63,670 research outputs found
A Nonlinear Estimation Approach for Vehicle and Tire-Road Monitoring with No Interaction Modelling
No full textThe reliability of control systems implemented onboard vehicles is strictly dependent on the correct estimation of the sideslip angle coupled with tire-road features. The prediction of tire-road forces and friction is strongly dependent on tire models typically characterizable through expensive experimental tests. In this paper, a nonlinear estimation approach, based on an Extended Kalman Filter, of lateral tire-road forces and friction coupled with the vehicle sideslip angle is proposed. The estimator is based on a single-track vehicle model, including a parametric estimation approach to avoid a specific tire model employment. The results obtained through the proposed technique are compared with a detailed vehicle model in both non-noisy and noisy measurements
AN ESTIMATOR BASED ON A SIGMA-POINTS KALMAN FILTER FOR VEHICLE AND TIRE-ROAD CONDITION MONITORING
No full textIn the automotive field, the estimation of key variables related to vehicle dynamics, such as the sideslip angle coupled with characteristic tire-road parameters, is fundamental for improving the safety performance of vehicle control systems. Typically, expensive tests are made to characterize tire parameters included in models functional for estimating tire-road forces. A nonlinear model-based estimator, based on the Central Difference Kalman Filter, is proposed in this work for making the vehicle condition monitoring through the coupled estimation of the sideslip angle, the tire-road friction coefficient and lateral forces. An estimator design model is designed around the single-track vehicle model coupled with a parametric estimation strategy functional for estimating tire-road features without specific modelling of tires. The proposed technique is assessed by comparing the estimated data with the ones obtained from a Multibody vehicle model. Two different tests are made considering both non-noisy and noisy measurements
La sicurezza microbiologica nella preparazione casalinga delle olive verdi al naturale siciliane.
No full textLa sicurezza microbiologica nella preparazione casalinga delle olive verdi al naturale siciliane
Railway pantograph contact strip monitoring through image processing techniques
No full textDuring the train operation and especially at high speed, the pantograph-catenary contact is influenced by electromechanical wear phenomena. Consequently, the overall reliability of the railway system is reduced. In particular, the pantograph contact strips are subjected to severe abrasions during the running operations. Their maintenance and inspection are usually made manually with negative consequences in terms of costs and safety. Therefore, in the last years, no-contact monitoring and measurements techniques for the pantograph-catenary elements have been developed. A practicable methodology to measure the pantograph contact strip thickness is proposed in this paper. The employed approach is based on image processing techniques useful to detect geometrical variations of the pantograph contact strips. The no-contact measurement system is made through a cost-effective single camera. Several images of the pantograph equipped with unworn and worn contact strips are employed to verify the reliability of the proposed technique
A supervised machine learning framework for smart tires
No full textArtificial intelligence techniques are today among the most important challenges for the future due to their ability to analyze big data and identify key characteristics. In transportation, the growing amount of data measured on vehicles and infrastructures constitute a wealth of information useful for energy, safety and environmental sustainability. In this paper, a supervised machine learning methodology applied to smart tires is presented. Starting from the definition of sensors inside the tire, it is shown how a predictive model can be used for the learning phase through the generation of virtual datasets. Following the learning phase, the architecture and the functional scheme of the machine learning algorithm is presented. The methodology is capable to estimate key variables of the tire operation starting from common measurements on the vehicle
Output-only estimation of lateral wheel-rail contact forces and track irregularities
No full textThe contact between the wheel and the rail heavily affects the dynamic behaviour of railway vehicles and their running safety. The real-time knowledge of wheel-rail contact forces is tricky due to the several running conditions of the train and the combined tribological and wear characteristics of wheels and rails. Furthermore, their measurement and that of track irregularities, sensitive to the harshness of the wheel-rail contact, require dedicated sensors. Therefore, the development of strategies for estimating wheel-rail contact features is essential to perform the condition monitoring of rails to improve the safety related to the entire railway system. A nonlinear model-based estimation procedure, based on a Central Difference Kalman Filter, is proposed in this work to estimate the lateral wheel-rail contact forces and moments, including the identification of lateral track irregularities in the estimation process related to rails. Furthermore, the developed estimation technique can..
Multipurpose model for pantograph dynamic interaction with flexible or rigid catenary
No full textRailway transportation powered by electrical supply systems is the most employed technology to provide passengers safety and vehicles reliability during the running operations. Both the flexible and rigid catenaries are employed to power up trains through interaction with pantographs. Within this context, principal issues are wear estimation and pantograph-catenary contact loss prediction. Therefore, various modelling approaches are adopted to build simulation models for these phenomena prediction, particularly related to the contact force exerted between the pantograph and the catenary. In this paper a new and versatile analytical model of the pantograph dynamic interaction with the catenary is presented. The model is characterized by a low computational load and the pantograph model can be interfaced with both rigid and flexible catenary models. Therefore, the proposed model is a versatile solution to reproduce the pantograph-catenary interaction behavior. Simulations have been carried out in both the contact sceneries and the results are reliable and in accordance with the system dynamics
Modelling of Hysteresis in Vibration Control Systems by means of the Bouc-Wen Model
No full textThe review presents developments concerning the modelling of vibration control systems with hysteresis. In particular, the review focuses on applications of the Bouc-Wen model that describes accurate hysteretic behaviour in vibration control devices. The review consists of theoretical aspects of the Bouc-Wen model, identification procedures, and applications in vibration control
p73 is regulated by phosphorylation at the G2/M transition
No full textp73 is a p53 paralog that encodes proapoptotic (transactivation-competent (TA)) and antiapoptotic ( dominant negative) isoforms. TAp73 transcription factors mediate cell cycle arrest and/or apoptosis in response to DNA damage and are involved in developmental processes in the central nervous system and the immune system. p73 proteins may also play a role in the regulation of cell growth. Indeed, p73 expression is itself modulated during the cell cycle and TAp73 proteins accumulate in S phase cells. In addition, the function of p73 proteins is also regulated by post-translational modifications and protein-protein interactions in different cellular and pathophysiological contexts. Here we show that p73 is a physiological target of the p34(cdc2)-cyclin B mitotic kinase complex in vivo. Both p73beta and p73alpha isoforms are hyperphosphorylated in normal mitotic cells and during mitotic arrest induced by microtubule-targeting drugs. p34(cdc2)-cyclin B phosphorylates and associates with p73 in vivo, which results in a decreased ability of p73 to both bind DNA and activate transcription in mitotic cells. Indeed, p73 is excluded from condensed chromosomes in meta- and anaphase, redistributes throughout the mitotic cytoplasm, and unlike p53, shows no association with centrosomes. Together these results indicate that M phase-specific phosphorylation of p73 by p34(cdc2)- cyclin B is associated with negative regulation of its transcriptional activating function
A Neural Network Based Approach for the Intake Air Mass Flow Prediction in SI Engines
No full textThe correct measurement of the intake air mass flow is fundamental for the Spark Ignition (SI) Engines to guarantee an efficient balancing with the fuel one. In the aeronautical field, this measurement is essential to ensure the correct functioning of engines based on the aircraft's altitude. The technological growth of SI engines coupled with advanced control and estimation techniques improved engines in terms of fuel consumption and pollution emission reductions, increasing their performances. Typically, model-based estimation techniques have been employed for the manifold air pressure (MAP) and flow (MAF) virtual measurements taking into account the two principal approaches for MAF determination in engine control units called speed-density and alpha-speed. Furthermore, Neural Networks can be employed to predict these variables, avoiding the presence of unmodeled dynamics in model-based approaches. In this work, a methodology based on Feedforward Neural Networks (FNNs) to predict MAP and MAF is presented. The developed Networks are able to predict these two fundamental operative variables of SI engines in transient conditions after training based on steady-state data obtained through a well-known intake manifold dynamical model (IMDM). The proposed approach allows the costs reduction related to expensive experimental tests and, virtual sensors based on FNNs can be employed as redundancies in measurements
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