This work aims to determine the orthotropic linear elastic constitutive parameters of Pinus pinaster Ait. wood from a single uniaxial compressive experimental test, under quasi-static loading conditions, based on two different specimen configurations: (a) on-axis rectangular specimens oriented on the radial-tangential plane, (b) off-axis specimens with a grain angle of about 60° (radial-tangential plane). Using digital image correlation (DIC), full-field displacement and strain maps are obtained and used to identify the four orthotropic elastic parameters using the finite element model updating (FEMU) technique. Based on the FE data, a synthetic image reconstruction approach is proposed by coupling the inverse identification method with synthetically deformed images, which are then processed by DIC and compared with the experimental results. The proposed methodology is first validated by employing a DIC-levelled FEA reference in the identification procedure. The impact of the DIC setting parameters on the identification results is systematically investigated. This influence appears to be stronger when the parameter is less sensitive to the experimental setup used. When using on-axis specimen configuration, three orthotropic parameters of Pinus pinaster ($E_{\mathrm{R}}$, $E_{\mathrm{T}}$ and ${\nu }_{\mathrm{RT}}$) are correctly identified, while the shear modulus ($G_{\mathrm{RT}}$) is robustly identified when using off-axis specimen configuration.

We tackle the problem of localizing the traffic surveillance cameras in cooperative perception. To overcome the lack of large-scale real-world intersection datasets, we introduce Carla Intersection, a new simulated dataset with 75 urban and rural intersections in Carla. Moreover, we introduce a novel neural network, TrafficLoc, localizing traffic cameras within a 3D reference map. TrafficLoc employs a coarse-to-fine matching pipeline. For image-point cloud feature fusion, we propose a novel Geometry-guided Attention Loss to address cross-modal viewpoint inconsistencies. During coarse matching, we propose an Inter-Intra Contrastive Learning to achieve precise alignment while preserving distinctiveness among local intra-features within image patch-point group pairs. Besides, we introduce Dense Training Alignment with a soft-argmax operator to consider additional features when regressing the final position. Extensive experiments show that our TrafficLoc improves the localization accuracy over the state-of-the-art Image-to-point cloud registration methods by a large margin (up to 86%) on Carla Intersection and generalizes well to real-world data. TrafficLoc also achieves new SOTA performance on KITTI and NuScenes datasets, demonstrating strong localization ability across both in-vehicle and traffic cameras. Our project page is publicly available at https://tum-luk.github.io/projects/trafficloc/.

Purpose: This work aims to identify the linear elastic orthotropic material paramters of Pinus pinaster Ait. wood, using full-field measurements and an inverse identification strategy based on the finite element (FE) method updating technique.Design/methodology/approach: Compression tests are carried out under uniaxial and quasi-static loading conditions on wood specimens oriented on the radial-tangential (RT) plane, with different grain orientations. Full-field displacements and strains are measured using digital image correlation (DIC), which are then used as a reference in the identification procedure. A FE model is implemented assuming plane stress conditions, where wood is modelled as an orthotropic homogeneous material. Based on the numerical results, a synthetic image reconstruction scheme is implemented to synthetically deform the reference experimental image, which is then processed by DIC and further compared to the experimental results.Findings: The results for both approaches were similar when both specimen configurations were used in a single run. However, when using the DIC-based FEMU approach with the on-axis configuration, the identified modulus of elasticity in the tangential direction and shear modulus are closer to the reference values.Originality/value: This approach ensures a fair comparison between both sets of data since the full-field strain maps are obtained through the same filter and therefore have the same strain formulation, spatial resolution and data filtering. Firstly, the identification is performed using a single configuration, either the on-axis or the off-axis specimen. Secondly, the identification is carried out by merging data from both on-axis and off-axis configurations.

On the inverse identification of sheet metal mechanical behaviour using a heterogeneous Arcan virtual experiment HENRIQUES Joao, ANDRADE-CAMPOS António, XAV

Machine learning (ML) algorithms have been studied in literature as an inverse method to predict material constitutive parameters. However, these approaches are often dependent on the mesh discretisation settings applied during numerical simulations, and then difficulty model adaptation to experimental digital image correlation (DIC) subsets. Although a recent study explores the use of an interpolation-based approach to achieve experimental adaptation from numerically-based trained ML models, the proposed methodology lacks evaluation using experimental data. As a follow-up, this study proposes a new evaluation approach. Numerical data is DIC-levelled via MatchID software and then submitted to interpolation. An XGBoost algorithm is then trained on interpolated DIC data and evaluated for parameter prediction, comparing the obtained results with those obtained from the model trained on interpolated numerical data. Overall, the proposed DIC-levelling and interpolation pipeline yields an excellent predictive performance, with results comparable to those obtained when training on interpolated numerical data. The largest deviations are observed for the hardening exponent, while the remaining parameters are predicted with consistently high accuracy. These findings validate the practical applicability of the interpolation-based strategy to reduce the subset scheme dependency of ML models trained on real experimental data.