pixel sizes on some controlling factors extracted from a digital elevation model and remote sensing data when producing a gully erosion susceptibility map (GESM) of Ekbatan Dam Basin, Hamadan, Iran. An inventory map of the gully landforms was prepared based on global positioning system routes of the gullies, extensive field surveys, and visual interpretations of satell…
Estimating brain connectivity when few data points are available: Perspectives and limitations
2017
Methods based on the use of multivariate autoregressive modeling (MVAR) have proved to be an accurate and flexible tool for the estimation of brain functional connectivity. The multivariate approach, however, implies the use of a model whose complexity (in terms of number of parameters) increases quadratically with the number of signals included in the problem. This can often lead to an underdetermined problem and to the condition of multicollinearity. The aim of this paper is to introduce and test an approach based on Ridge Regression combined with a modified version of the statistics usually adopted for these methods, to broaden the estimation of brain connectivity to those conditions in …
A Concurrent Neural Classifier for HTML Documents Retrieval
2003
A neural based multi-agent system for automatic HTML pages retrieval is presented. The system is based on the EαNet architecture, a neural network having good generalization capabilities and able to learn the activation function of its hidden units. The starting hypothesis is that the HTML pages are stored in networked repositories. The system goal is to retrieve documents satisfying a user query and belonging to a given class (i.e. documents containing the word “football” and talking about “Sports”). The system is composed by three interacting agents: the EαNet Neural Classifier Mobile Agent, the Query Agent, and the Locator Agent. The whole system was successfully implemented exploiting t…
Assessment of mental stress through the analysis of physiological signals acquired from wearable devices
2019
Mental stress is a physiological state that directly correlates to the quality of life of individuals. Generally speaking, but especially true for disabled or elderly subjects, the assessment of such condition represents a very strong indicator correlated to the difficulties, and, in some case, to the frustration that derives from the execution of a task that results troublesome to be accomplished. This article describes a novel procedure for the assessment of the mental stress level through the use of low invasive wireless wearable devices. The information contained in electrocardiogram, respiratory signal, blood volume pulse, and electroencephalogram was extracted to set up an estimator f…
Analysis and simulation of creativity learning by means of artificial neural networks
2007
The paper presents a new neural network approach for analysis and simulation of creative behavior. The used concept of Dynamically Controlled Neural Gas (DyCoNG) entails a combination of Dynamically Controlled Network [Perl, J. (2004a). A neural network approach to movement pattern analysis. Human Movement Science,23, 605-620] and Growing Neural Gas (Fritzke, 1995) by quality neurons. A quality neuron reflects the rareness of a piece of information and therefore can measure the originality of a recorded activity that was assigned to the neuron during the network training. The DyCoNG approach was validated using data from a longitudinal field-based study. The creative behavior of 42 particip…
Psychophysically Tuned Divisive Normalization Approximately Factorizes the PDF of Natural Images
2010
The conventional approach in computational neuroscience in favor of the efficient coding hypothesis goes from image statistics to perception. It has been argued that the behavior of the early stages of biological visual processing (e.g., spatial frequency analyzers and their nonlinearities) may be obtained from image samples and the efficient coding hypothesis using no psychophysical or physiological information. In this work we address the same issue in the opposite direction: from perception to image statistics. We show that psychophysically fitted image representation in V1 has appealing statistical properties, for example, approximate PDF factorization and substantial mutual informatio…