635 research outputs found
Optimización de trayectorias para AGVs mediante algoritmos genéticos
[ES] La búsqueda de la eficiencia y la mejora continua de los procesos productivos está aumentando su automatización. Los vehículos de guiado automático (AGV) usados para transporte, son elementos clave a la hora de cumplir esta función, siendo fundamental el desarrollo y mejora de sus sistemas de navegación y posicionamiento. Así, en este trabajo se propone el uso de una técnica evolutiva, los algoritmos genéticos (AG), con el fin de obtener los parámetros óptimos de un método de generación
de trayectorias. Se utiliza un modelo de mapa de ocupación para el diseño del entorno con el fin de verificar posibles eventos de colisión con el AGV. Se simulan diferentes escenarios donde se busca optimizar la longitud de la trayectoria generada evitando colisiones. Los resultados de la simulación muestran que el algoritmo es capaz de minimizar la longitud de las trayectorias generadas evitando las posibles colisiones.[EN] Continuous improvement of industrial and production processes for efficiency optimization has led to an increase in the need for automatization. Automatic guided vehicles (AGV) are key transport elements when it comes to fulfilling this function, as well as the development and improvement of their navigation and positioning systems. Thus, in this work the use of a Soft Computing evolutive technique, genetic algorithms (GA), is proposed in order to obtain the optimal parameters of a trajectory generation method. An occupancy map model for the environment layout is used in order to check collision events with the
AGV. Different scenarios have been simulated to optimize the trajectory length avoiding collisions if possible. Simulation results show that the algorithm is able to minimize the length of the path successfull
Developing a new Bayesian Risk Index for risk evaluation of soil contamination
Supplementary data associated with this article can befound in the online version, at http://dx.doi.org/10.1016/j.scitotenv.2017.06.068. These data include the Google map of the most important areas described in this article.Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs.Carlos Sierra obtained a grant from the “Severo Ochoa (BP10-112)” Programme(Ficyt, Asturias,Spain).info:eu-repo/semantics/publishedVersio
Model-independent search for CP violation in D0→K−K+π−π+ and D0→π−π+π+π− decays
A search for CP violation in the phase-space structures of D0 and View the MathML source decays to the final states K−K+π−π+ and π−π+π+π− is presented. The search is carried out with a data set corresponding to an integrated luminosity of 1.0 fb−1 collected in 2011 by the LHCb experiment in pp collisions at a centre-of-mass energy of 7 TeV. For the K−K+π−π+ final state, the four-body phase space is divided into 32 bins, each bin with approximately 1800 decays. The p-value under the hypothesis of no CP violation is 9.1%, and in no bin is a CP asymmetry greater than 6.5% observed. The phase space of the π−π+π+π− final state is partitioned into 128 bins, each bin with approximately 2500 decays. The p-value under the hypothesis of no CP violation is 41%, and in no bin is a CP asymmetry greater than 5.5% observed. All results are consistent with the hypothesis of no CP violation at the current sensitivity
Search for the lepton-flavor-violating decays Bs0→e±μ∓ and B0→e±μ∓
A search for the lepton-flavor-violating decays Bs0→e±μ∓ and B0→e±μ∓ is performed with a data sample, corresponding to an integrated luminosity of 1.0 fb-1 of pp collisions at √s=7 TeV, collected by the LHCb experiment. The observed number of Bs0→e±μ∓ and B0→e±μ∓ candidates is consistent with background expectations. Upper limits on the branching fractions of both decays are determined to be B(Bs0→e±μ∓)101 TeV/c2 and MLQ(B0→e±μ∓)>126 TeV/c2 at 95% C.L., and are a factor of 2 higher than the previous bounds
Measurements of long-range near-side angular correlations in TeV proton-lead collisions in the forward region
Two-particle angular correlations are studied in proton-lead collisions at a
nucleon-nucleon centre-of-mass energy of TeV, collected
with the LHCb detector at the LHC. The analysis is based on data recorded in
two beam configurations, in which either the direction of the proton or that of
the lead ion is analysed. The correlations are measured in the laboratory
system as a function of relative pseudorapidity, , and relative
azimuthal angle, , for events in different classes of event
activity and for different bins of particle transverse momentum. In
high-activity events a long-range correlation on the near side, , is observed in the pseudorapidity range . This
measurement of long-range correlations on the near side in proton-lead
collisions extends previous observations into the forward region up to
. The correlation increases with growing event activity and is found
to be more pronounced in the direction of the lead beam. However, the
correlation in the direction of the lead and proton beams are found to be
compatible when comparing events with similar absolute activity in the
direction analysed.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-040.htm
Study of the production of and hadrons in collisions and first measurement of the branching fraction
The product of the () differential production
cross-section and the branching fraction of the decay () is
measured as a function of the beauty hadron transverse momentum, ,
and rapidity, . The kinematic region of the measurements is and . The measurements use a data sample
corresponding to an integrated luminosity of collected by the
LHCb detector in collisions at centre-of-mass energies in 2011 and in 2012. Based on previous LHCb
results of the fragmentation fraction ratio, , the
branching fraction of the decay is
measured to be \begin{equation*} \mathcal{B}(\Lambda_b^0\rightarrow J/\psi
pK^-)= (3.17\pm0.04\pm0.07\pm0.34^{+0.45}_{-0.28})\times10^{-4},
\end{equation*} where the first uncertainty is statistical, the second is
systematic, the third is due to the uncertainty on the branching fraction of
the decay , and the
fourth is due to the knowledge of . The sum of the
asymmetries in the production and decay between and
is also measured as a function of and .
The previously published branching fraction of , relative to that of , is updated.
The branching fractions of are determined.Comment: 29 pages, 19figures. All figures and tables, along with any
supplementary material and additional information, are available at
https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-032.htm
Evidence for the strangeness-changing weak decay
Using a collision data sample corresponding to an integrated luminosity
of 3.0~fb, collected by the LHCb detector, we present the first search
for the strangeness-changing weak decay . No
hadron decay of this type has been seen before. A signal for this decay,
corresponding to a significance of 3.2 standard deviations, is reported. The
relative rate is measured to be
, where and
are the and fragmentation
fractions, and is the branching
fraction. Assuming is bounded between 0.1 and
0.3, the branching fraction would lie
in the range from to .Comment: 7 pages, 2 figures, All figures and tables, along with any
supplementary material and additional information, are available at
https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-047.htm
flavour tagging using charm decays at the LHCb experiment
An algorithm is described for tagging the flavour content at production of
neutral mesons in the LHCb experiment. The algorithm exploits the
correlation of the flavour of a meson with the charge of a reconstructed
secondary charm hadron from the decay of the other hadron produced in the
proton-proton collision. Charm hadron candidates are identified in a number of
fully or partially reconstructed Cabibbo-favoured decay modes. The algorithm is
calibrated on the self-tagged decay modes and using of data collected by the LHCb
experiment at centre-of-mass energies of and
. Its tagging power on these samples of
decays is .Comment: All figures and tables, along with any supplementary material and
additional information, are available at
http://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-027.htm
- …