Optimal-Transport-Based Positive and Unlabeled Learning Method for Windshear Detection
Windshear is a microscale meteorological phenomenon that can be dangerous to aircraft during the take-off and landing phases. Accurate windshear detection plays a significant role in air traffic control. In this paper, we aim to investigate a machine learning method for windshear detection based on...
Saved in:
| Main Authors: | Jie Zhang, Pak-Wai Chan, Michael Kwok-Po Ng |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/23/4423 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multiple Instance Learning With Instance-Level Positive-Unlabeled Learning in Anomaly Detection
by: Ryosuke Matsuo, et al.
Published: (2025-01-01) -
Evidence of Terrain-Induced Windshear Due to Lantau Island over the Third Runway of the Hong Kong International Airport—Examples and Numerical Simulations
by: Pak Wai Chan, et al.
Published: (2024-12-01) -
A recent survey on instance-dependent positive and unlabeled learning
by: Chen Gong, et al.
Published: (2025-03-01) -
Disease candidate genes prediction using positive labeled and unlabeled instances
by: Sepideh Molaei, et al.
Published: (2025-04-01) -
A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
by: Zhigang Zhang, et al.
Published: (2024-10-01)