The paper introduces a two‑stage AI framework that first estimates an unknown hydrogen leakage rate from early sensor data and then predicts the subsequent dispersion of the hydrogen cloud in confined maritime spaces. It outputs a concentration map with confidence bounds, enabling operators to assess the reliability of each forecast. The system, trained on an open dataset, achieves a mean absolute error of 0.052 g/s for leakage‑rate estimation and 0.018 for concentration prediction, approaching the sensor precision limit of ±0.01.
arXiv:2610.08363v1 Announce Type: new Abstract: Maritime systems operate in highly dynamic environments where unexpected equipment failures can compromise safety, reliability, and operational efficie...
COMET is a coordinated multi-layer runtime monitoring framework designed for human-supervised AI-enabled safety systems. It employs three monitoring layers—signal integrity, physics consistency, and the AI system’s competence envelope—whose outputs are combined and displayed via a traffic-light interface. Evaluated on a compressed‑air leak dataset, COMET eliminates the AI’s silent failures under covariate shift and most sensor faults, while maintaining a low false‑alarm rate.
The paper presents a machine‑learning approach (ML_CP) that automatically learns patterns and rules for complex event prediction, reducing reliance on manual rule creation. It incorporates sensitivity analysis to assess how output varies with each input and uses conformal prediction to generate uncertainty‑aware prediction intervals. Experiments on binary, multi‑level classification, and regression tasks show promising results for safety‑critical embedded systems.
Incorporating AI systems into safety systems requires monitoring to avoid silent faults and ensure safe and reliable operation. Single monitors often do not provide actionable information for human su...
arXiv:2509. 25017v2 Announce Type: replace Abstract: Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems.
arXiv:2609.14821v1 Announce Type: new Abstract: Earth system modeling is shifting from task-specific predictors toward foundation models with general spatiotemporal representation capabilities. Altho...
arXiv:2509.15959v2 Announce Type: replace-cross Abstract: Autonomous navigation in maritime domains is accelerating alongside advances in artificial intelligence, sensing, and connectivity. Opaque de...
arXiv:2606. 05274v1 Announce Type: new Abstract: Electro-Hydrostatic Actuators (EHAs) are widely used in aerospace and industrial systems, where timely detection of sensor anomalies is essential to ensure safe and reliable operation.
arXiv:2606. 14601v1 Announce Type: new Abstract: This study presents a statistical and machine learning framework for characterizing a hydrogen-based multi-energy system (H-MES) using one year of high-resolution operational data.
arXiv:2606. 17739v1 Announce Type: cross Abstract: Robotics are expected to support environmental monitoring and natural disaster management, where decisions must be made under uncertainty, resource limitations, and strict operational constraints.
arXiv:2606.17739v2 Announce Type: replace-cross Abstract: Robotics are expected to support environmental monitoring and disaster detection, where decisions must be made under uncertainty, resource li...
The article discusses how the rapid development of artificial intelligence (AI) and machine learning (ML) is transforming chemical engineering by influencing problem formulation, analysis, and solution across a wide range of applications, from atomic-scale simulations to industrial operations. It highlights recent methodological advances and representative uses, noting a shift from purely black-box models to hybrid and physics-informed frameworks that incorporate conservation laws, thermodynamic consistency, and structural constraints. These integrated approaches enhance robustness, reliability, and human-AI collaboration, ultimately amplifying rather than replacing core chemical engineering principles.