activity
20232026
most citedFoundation Models for CPS-IoT: Opportunities and Challenges

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2026

TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering

Liying Han, Kang Yang, Oliver Wang +9

Large language models (LLMs) and time-series language models (TSLMs) are increasingly applied to time-series question answering (TSQA). Unlike text-only QA, TSQA requires models to…

cs.LG20251 cited

Foundation Models for CPS-IoT: Opportunities and Challenges

Ozan Baris, Yizhuo Chen, Gaofeng Dong +9

Methods from machine learning (ML) have transformed the implementation of Perception-Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of Things…

cs.AI2024

State-of-the-Art Review: The Use of Digital Twins to Support Artificial Intelligence-Guided Predictive Maintenance

Sizhe Ma, Katherine A. Flanigan, Mario Bergés

In recent years, predictive maintenance (PMx) has gained prominence for its potential to enhance efficiency, automation, accuracy, and cost-effectiveness while reducing human invol…

eess.SP2024

Unmasking the Role of Remote Sensors in Comfort, Energy and Demand Response

Ozan Baris Mulayim, Edson Severnini, Mario Bergés

In single-zone multi-node systems (SZMRSs), temperature controls rely on a single probe near the thermostat, resulting in temperature discrepancies that cause thermal discomfort an…

cs.AI2023

State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies

Sizhe Ma, Katherine A. Flanigan, Mario Bergés

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance processes.…