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A robust inverse method for the complex wavenumber space (complex k-space) extraction is essential for structural vibration and damping analysis of two-dimensional structures. Most existing methods suffer from extracting the reliable complex k-space of plates in the presence of realistic uncertainties, especially for plates with low damping properties. To this end, this paper presents a new method for extracting the dispersion and damping characteristics of two-dimensional periodic structures using only the full-field displacement fields as input. The proposed method, the Algebraic K-Space Identification 2D technique (AKSI 2D), is an extension of the Algebraic Wavenumber Identification technique to solve two-dimensional problems. The optimised formulas are developed within the algebraic identification framework, which allows the extraction of all the properties of the complex k-space in a comprehensive way. The proposed method is validated numerically and experimentally, and its performances are compared with other popular k-space identification methods under different uncertainty conditions. The test cases cover analytically solved isotropic fields to numerically solve orthotropic fields and finally experimental measurements. The different cases show promising results and demonstrate that the proposed method is a robust tool to characterise the wave propagation of two-dimensional structures under stochastic structural and constitution conditions., QC 20240927

Emergent manufacturing paradigms, including ubiquitous manufacturing, social manufacturing, and agile manufacturing, facilitate the advancement of the manufacturing industry. However, these paradigms suffer from the difficulty of interoperability due to the heterogeneity of field devices and diverse communication protocols. The OLE for Process Control Unified Architecture (OPC UA) technique, which is featured with cross-platform, is considered as the pivotal technology for addressing this issue. However, there still exist a large number of heterogeneous devices which do not support OPC UA protocol. The communication protocols of these heterogeneous devices need to be converted into OPC UA based on information model, nevertheless, the manual construction of OPC UA information model is cumbersome and inefficient. In this paper, an integrated architecture based on reasoning over the OPC UA information model is proposed to realize rapid integration of heterogeneous devices to achieve interoperability. To realize the proposed integrated architecture, an automatic device information model construction method is developed simultaneously. The method first identifies the type of the newly accessed device based on the character-level TextCNN (CTCNN) model, which utilizes the word sequence extracted from the corresponding device data frame as input. Subsequently an open-world knowledge completion model is adopted to link the unseen entities in the device data frame to the information model knowledge graph(KG) to support the information model automatic construction for unknown device. For evaluation purpose, two industrial datasets are constructed and the results demonstrate the feasibility and effectiveness of the proposed method., QC 20240229

Modern edge real-time automotive applications are becoming more complex, dynamic, and distributed, moving away from conventional static operating environments to support advanced driving assistance and autonomous driving functionalities. This shift necessitates formulating more complex task models to represent the evolving nature of these applications aptly. Modeling of real-time automotive systems is typically performed leveraging Architectural Languages (ALs) such as Amalthea, which are commonly used by the industry to describe the characteristics of processing platforms, operating systems, and tasks. However, these architectural languages are originally derived for classical automotive applications and need to evolve to meet the needs of next-generation applications. This paper proposes an automatic framework for the modeling and automatic code generation of dynamic automotive applications under the QNX RTOS. To this end, we extend Amalthea to describe chains of communicating tasks with multiple operating modes and to consider the QNX's reservation-based scheduler, called APS, which allows providing temporal isolation between applications co-located on the same hardware platform. Finally, an evaluation is presented to compare different implementation alternatives under QNX that are automatically generated by our code generation framework., QC 20240813

Ultrasound imaging for Non Destructive Testing is frequently performed in an immersion setup, where water is used as coupling medium between the probe and the component under test. For the computation of the time delays needed for beam-forming, the shape of the component surface and probe location and orientation (PLO) must be known. In this work we develop methods for the automatic detection of the surface and the estimation of PLO for 2D array probes. In particular, the methods developed apply to three types of elementary surfaces which are usually found in industrial and structural components: planes, cylinders and spheres. The methods use the measured surface echoes Time of Flight (TOF) to fit parametric models based on ray propagation and reflection on the surface, giving the coordinates and Euler angles that define the PLO relative to the component under test. Validation experiments with four test specimens representing the three types of surfaces are presented. The accuracy and precision of estimated PLO coordinates and angles are analyzed, and a Total Focusing Method (TFM) imaging example is shown achieving a correct detection of artificial defects in the component for a different PLOs.

Continuous underwater Passive Acoustic Monitoring (PAM) has emerged as a strong tool for cetacean research. To handle the vast volume of collected data, it is essential to employ automated detection and classification methods. The recent advancement of deep learning, involving model training and testing, requires a large amount of labeled data. These labels are derived through the manual annotation of audio files often reliant on human experts. Based on an annotation campaign focusing on blue whale calls in the Indian Ocean involving 19 novice annotators and an expert in bioacoustics, this study explores the integration of novice annotators in marine bioacoustics research, through citizen science programs, which could drastically increase the size of labeled datasets and enhance the performance of detection and classification models. The analysis reveals distinctive annotation profiles influenced by the complexity of vocalizations and the annotators' strategies, ranging from conservative to permissive. To address the challenges of annotation discrepancies, Convolutional Neural Networks (CNNs) are trained on annotations from both novices and the expert. The results show variations in model performance. Our work highlights the importance of annotation guidelines encouraging a more conservative approach to improve overall annotation quality. In an effort to optimize the potential of multi-annotation and mitigate the presence of noisy labels, two annotation aggregation methods (majority voting and soft labeling) are proposed and tested. The results demonstrate that both methods, particularly when a sufficient number of annotators are involved, significantly improve model performance and reduce variability: the standard deviation of the area under PR and ROC curves fall under 0.02 for both vocalizations with 13 aggregated annotators, while it was at 0.17 and 0.21 for the Blue Whale Dcalls and 0.05 and 0.04 for the SEIO PBW vocalizations with all annotator separately. Mor

In the Dot task, children and adults involuntarily compute an avatar's visual perspective, which has been interpreted by some as automatic Theory of Mind. This interpretation has been challenged by other researchers arguing that the task reveals automatic attentional orienting. Here we tested a new interpretation of previous findings: the seemingly automatic processes revealed by the Dot task result from the high Executive Control demands of this verification paradigm, which taxes short-term memory and imposes perspective-switching costs. We tested this hypothesis in three experiments conducted in India with newly sighted children (Experiment 1; N = 5; all girls), neurotypical children (Experiment 2; ages 5-10; N = 90; 38 girls) and adults (Experiment 3; N = 30; 18 women) in a highly simplified version of the Dot task. No evidence of automatic perspective-taking was observed, although all groups revealed perspective-taking costs. A newly sighted child and the youngest children in our sample also showed an egocentric bias, which disappeared by age 10, confirming that visual perspective taking develops during the school years. We conclude that the standard Dot task imposes such methodological demands on both children and adults that the alleged evidence of automatic processes (either mindreading or domain general) may simply reveal limitations in Executive Control.

With the rapid and continuous advancements in technology, as well as the constantly evolving competences required in the field of engineering, there is a critical need for the harmonization and unification of engineering professional figures or archetypes. The current limitations in tymely defining and updating engineers' archetypes are attributed to the absence of a structured and automated approach for processing educational and occupational data sources that evolve over time. This study aims to enhance the definition of professional figures in engineering by automating archetype definitions through text mining and adopting a more objective and structured methodology based on topic modeling. This will expand the use of archetypes as a common language, bridging the gap between educational and occupational frameworks by providing a unified and up-to-date engineering professional figure tailored to a specific period, specialization type, and level. We validate the automatically defined industrial engineer archetype against our previously manually defined profile., QC 20230904