Search

With the improvement in sensor technologies, air quality is increasingly being monitored. Two major factors in obtaining relevant information are the optimal placement and the number of air quality sensors. Moreover, in cases of poor air quality, the information of the pollution level given by the deployed sensors is not sufficient. An advanced understanding of the data is required to precisely identify the source pollution and thus propose effective solutions. In this article, a virtual testing strategy based on computational fluid dynamics (CFD) is presented for the optimal placement of indoor air quality sensors. We determine the placement of sensors in view of localizing the maximum of sources emitting on the indoor environment surfaces. Therefore, an adjoint framework is used to obtain the observable region associated with a given sensor position. The proposed method takes into account technical sensor features, such as the limit of detection (LOD). Two applications are studied: a simple 2D case and a real 3D room. In these examples, we first show that reducing the LOD of the sensors by one order of magnitude can increase the observable area by more than 50 % . Then, we note that one-fourth of the potential sensor placements observe almost nothing and that 80 % of the potential sensor placements have an observable area two times smaller than the optimal sensor position determined by the proposed CFD-based strategy.

This study focuses on the experimental characterization of the bond behavior between concrete and Fiber Reinforced Polymer (FRP) reinforcing bars (rebars). Pull-out tests were performed on glass, carbon, and aramid FRP rebars, as well as on deformed steel rebars. The influence of various parameters on the bond behavior was studied, such as the type of fibers, the diameter of the FRP bars and their surface geometry. Scanning-Electron-Microscope (SEM) observations were performed to precisely study the sand coating characteristics of these rebars. A main originality of the proposed approach relied on the instrumentation of pull-out samples using Distributed Optical Fiber Sensing (DOFS) instrumentation. Such a distributed measurement system provided access to the longitudinal strain distribution along the rebar near the rebar-concrete interface, and then made it possible to determine the effective development length of the various types of rebars considered in this study. As the introduction of DOFS instrumentation may be intrusive, its influence on the interface behavior was also discussed.

We present an original method using a low cost accelerometer and a Kalman-filter based algorithm to monitor cardiopulmonary resuscitation chest compressions (CC) depth. A three-axis accelerometer connected to a computer was used during CC. A Kalman filter was used to retrieve speed and position from acceleration data. We first tested the algorithm for its accuracy and stability on surrogate data. The device was implemented for CC performed on a manikin. Different accelerometer locations were tested. We used a classical inertial navigation algorithm to reconstruct CPR depth and frequency. The device was found accurate enough to monitor CPR depth and its stability was checked for half an hour without any drift. Average error on displacement was ±0.5mm. We showed that depth measurement was dependent on the device location on the patient or the rescuer. The accuracy and stability of this small low-cost accelerometer coupled to a Kalman-filter based algorithm to reconstruct CC depth and frequency, was found well adapted and could be easily implemented.

International audience; Flexural resonators operated within a fluid are most of the time limited by viscous damping. Nonetheless, depending on the resonator geometry, significant acoustic damping has also to be considered. After reviewing the main theoretical approaches proposed in the literature to evaluate acoustic losses, we provide useful and simplified expressions to quantify their corresponding quality factor. Original results are obtained by extending well-known methods to different shapes. We also present numerical simulations and experiments to compare and assess the validity of our predictions, and show that analytical models combining both viscous and acoustic damping can give reliable values for the observed quality factors.

TRA 2016, European Research and Technology Conference on Transport and Mobility, VARSOVIE, POLOGNE, 18-/04/2016 - 21/04/2016; Recently, IFSTTAR has taken an interest in the use of probe vehicles for the road monitoring of some characteristics such as the pavement longitudinal profile. After studying the feasibility of the method on a metrological angle, a measurement chain and an automated data processing tool have been developed. The process begins with the data collection using low-cost sensors embedded in each probe vehicle and ends with the presentation to the road managers of the profile quality indicator matched on a map. Thereafter, this innovative pavement monitoring method has been tested with the deployment of a dozen of probe vehicles on a road network of about one thousand kilometers long. This article aims to present, in a first step, the different tools developed by IFSTTAR (data collection systems, map-matching algorithms, database format, etc.) and then, in a second step, to comment the results coming from the application of the pavement monitoring method on the road test network (level of accuracy, technological and scientific limits, opinion of the road manager, etc.).

A two-dimensional (2D) concentration field may be reconstructed using inverse advection–diffusion–reaction based on pointwise sensor outputs. The studied reconstruction process consists in defining fictitious boundaries and identifying the optimal concentrations on these boundaries that minimize the data misfit. The minimization is performed by means of the adjoint method. The reconstruction process is observed to fail for some sets of sensor placements. Therefore, in this paper, we aim to develop a low computational cost framework that can be used to identify badly placed sensors and to reveal potentially optimal sensor placement. From this framework, we have derived a sensitivity tool that can predict, for a given set of sensors, which boundary concentrations are easy or difficult to identify using the inverse method. This tool only requires the resolution of an adjoint problem. It is compared with the 'sensitivity-to-observation' method in a 2D water pipe junction with 2000 Reynolds number and 1000 Peclet number. The compared methods do produce similar quantitative and qualitative results.

This paper introduces an online pedestrian crossing detection system that uses pre-existing traffic-oriented video-sensors which, at regular intervals, provide coarse spatial measurements on areas along a crosswalk. Pedestrian crossing detection is based on the recognition of occupancy patterns induced by pedestrians when they move on the crosswalk. In order to improve the ability of non-dedicated sensors to detect pedestrians, we introduce an evidential-based data fusion process that exploits redundant information coming from one or two sensors: intra-sensor fusion uses spatiotemporal characteristics of the measurements, and inter-sensor fusion uses redundancy between the two sensors. As part of the EU funded TRACKSS project on cooperative advanced sensors for road traffic applications, real data have been collected on an urban intersection equipped with two cameras. The results obtained show that the data fusion process enhances the quality of occupancy patterns obtained and leads to high detection rates of pedestrian crossings with multi-purpose sensors in operational conditions, especially when a secondary sensor is available.