A robust observer based on H∞ filtering with parameter uncertainties combined with Neural Networks for estimation of vehicle roll angle

Nowadays, one of the main objectives in road transport is to decrease the number of accident victims. Rollover accidents caused nearly 33% of all deaths from passenger vehicle crashes. Roll Stability Control (RSC) systems prevent vehicles from untripped rollover accidents. The lateral load transfer is the main parameter which is taken into account in the RSC systems. This parameter is related to the roll angle, which can be directly measured from a dual-antenna GPS. Nevertheless, this is a costly technique. For this reason, roll angle has to be estimated. In this paper, a novel observer based on H∞ filtering in combination with a neural network (NN) for the vehicle roll angle estimation is proposed. The design of this observer is based on four main criteria: to use a simplified vehicle model, to use signals of sensors which are installed onboard in current vehicles, to consider the inaccuracy in the system model and to attenuate the effect of the external disturbances. Experimental results show the effectiveness of the proposed observer.This work is supported by the Spanish Government through the Project TRA2013-48030-C2-1-R, which is gratefully acknowledged

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle's parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle's roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle's states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.This work is supported by the Spanish Government through the Project TRA2013-48030-C2-1-R, which is gratefully acknowledged

Historia del conocimiento de los Ammonites del Jurásico de España: l. los tiempos de José Torrubia (1754)

The first published Jurassic Ammonites from Spain in the Aparato para la Historia Natural Española of José Torrubia (1754) is described here with plates depicting some Ammonite fossils atributed by Torrubia to «cornu ammonis». An evaluarían of the knowledge of Torrubia about the contemporaneus pa/aeontologicalliterature and the controversies about the petrifications is made

Influencia de especies arbóreas nativas en sistemas silvopastoriles sobre el rendimiento y valor nutricional de Lolium multiflorum y Trifolium repens

Los sistemas silvopastoriles, se han convertido en una alternativa sostenible para la producción ganadera, en ese contexto, el presente estudio tuvo por objetivo evaluar la influencia de los sistemas silvopastoriles (SSP) con especies arbóreas nativas como Eritryna edulis (Pajuro), Alnus acuminata (Aliso) y Salix babylonica (Sauce) sobre el rendimiento y valor nutricional de Lolium multiflorum (Rye grass) y Trifolium repens (Trébol). El rendimiento se determinó a través de la cuantificación de forraje verde y materia seca, mientras que el valor nutritivo fue calculado mediante la determinación de proteína, grasa bruta, fibra detergente neutra y fibra detergente ácida. Los resultados alcanzados permiten concluir que, en el rendimiento de biomasa y materia seca para ambas especies de pastos existen diferencias estadísticas (p < 0,05) entre los sistemas evaluados; por otro lado, aunque los niveles de proteína son superiores en los sistemas silvopastoriles, estos no representan diferencias estadísticas entre sistemas. Los resultados de grasa bruta y fibra detergente ácida indican que son estadísticamente iguales, es decir el tipo de sistema productivo no influyo en estas variables

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.This work was possible thanks to the funds provided by the Spanish Government through the CICYTProject TRA2013-48030-C2-1-R

Biomarkers Associated with Leishmania infantum Exposure, Infection, and Disease in Dogs

Canine leishmaniosis (CanL) is a vector-borne disease caused by the protozoan Leishmania (Leishmania) infantum species [syn. L. (L.) infantum chagasi species in the Americas] which is transmitted by the bite of a female phlebotomine sand fly. This parasitosis is endemic and affect millions of dogs in Asia, the Americas and the Mediterranean basin. Domestic dogs are the main hosts and the main reservoir hosts for human zoonotic leishmaniosis. The outcome of infection is a consequence of intricate interactions between the protozoan and the immunological and genetic background of the host. Clinical manifestations can range from subclinical infection to very severe disease. Early detection of infected dogs, their close surveillance and treatment are essential to control the dissemination of the parasite among other dogs, being also a pivotal element for the control of human zoonotic leishmaniosis. Hence, the identification of biomarkers for the confirmation of Leishmania infection, disease and determination of an appropriate treatment would represent an important tool to assist clinicians in diagnosis, monitoring and in giving a realistic prognosis to subclinical infected and sick dogs. Here, we review the recent advances in the identification of Leishmania infantum biomarkers, focusing on those related to parasite exposure, susceptibility to infection and disease development. Markers related to the pathogenesis of the disease and to monitoring the evolution of leishmaniosis and treatment outcome are also summarized. Data emphasizes the complexity of parasite-host interactions and that a single biomarker cannot be used alone for CanL diagnosis or prognosis. Nevertheless, results are encouraging and future research to explore the potential clinical application of biomarkers is warranted.publishersversionpublishe

EUREC⁴A

The science guiding the EUREC⁴A campaign and its measurements is presented. EUREC⁴A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC⁴A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC⁴A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC⁴A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement

EUREC⁴A

The science guiding the EUREC⁴A campaign and its measurements is presented. EUREC⁴A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC⁴A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC⁴A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC⁴A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement