Sensor-based pavement diagnostic using acoustic signature for moduli estimation

AbstractThe diffusion of smart infrastructures for smart cities provides new opportunities for the improvement of both road infrastructure monitoring and maintenance management.Often pavement management is based on the periodic assessment of the elastic modulus of the bound layers (i.e., asphalt concrete layers) by means of traditional systems, such as Ground Penetrating Radar (GPR) and Falling Weight Deflectometer (FWD). Even if these methods are reliable, well-known, and widespread, they are quite complex, expensive, and are not able to provide updated information about the evolving structural health condition of the road pavement. Hence, more advanced, effective, and economical monitoring systems can be used to solve the problems mentioned above.Consequently, the main objective of the study presented in this paper is to present and apply an innovative solution that can be used to make smarter the road pavement monitoring. In more detail, an innovative Non-Destructive Test (NDT)-based sensing unit was used to gather the vibro-acoustic signatures of road pavements with different deterioration levels (e.g. with and without fatigue cracks) of an urban road. Meaningful features were extracted from the aforementioned acoustic signature and the correlation with the elastic modulus defined using GPR and FWD data was investigated.Results show that some of the features have a good correlation with the elastic moduli of the road section under investigation. Consequently, the innovative solution could be used to evaluate the variability of elastic modulus of the asphalt concrete layers, and to monitor with continuity the deterioration of road pavements under the traffic loads

Ten years of infant mental health in the Netherlands:Who are the clients?

Background: Infant Mental Health (IMH) is a topic of current interest that emerged over the past decades, concerned with alleviating suffering and enhancing the social and emotional competence of young children. Worldwide there is increasing recognition of infant psychopathology meriting intervention. However, there are still limited data regarding the prevalence of psychiatric disorders and sociodemographic characteristics of these youngest of patients in clinical settings. Aim: This large, descriptive study aims at presenting the socio-demographic and clinical characteristics of infants referred consecutively to three outpatient Infant Mental Health teams in the Netherlands between September 2000 and July 2013. Methods: The medical records of 783 infants were retrospectively examined and the data were collected from paper and electronic patient files. Clinical and socio-demographic characteristics were categorized in child factors, developmental milestones, family factors and clinical outcome measures (DSM-IV, DC:0-3R, WIPPSI-III, SON-R 2½-7). Results: Our sample showed significantly more boys (543, 69%) than girls (240, 31%) being referred to the Infant Mental Health teams. Most children were referred when they were four or five years of age, both boys and girls. Mean duration of treatment was about a year and a half (20.34 months, SD 18.87) and most reported diagnoses were ADHD/behavioral disorders, ASS and disorder in infancy/childhood NOS. Familial psychiatric disorders were reported in 242 families (41%). These findings are discussed in the light of earlier research

Deep Learning Architecture for Forest Detection in Satellite Data

Deep Learning algorithms have achieved great progress in different applications due to their training capabilities, parameter reduction and increased accuracy. Image processing is a particular area that has received recent attention promoted by the growing processing power and data availability. Remote sensing devices provide image-like data that can be used to characterize Earth’s natural or artificial phenomena. Particularly, forest detection is important in many applications like flooding simulations, analysis of forest health or detection of area desertification. The existing techniques for forest detection based on satellite data lack accuracy or still require human expert intervention to correct recognition errors or parameter setup. In this work a Deep Learning architecture for forest detection is presented, that aims at increasing accuracy and reducing expert dependency. A data preprocessing procedure, analysis and dataset composition for robust automatic forest detection is described. The proposed approach was validated with real SRTM and Landsat-8 satellite data.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Deep Learning Architecture for Forest Detection in Satellite Data

Deep Learning algorithms have achieved great progress in different applications due to their training capabilities, parameter reduction and increased accuracy. Image processing is a particular area that has received recent attention promoted by the growing processing power and data availability. Remote sensing devices provide image-like data that can be used to characterize Earth’s natural or artificial phenomena. Particularly, forest detection is important in many applications like flooding simulations, analysis of forest health or detection of area desertification. The existing techniques for forest detection based on satellite data lack accuracy or still require human expert intervention to correct recognition errors or parameter setup. In this work a Deep Learning architecture for forest detection is presented, that aims at increasing accuracy and reducing expert dependency. A data preprocessing procedure, analysis and dataset composition for robust automatic forest detection is described. The proposed approach was validated with real SRTM and Landsat-8 satellite data.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Deep Learning Architecture for Forest Detection in Satellite Data

