En este trabajo se presentan los desarrollos que se han realizado en instituciones de Uruguay, relativos a patrones de las magnitudes de tiempo y frecuencia. El período analizado cubre unos 80 años. Por tanto, las tecnologías fueron muy diversas, desde osciladores basado en cristales de cuarzo y electrónicas de válvulas de vacío, hasta modernos relojes de cesio, basados en la transición de sus niveles electrónicos. Las estabilidades relativas, por consiguiente, cubren varios órdenes de magnitud, desde 10-7 hasta 10-13, lo que demuestra el gran avance que ha tenido el desarrollo de esta magnitud física en Uruguay. El estado actual del patrón Nacional del Uruguay, basado en relojes de cesio, ha permitido llegar a incertidumbres suficientemente bajas para ser admitido como parte del sistema internacional que define la hora UTC (Tiempo Universal Coordinado).

Overshooting defects in shipbuilding subassemblies are essential to ensure the final product’s overall integrity and safety. In this work, we focus on the automatic detection of overshooting defects in simple and T-shaped sub-assemblies by employing reconstruction-based unsupervised learning on 3D point clouds. To this purpose, we implemented and compared four state-of-the-art architectures, including a Variational Autoencoder (VAE), FoldingNet, a Dynamic Graph CNN (DGCNN) autoencoder, and a PointNet++ autoencoder. These architectures were trained exclusively on defect-free samples, anticipating the possibility of overshooting defects occurring in different locations and with varying geometric patterns that are difficult to characterize explicitly in advance. Those defects are then identified by applying an Isolation Forest to the reconstruction error features, enabling fully unsupervised anomaly detection and allowing us to study how the detection performance changes with the contamination parameter. The results show that reconstruction-based anomaly detection on point clouds is a viable strategy for identifying defects in an industrial environment and the importance of choosing architectures that balance detection performance, stability across different geometries, and computational cost.

The digitalization of shipbuilding processes has become an important trend in modern naval construction, enabling more efficient design, assembly, and maintenance operations. A key aspect of this digital transformation is traceability, which ensures that every component and step in the shipbuilding process can be accurately tracked and managed. Traceability is critical for quality assurance, safety, and operational efficiency, especially when it comes to identifying and addressing defects that may arise during construction. In this context, defect traceability plays a key role, enabling manufacturers to track the origin, type, and evolution of issues throughout the production process, which are fundamental for maintaining structural integrity and preventing failures. In this paper, we focus on the detection of defects in minor and simple pre-assemblies, which are among the smallest components that form the building blocks of ship assemblies. These components are essential to the larger shipbuilding process, yet their defects can propagate and lead to more significant issues in the overall assembly if left unaddressed. For that reason, we propose an intelligent approach to defect detection in minor and simple pre-assembly pieces by implementing unsupervised learning with convolutional autoencoders (CAEs). Specifically, we evaluate the performance of five different CAEs: BaseLineCAE, InceptionCAE, SkipCAE, ResNetCAE, and MVTecCAE, to detect overshooting defects in these components. Our methodology focuses on automated defect identification, providing a scalable and efficient solution to quality control in the shipbuilding process.

La irrupción de los modelos de lenguaje de gran tamaño (LLM) ha abierto nuevas vías para la interacción con sistemas industriales complejos, en particular con plataformas SCADA. Este trabajo presenta una propuesta de arquitectura para la integración de agentes basados en LLM con sistemas SCADA mediante el protocolo abierto MCP (Model Context Protocol), comparándolo con la alternativa de las llamadas skills, instrucciones en lenguaje natural que orquestan herramientas preexistentes. Se identifican las limitaciones estructurales del uso de herramientas genéricas de bajo nivel, especialmente la alucinación de identificadores en cadenas largas y repetitivas, y se justifica el diseño de herramientas específicas de alto nivel. Se describe una arquitectura modular replicable y se reportan los resultados obtenidos en un caso de estudio industrial, donde la propuesta permite reducir significativamente el número de invocaciones requeridas y aumentar la fiabilidad de las respuestas frente al enfoque basado en skills.