#semantic-modeling News & Analysis

4 articles tagged with #semantic-modeling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 235/10

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Towards a Bathroom-Centered Human-Building Digital Twin Framework for Indoor Safety Analysis

Researchers propose a digital twin framework that combines semantic bathroom environment modeling with human skeleton tracking to analyze safety risks for older adults. The system integrates body-environment interaction data to better understand fall and injury risks in bathrooms, a critical safety challenge for aging populations, with a Unity-based prototype demonstrating feasibility.

GeneralNeutralarXiv – CS AI · Jun 106/10

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A Survey on Semantic Modeling for Building Energy Management

A comprehensive survey examines semantic modeling approaches for Building Energy Management (BEM), analyzing 60 semantic models and 20+ ontology-based use cases to address data interoperability challenges. The research identifies significant gaps in how current ontologies represent abstract operational concepts like performance indicators and control logic, highlighting the need for more integrated semantic frameworks to enable autonomous, context-aware building systems.

AINeutralarXiv – CS AI · Jun 95/10

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DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling

DynaOD is a machine learning framework that generates realistic urban mobility patterns by modeling temporal dynamics through discrete directional trends and continuous evolution, without requiring historical origin-destination data. The approach uses semantic temporal signals to condition pretrained OD generators, achieving better accuracy and distributional fidelity than existing methods with cross-city transferability.

AINeutralarXiv – CS AI · Jun 15/10

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SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction

Researchers propose SKETCH, a semantic key-point-conditioned framework that improves long-horizon vessel trajectory prediction by decomposing the problem into high-level navigational intent and local motion modeling. The method outperforms existing approaches on real-world AIS data, particularly for extended time horizons and directional accuracy.