Multivariate Techniques for Identifying Diffractive Interactions at the LHC

31 pages, 14 figures, 11 tablesClose to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.Peer reviewe

Aiding first incident responders using a decision support system based on live drone feeds

In case of a dangerous incident, such as a fire, a collision or an earthquake, a lot of contextual data is available for the first incident responders when handling this incident. Based on this data, a commander on scene or dispatchers need to make split-second decisions to get a good overview on the situation and to avoid further injuries or risks. Therefore, we propose a decision support system that can aid incident responders on scene in prioritizing the rescue efforts that need to be addressed. The system collects relevant data from a custom designed drone by detecting objects such as firefighters, fires, victims, fuel tanks, etc. The drone autonomously observes the incident area, and based on the detected information it proposes a prioritized based action list on e.g. urgency or danger to incident responders

SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases

Combinaison crédibiliste de classifieurs binaires

The problem of binary classifier combination is adressed in this article. This approach consists in solving a multi-class classification problem by combining the solutions of binary sub-problems. We consider two strategies in which each class is opposed to each other, or to all others. The combination is considered from the point of view of the theory of evidence. The classifier outputs are interpreted either as conditional belief functions, or as belief functions expressed in a coarser frame. They are combined by computing a belief function that is consistent with the available information. The performances of the methods are compared with those of other techniques and illustrated on various datasets.Nous étudions dans cet article le problème de la combinaison de classifieurs binaires. Cette approche consiste à résoudre un problème de discrimination multi-classes, en combinant les solutions de sous-problèmes binaires ; nous nous intéressons aux stratégies opposant chaque classe à chaque autre, et chaque classe à toutes les autres. La combinaison est considérée ici du point de vue de la théorie de Dempster-Shafer : les sorties des classifieurs sont ainsi interprétées comme des fonctions de croyance, conditionnelles ou exprimées dans un cadre plus grossier que le cadre initial. Elles sont combinées en calculant une fonction de croyance consistante avec les informations disponibles. Les performances des deux approches sont comparées à celles d’autres méthodes et illustrées sur divers jeux de données

Protein classification using functional motifs as sub-structures of active site

Master'sMASTER OF SCIENC