Deep Learning algorithms have achieved great progress in different applications due to their training capabilities, parameter reduction and increased accuracy. Image processing is a particular area that has received recent attention promoted by the growing processing power and data availability. Remote sensing devices provide image-like data that can be used to characterize Earth’s natural or artificial phenomena. Particularly, forest detection is important in many applications like flooding simulations, analysis of forest health or detection of area desertification. The existing techniques for forest detection based on satellite data lack accuracy or still require human expert intervention to correct recognition errors or parameter setup. In this work a Deep Learning architecture for forest detection is presented, that aims at increasing accuracy and reducing expert dependency. A data preprocessing procedure, analysis and dataset composition for robust automatic forest detection is described. The proposed approach was validated with real SRTM and Landsat-8 satellite data.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Halo effects on fusion cross section in 4,6He+64Zn collision around and below the Coulomb barrier

The structure of the halo nuclei is expected to influence the fusion mechanism at energies around and below the Coulomb barrier. Here new data of 4He+64Zn at sub-barrier energies are presented which cover the same energy region of previous measurements of 6He+64Zn. The fusion cross section was measured by using an activation technique where the radioactive evaporation residues produced in the reaction were identified by the X-ray emission which follows their electron capture decay. By comparing the two system, we observe an enhancement on the fusion cross section in the reaction induced by 6He, at energy below the Coulomb barrier. It is shown that this enhancement seems to be due to static properties of halo 2n 6He nucleus

Enhancement in the 6He+64Zn fusion cross section at energies around the barrier: static or dynamic effect?

A new measurement of fusion cross-section for the system 4 He+64 Zn was performed at sub-barrier energy in order to cover the same energy region of previous measurements of 6 He+64 Zn. The fusion cross-section was obtained using an activation technique. From the comparison of the two excitation functions an enhancement of the fusion cross section was observed, at energy below the Coulomb barrier, in the reaction induced by 6 He in respect to the one induced by 4 He

Role of neutron transfer processes on the 6Li+120Sn and 7Li+119Sn fusion reactions

The results concerning the study of 6 Li+ 120 Sn and 7 Li+ 119 Sn systems are presented. These two sistems are characterised by very similar structures of the interacting nuclei and by different Q-value for oneand two- neutron transfer. Our aim is to disentangle the possible effects due to the different n-transfer Q-values, at sub-barriers energies, by comparing the two fusion excitation function. In these experiments the fusion cross section has been measured by using a stack activation technique. No particular differences in the two fusion excitation functions have been observed. The influence of transfer channels on fusion cross-section has been object of investigations in the last years. In particular, the possible dependence of the fusion cross-section on the sign of the neutron transfer Q-value has been much debated in literature. The systematic approach used for the study of the Ca+Zr systems [1] provided relatively clear evidence of the relation between sub-barrier-cross section and the sign of the neutron transfer Q-value, in a model-independent way. According to experimenta

The Challenges of Adopting PLM Tools Involving Diversified Technologies in Today’s Automotive Supplier Business

In order to reduce product development (PD) costs and duration, PD cycles are being accelerated in order to reduce the time to market and satisfy the end customer needs. Another key challenge in PD today, is product diversification in the technologies used, requiring improved collaboration amongst local and dispersed multi disciple PD teams. A main stream tool that aids and support engineers in PD to collaborate and share information/knowledge is Product Lifecycle Management (PLM). This research explores the benefits and requirements of implementing a PLM system for a PD and manufacturing company within the automotive supply chain. This paper first provides a brief background of the subject area, followed by an explanation of the initial industrial investigation for the implementation of a PLM system, from which investigation the resulting conclusions and recommendations are presented as the building blocks of the implementation project