CaliFormer: Leveraging Unlabeled Measurements to Calibrate Sensors with Self-supervised Learning-Reference-Cited by-同舟云学术

CaliFormer: Leveraging Unlabeled Measurements to Calibrate Sensors with Self-supervised Learning

Published:2023-10-08 Issue: Volume: Page:
ISSN:
Container-title:Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing
language:
Short-container-title:

Author:

Wang Haoyang¹^ORCID,Liu Yuxuan²^ORCID,Zhao Chenyu¹^ORCID,He Jiayou³^ORCID,Ding Wenbo⁴^ORCID,Chen Xinlei⁵^ORCID

Affiliation:

1. Shenzhen International Graduate School, Tsinghua University, China

2. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, China

3. School of Engineering, Hong Kong University of Science and Technology, China

4. Shenzhen International Graduate School, Tsinghua University, China and Pengcheng Laboratory, China and RISC-V International Open Source Laboratory, China

5. Shenzhen International Graduate School, Tsinghua University, China and Pengcheng Laboratory, China

Funder

Shenzhen 2022 Stabilization Support Program

Guangdong Innovative and Entrepreneurial Research Team Program

National Key R&D program of China

Tsinghua Shenzhen International Graduate School Cross-disciplinary Research and Innovation Fund Research Plan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3594739.3612917

Reference38 articles.

1. Jieming Bian , Zhu Fu , and Jie Xu. 2021. FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning. arXiv preprint arXiv:2110.07829 ( 2021 ). Jieming Bian, Zhu Fu, and Jie Xu. 2021. FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning. arXiv preprint arXiv:2110.07829 (2021).

2. Xuecheng Chen Haoyang Wang Zuxin Li Wenbo Ding Fan Dang Chengye Wu and Xinlei Chen. 2022. DeliverSense: Efficient Delivery Drone Scheduling for Crowdsensing with Deep Reinforcement Learning. In Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers. 403–408. Xuecheng Chen Haoyang Wang Zuxin Li Wenbo Ding Fan Dang Chengye Wu and Xinlei Chen. 2022. DeliverSense: Efficient Delivery Drone Scheduling for Crowdsensing with Deep Reinforcement Learning. In Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers. 403–408.

3. PAS: Prediction-Based Actuation System for City-Scale Ridesharing Vehicular Mobile Crowdsensing

4. Adaptive Hybrid Model-enabled Sensing System (HMSS) for Mobile Fine-Grained Air Pollution Estimation

5. HAP

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Physics-informed Neural ODE for Post-disaster Mobility Recovery;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. MobiAir: Unleashing Sensor Mobility for City-scale and Fine-grained Air-Quality Monitoring with AirBERT;Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services;2024-06-03

3. QUEST: Quality-informed Multi-agent Dispatching System for Optimal Mobile Crowdsensing;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20

4. Demo Abstract: Bio-inspired Tactile Sensing for MAV Landing with Extreme Low-cost Sensors;2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN);2024-05-13

5. Sens-BERT: A BERT-Based Approach for Enabling Transferability and Re-Calibration of Calibration Models for Low-Cost Sensors Under Reference Measurements Scarcity;IEEE Sensors Journal;2024-04-01