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Guoming Tang

4 papers hereh-index 25 citations7 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cs.DC1
  • cs.NI1
same name
  • Guoming Tang — 4 papers, h 2
  • Guoming Tang — 2 papers, h 1
  • Guoming Tang — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedEnergy Injection Identification enabled Disaggregation with Deep Multi-Task Learning

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

collaborators

4 papers

cs.LG2026★ 1 cited

Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning

Xudong Wang, Guoming Tang, Junyu Xue +3

Non-Intrusive Load Monitoring (NILM) offers a cost-effective method to obtain fine-grained appliance-level energy consumption in smart homes and building applications. However, the…

cs.NI2026

ARTSN: Exact and Adaptive Self-triggered Traffic Scheduling for ARTS Networks

Ruide Cao, Shuangping Zhan, Jiashuo Lin +4

Autonomous real-time systems (ARTS), such as self-driving vehicles and robotic assembly lines, are increasingly deployed to improve efficiency, accuracy, and responsiveness with re…

cs.DC2025

SAGkit: A Python SAG Toolkit for Response Time Analysis of Hybrid-Triggered Jobs

Ruide Cao, Zhuyun Qi, Qinyang He +3

For distributed control systems, modern latency-critical applications are increasingly demanding real-time guarantees and robustness. Response-time analysis (RTA) is useful for thi…

cs.LG2025

Prompting Large Language Models for Training-Free Non-Intrusive Load Monitoring

Junyu Xue, Xudong Wang, Xiaoling He +3

Non-intrusive load monitoring (NILM) aims to disaggregate total electricity consumption into individual appliance usage, thus enabling more effective energy management. While deep…

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