Thermal and Optical Characterization of Undoped and Neodymium-Doped Y3ScAl4O12 Ceramics

Y3–3xNd3xSc1Al4O12 (x = 0, 0.01, and 0.02) ceramics were fabricated by sintering at high temperature under vacuum. Unit cell parameter refinement and chemical analysis have been performed. The morphological characterization shows micrograins with no visible defects. The thermal analysis of these ceramics is presented, by measuring the specific heat in the temperature range from 300 to 500 K. Their values at room temperature are in the range 0.81–0.90 J g1–K–1. The thermal conductivity has been determined by two methods: by the experimental measurement of the thermal diffusivity by the photopyroelectric method, and by spectroscopy, evaluating the thermal load. The thermal conductivities are in the range 9.7–6.5 W K–1 m–1 in the temperature interval from 300 to 500 K. The thermooptic coefficients were measured at 632 nm by the dark mode method using a prism coupler, and the obtained values are in the range 12.8–13.3 × 10–6 K–1. The nonlinear refractive index values at 795 nm have been evaluated to calibrate the nonlinear optical response of these materials.This work is supported by the Spanish Government under projects MAT2011-29255-C02-01-02, MAT2013-47395-C4-4-R, and the Catalan Government under project 2014SGR1358. It was also funded by the European Commission under the Seventh Framework Programme, project Cleanspace, FP7-SPACE-2010-1-GA No. 263044

Technological developments as an answer to bridge management challenges

IABSE Symposium 2019, Towards a Resilient Built Environment - Risk and Asset Management, GUIMARAES, PORTUGAL, 27-/03/2019 - 29/03/2019Bridge management is a challenge as owners have to deal with limited financial resources to maintain the functionalities and safety of ageing structures. Demands on transportation networks change, due to regulatory developments, society's evolution and shifts with high expectations on the operational performance of roadway bridges with reduced congestion, delay, and accidents. To minimize intrusion in the transport flow, inspection and monitoring methods should be non?destructive, minimally invasive. They should be capable of yielding rapid and accurate inspection results allowing an adequate response from the asset manager. Research aims at including autonomously operating equipment (e.g. robotics), non?intrusive (remote or proximity) observation techniques, or other methods that ensure quality and performance control of the roadway bridges in time, more safely, more quickly and/or to a higher degree of accuracy and precision.The innovation subgroup in COST action TU1406 investigates novel condition monitoring and sensing technologies for the assessment of structural serviceability and safety. Advanced, integrated, cost-effective and reliable instrumentation solutions, techniques and concepts are looked at with the aim to provide data, that will be used to compute innovative performance indicators. In this context, this paper briefly reminds some significant challenges associated with bridge management and presents three examples of innovation in bridge monitoring and NDT investigation techniques

Lead-free piezoceramics - Where to move on?

Lead-free piezoceramics aiming at replacing the market-dominant lead-based ones have been extensively searched for more than a decade worldwide. Some noteworthy outcomes such as the advent of commercial products for certain applications have been reported, but the goal, i.e., the invention of a lead-free piezocermic, the performance of which is equivalent or even superior to that of PZT-based piezoceramics, does not seem to be fulfilled yet. Nevertheless, the academic effort already seems to be culminated, waiting for a guideline to a future research direction. We believe that a driving force for a restoration of this research field needs to be found elsewhere, for example, intimate collaborations with related industries. For this to be effectively realized, it would be helpful for academic side to understand the interests and demands of the industry side as well as to provide the industry with new scientific insights that would eventually lead to new applications. Therefore, this review covers some of the issues that are to be studied further and deeper, so-to-speak, lessons from the history of piezoceramics, and some technical issues that could be useful in better understanding the industry demands. As well, the efforts made in the industry side will be briefly introduced for the academic people to catch up with the recent trends and to be guided for setting up their future research direction effectively.ope

Effets de taille dans les poudres et céramiques de composés ferroélectriques relaxeurs à base de plomb PbMg1/3Nb2/3O3-PbTiO3 (PMN-PT)

CHATENAY MALABRY-Ecole centrale (920192301) / SudocSudocFranceF

Automating image labeling for remote sensing using cadastral database and video game engine simulation

In the remote sensing field, utilization of deep learning algorithms, such as Convolutional Neural Networks (CNNs) for automated detection is a commonly adopted approach, as reported by [1]. These techniques have demonstrated significant power and efficacy, largely due to the availability of increasingly large datasets and the rapid advancement in computing technology. However, the preparation of these datasets necessitates a substantial amount of manual labor, which is often outsourced to cost-efficient labor forces. In this paper, we present two methods developed to automate the labeling work for semantic segmentation and object detection tasks. We will analyze the results in terms of accuracy and time saved, and show how we've successfully applied them to two real-life projects

Automating the underground cadastral survey ::a processing chain proposal

In order to ensure the proper functioning and evolution of underground networks (water, gas, etc.) over time, municipal services need to maintain accurate and up-to-date maps. Such maps are generally updated using traditional data acquisition methods (total station or GNSS), which are time-consuming, expensive, and require several teams of surveyors in the field. In this context, an important topic of research is the automation of the updating of the underground cadastre in order to save time, money, and human effort. In this paper, we present a new method that we developed ranging from the choice of the acquisition system, the tests carried out in the field to the detection of objects and the automatic segmentation in a 3D point cloud. We have chosen to use a convolutional neural network on images for the detection of objects that are part of the underground cadastre. As the next step, objects are projected to obtain a 3D point cloud segmented based on the object type. The vectorization step is still under development so that objects can be converted to vector format and therefore be used for updating the cadastre. The results based on excavation sites with well-represented objects in our training database are excellent, approaching 96% accuracy. However, the detection of rare objects is much less good and thus remains a topic for future research. Overall, the complete processing chain allowing to automate as much as possible the update of an underground cadastre is presented in this paper

LARGE STRUCTURES: WHICH SOLUTIONS FOR HEALTH MONITORING?

Whatever the age of a large structure (dam, viaduct, cooling tower, nuclear containment, tunnel, …) It has to be periodically monitored. It is a challenge to realise these services when the access is limited and difficult for Man. This paper introduces a global approach, developed by SITES, through examples of application on different concrete dams or cooling towers, and their results. This global method involves three techniques: the SCANSITES® (a visual inspection system), the LIDAR (3D laser scanning) and high resolution photogrammetry