arXiv:2606. 06156v1 Announce Type: new Abstract: Machine learning-based predictive emissions monitoring systems offer a practical alternative to direct emissions measurement, but their deployment across gas turbine fleets is challenging when emissions labels are available for only a small subset of assets.
arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.
arXiv:2609. 08375v1 Announce Type: cross Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants.
The paper introduces a reliability‑aware short‑term roll prediction framework for unmanned surface vehicles (USVs) that combines a multi‑task learning architecture with an adaptive centralization strategy. The model uses a shared backbone to feed a regression head for precise roll prediction and a quantification head for confidence scoring, enabling accurate predictions alongside reliability estimates. Experiments on a real‑sea dataset show that the approach effectively quantifies prediction reliability and generalizes well across varying operational conditions.
The paper introduces PLSP (Pre-hoc Liminal Space Profiling), an anticipatory framework for predicting out-of-distribution (OOD) data before inference. It proposes a dataset‑independent metric called the CREDibility Score (CREDS) and introduces credibility curves and heat maps to analyze a model’s maximum credibility and behavior across datasets. Experiments on multiple datasets show that CREDS can improve model robustness to OOD prediction.
arXiv:2602. 13010v2 Announce Type: replace Abstract: Accurate production forecasts are essential for the integration of renewable energy sources into the power grid.
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.
The paper introduces a method for learning from multiple experts who provide interval labels, addressing both within‑label imprecision and between‑expert variation. It harmonizes diverse label vocabularies into a shared probabilistic space, retains individual intervals using a mixture of Beta distributions, and decomposes predictive uncertainty into components that are matched to their corresponding sources of label uncertainty. On sea‑ice concentration data, the approach achieves a 31% reduction in mean absolute error compared to hard‑label baselines and outperforms several aggregation and interval‑regression methods.
arXiv:2609.24358v1 Announce Type: new Abstract: Predicting when maritime systems require maintenance can be critical, avoiding hazards and costly consequences. To address this problem, this paper pro...
arXiv:2608.29024v1 Announce Type: new Abstract: Wind power ramp events which are sudden, large swings in turbine output over short windows are difficult to estimate, and standard models often miss th...
arXiv:2608.30323v1 Announce Type: new Abstract: Normal behavior models have shown promise for reliable fault detection in wind turbines. However, these unsupervised anomaly detection models require s...
arXiv:2606. 26710v1 Announce Type: new Abstract: Combined-cycle gas turbines (CCGTs) play a key role in modern power generation, offering both high efficiency and reduced environmental impact.
arXiv:2607. 19054v1 Announce Type: new Abstract: In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations.