Search results for "A* algorithm"
showing 10 items of 2538 documents
Jet mass and substructure of inclusive jets in $ \sqrt {s} = 7\;{\text{TeV}} $ pp collisions with the ATLAS experiment
2012
Recent studies have highlighted the potential of jet substructure techniques to identify the hadronic decays of boosted heavy particles. These studies all rely upon the assumption that the internal substructure of jets generated by QCD radiation is well understood. In this article, this assumption is tested on an inclusive sample of jets recorded with the ATLAS detector in 2010, which corresponds to 35 pb-1 of pp collisions delivered by the LHC at √s = 7TeV. In a subsample of events with single pp collisions, measurements corrected for detector efficiency and resolution are presented with full systematic uncertainties. Jet invariant mass, kt splitting scales and N-subjettiness variables are…
MODELLING VAGUE KNOWLEDGE FOR DECISION SUPPORT IN PLANNING ARCHAEOLOGICAL PROSPECTIONS
2012
Abstract. Most archaeological predictive models lack significance because fuzziness of data and uncertainty in knowledge about human behaviour and natural processes are hardly ever considered. One possibility to cope with such uncertainties is utilization of probability based approaches like Bayes Theorem or Dempster-Shafer-Theory. We analyzed an area of 50 km2 in Rhineland Palatinate (Germany) near a Celtic oppidum by use of Dempster-Shafer's theory of evidence for predicting spatial probability distribution of archaeological sites. This technique incorporates uncertainty by assigning various weights of evidence to defined variables, in that way estimating the probability for supporting a …
CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS
2018
Abstract. Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-for…
Sustainable vehicle routing based on firefly algorithm and TOPSIS methodology
2019
Abstract In a sustainable management of logistics, transportation plays a crucial role. Traditionally, the main purpose was to solve the Vehicle Routing Problem minimizing the cost associated with the travelled distances. Nowadays, the economic profit cannot be the only driver for achieving sustainability and environmental issues have to be also considered. In this paper, to satisfy the intricate limits involved in real vehicle routing problem, the study has been structured considering different types of vehicles in terms of maximum capacity, velocity and emissions, asymmetric paths, vehicle-client constraints and delivery time windows. The firefly algorithm has been implemented to solve th…
An Approach to Delineate Groundwater Bodies at Risk: Seawater Intrusion in Liepāja (Latvia)
2018
Groundwater quality in coastal areas is frequently affected by seawater intrusion as a consequence of intensive water consumption. To achieve “good chemical status” of a groundwater body according to Water Framework Directive the effects of saline or other intrusions should not be observed. Groundwater pumping in former decades has caused a significant seawater intrusion into confined aquifer in Liepāja and has led to deterioration of relatively wide coastal area of the third largest city in Latvia. However, the area affected by seawater intrusion is a small part of groundwater body F1 which overall chemical status is good. Thus, no specific management measures have been applied to explore …
High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid
2011
Statistical iterative methods are a widely used method of image reconstruction in emission tomography. Traditionally, the image space is modelled as a combination of cubic voxels as a matter of simplicity. After reconstruction, images are routinely filtered to reduce statistical noise at the cost of spatial resolution degradation. An alternative to produce lower noise during reconstruction is to model the image space with spherical basis functions. These basis functions overlap in space producing a significantly large number of non-zero elements in the system response matrix (SRM) to store, which additionally leads to long reconstruction times. These two problems are partly overcome by expl…
Prenatal Risk Calculation (PRC) 3.0: An Extended DoE-Based First-Trimester Screening Algorithm Allowing For Early Blood Sampling
2015
Aim: Both previous versions of the German PRC algorithm developed by our group for routine first-trimester screening relied on the assumption that maternal blood sampling and fetal ultrasonography are performed at the same visit of a pregnant women. In this paper we present an extension of our method allowing also for constellations where this synchronization is abandoned through preponing blood sampling to dates before 11 weeks of gestation. Methods: In contrast to the directly measured concentrations of the serum parameters PAPP-A and free ß-hCG, the logarithmically transformed values could be shown to admit the construction of reference bands covering the whole range from 16 to 84 mm CRL…
A Combined Multi-Cohort Approach Reveals Novel and Known Genome-Wide Selection Signatures for Wool Traits in Merino and Merino-Derived Sheep Breeds.
2019
Merino sheep represents a valuable genetic resource worldwide. In this study, we investigated selection signatures in Merino (and Merino-derived) sheep breeds using genome-wide SNP data and two different approaches: a classical F-ST-outlier method and an approach based on the analysis of local ancestry in admixed populations. In order to capture the most reliable signals, we adopted a combined, multi-cohort approach. In particular, scenarios involving four Merino breeds (Spanish Merino, Australian Merino, Chinese Merino, and Sopravissana) were tested via the local ancestry approach, while nine pair-wise breed comparisons contrasting the above breeds, as well as the Gentile di Puglia breed, …
Special Issue on Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition
2018
This special issue of Algorithms is devoted to the study of Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition. The special issue considered both theoretical contributions able to advance the state-of-the-art in this field and practical applications that describe novel approaches for solving real-world problems.
System identification via optimised wavelet-based neural networks
2003
Nonlinear system identification by means of wavelet-based neural networks (WBNNs) is presented. An iterative method is proposed, based on a way of combining genetic algorithms (GAs) and least-square techniques with the aim of avoiding redundancy in the representation of the function. GAs are used for optimal selection of the structure of the WBNN and the parameters of the transfer function of its neurones. Least-square techniques are used to update the weights of the net. The basic criterion of the method is the addition of a new neurone, at a generic step, to the already constructed WBNN so that no modification to the parameters of its neurones is required. Simulation experiments and compa…