Search results for "UML"
showing 10 items of 407 documents
Classification of the hadronic decays of the Z$^0$ into b and c quark pairs using a neural network
1992
A classifier based on a feed-forward neural network has been used for separating a sample of about 123 500 selected hadronic decays of the Z 0 , collected by DELPHI during 1991, into three classes according to the flavour of the original quark pair: u u +d d +s s (unresolved), c c and b b . The classification has been used to compute the partial widths of the Z 0 into b and c quark pairs. This gave Γ c c /Γ h = 0.151 ± 0.008 ( stat. ) ± 0.041 ( syst. ) , Γ b b /Γ h = 0.232±0.005 ( stat. )±0.017 ( syst. ) .
Including Covariates in the ETAS Model Triggered Seismicity
2020
The paper proposes a stochastic process that improves the assessment of seismic events in space and time, considering a contagion model (branching process) within a regression-like framework to take covariates into account. The proposed approach develops the Forward Likelihood for prediction (FLP) method for estimating the ETAS model, including covariates in the model specification of the epidemic component. A simulation study is carried out for analysing the misspecification model effect under several scenarios. Also an application to the Italian catalogue is reported, together with the reference to the developed R package.
Dynamic integration of classifiers in the space of principal components
2003
Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble to be successful, it should consist of accurate and diverse base classifiers. However, it is also important that the integration procedure in the ensemble should properly utilize the ensemble diversity. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique of dynamic integration, in which local accuracy estimates are calculated for each base classifier of an ensemble, in the neighborhood of a new instance to be pr…
Multidimensional Model Design using Data Mining: A Rapid Prototyping Methodology
2017
[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]MOTIVE; International audience; Designing and building a Data Warehouse (DW), and associated OLAP cubes, are long processes, during which decision-maker requirements play an important role. But decision-makers are not OLAP experts and can find it difficult to deal with the concepts behind DW and OLAP. To support DW design in this context, we propose: (i) a new rapid prototyping methodology, integrating two different DM algorithms, to define dimension hierarchies according to decision-maker knowledge; (ii) a complete UML Profile, to define a DW schema that integrates both the DM algorithms; (iii) a mapping process to transform multidimensional …
Incremental Forming Process for the Accomplishment of Automotive Details
2007
In the last decades the scenario of the industrial production is remarkably changed, since new market requirements have to be faced by the industries. The market, actually, more and more, asks for vary models and niches product. The necessity to intercept dynamically and to satisfy the demands for the market, driver of the innovation process, involves the necessity to reduce the Timeto- market introducing to new methodologies of engineering, like the 3D-prototyping, for the qualitative and structural analysis of the final component. For these reasons, at the beginning of the nineties, a new philosophy of sheet metal forming process begins to assert on the industrial scene, whose basic logic…
Is Written Language Production more Difficult than Oral Language Production? A Working Memory Approach
1994
Abstract Is written language production more difficult than oral language production? Probably, yes. But why? Several experiments were conducted in order to test the impact of low-level activities involved in writing on the performance of higher-level activities also involved in writing. Three assumptions were made: (1) the capacity of working-memory is limited, (2) every component of writing has a cognitive load, and (3) every increase in the load devoted to the activity of one component would lead to a decrease in the remaining resources available for the other components. These low-level activities are more resource-consuming in children than in adults because children have not yet autom…
A Joint Reading of Positional and Relational Goods
2012
Both relational and positional goods are based upon an idea of joint consumption – though with opposite signs. Indeed, in both cases, individuals’ consumption choices take into account not only the individuals themselves, but others, as well. Given that relational goods provide a form of identity to their consumers, we show that a certain degree of positionality emerges within the consumption of relational goods. Analogously, except in a two-agent context, each positional good also has a relational component. What emerges is a complex structure of economic outcomes based on both relational and positional motives.
Multi-State System in human reliability analysis
2009
Application of mathematical model of Multi-State System for human reliability analysis is considered in the paper. This paper describes new method for estimation of changes of more than one component states and they influence to Multi-State System reliability by Dynamic Reliability Indices. The Multi-State System failure is considered depending on decrease of some system component efficiency and the Multi-State System repair is declared depending on replacement of some failed components. The mathematical approach of Logical Differential Calculus is used for analysis of the Multi-State System reliability change that is caused by modifications of some system components states.
A genetic algorithm approach to purify the classifier training labels for the analysis of remote sensing imagery
2017
This paper proposes a Genetic Algorithm (GA) approach to clean a given classifier training set for remote sensing image analysis. Starting from an initial set of training data, the new method called GA-Training Label Purifying (GA-TLP) consists of the significant training sample selection using GAs in order to maximize the classifier accuracy. This means to retain the most informative samples and to remove the uncertain, redundant, and misclassified ones. As a result of the selection process, we can obtain a purified training set. The proposed model is implemented and evaluated using a LANDSAT 7 ETM+ image. The experimental results confirm the effectiveness of the proposed approach.
Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data
2020
In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because …