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Talent Scout

J

Jiakang Shen

Shandong University

Q

Qinghui Chen

Shandong University

R

Runtong Wang

Shandong University

C

Chenrui Xu

Shandong University

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References (24)

[1]
Benchmark dataset and deep learning method for global tropical cyclone forecasting
2025Cheng Huang, Pan Mu et al.
[2]
A fast physics-based perturbation generator of machine learning weather model for efficient ensemble forecasts of tropical cyclone track
2025Jingchen Pu, Mu Mu et al.
[3]
Tropical cyclone intensity estimation based on YOLO-NAS using satellite images in real time
2025P. Nandal, Prerna Mann et al.
[4]
Phy-CoCo: Physical Constraint-Based Correlation Learning for Tropical Cyclone Intensity and Size Estimation
2024Hanting Yan, Pan Mu et al.
[5]
The Development of Experimental Remote Sensing Cubesat Payload Integrated With On-Board Classification Feature: The Progress and Educational Aspect
2023Edwar, S. Oktaviani et al.
[6]
Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method
2023J. Kumar, V. Venkataraman et al.
[7]
A Lightweight Multitask Learning Model With Adaptive Loss Balance for Tropical Cyclone Intensity and Size Estimation
2023Wei Tian, Xinxin Zhou et al.
[8]
A Physics-guided NN-based Approach for Tropical Cyclone Intensity Estimation
2023Ziheng Zhou, Ying Zhao et al.
[9]
Faster and Lighter Meteorological Satellite Image Classification by a Lightweight Channel-Dilation-Concatenation Net
2023Shuyao Shang, Jinglin Zhang et al.
[10]
Scanner Neural Network for On-Board Segmentation of Satellite Images
2022Gaétan Bahl, Florent Lafarge
[11]
MMSTN: A Multi‐Modal Spatial‐Temporal Network for Tropical Cyclone Short‐Term Prediction
2022Cheng Huang, Cong Bai et al.
[12]
The Φ-Sat-1 Mission: The First On-Board Deep Neural Network Demonstrator for Satellite Earth Observation
2022Gianluca Giuffrida, L. Fanucci et al.
[13]
Physics-augmented Deep Learning to Improve Tropical Cyclone Intensity and Size Estimation from Satellite Imagery
2021Jingwei Zhuo, Z. Tan
[14]
Tropical Cyclone Intensity Classification and Estimation Using Infrared Satellite Images With Deep Learning
2021Changjiang Zhang, Xiaojie Wang et al.
[15]
RGB Image Prioritization Using Convolutional Neural Network on a Microprocessor for Nanosatellites
2020Ji Hyun Park, T. Inamori et al.
[16]
Real-time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data
2020Boyo Chen, Buo‐Fu Chen et al.
[17]
Evaluation of a Physics-Based Tropical Cyclone Rainfall Model for Risk Assessment
2020D. Xi, N. Lin et al.
[18]
Tropical Cyclone Intensity Estimation Using Two-Branch Convolutional Neural Network From Infrared and Water Vapor Images
2020Rui Zhang, Qingshan Liu et al.
[19]
Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery
2019A. Wimmers, C. Velden et al.
[20]
Physical understanding of the tropical cyclone wind-pressure relationship
2017D. Chavas, K. Reed et al.

Showing 20 of 24 references

Founder's Pitch

"KAN-FIF: Real-time tropical cyclone estimation on edge devices using lightweight neural networks for accurate disaster management."

Meteorological AIScore: 8View PDF ↗

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5

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10

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7.5

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Why It Matters

The research introduces a method to efficiently estimate tropical cyclones on resource-constrained edge devices, crucial for real-time disaster management, reducing latency in predicting destructive events, and extending beyond current centralized methods.

Product Angle

The product can be packaged as a firmware or software upgrade for satellite operators, or as a standalone API for organizations involved in weather prediction and natural disaster management.

Disruption

It could potentially replace existing centralized tropical cyclone prediction systems by enabling more rapid and local processing on existing satellite infrastructure.

Product Opportunity

The growing need for accurate and rapid disaster prediction offers enormous market potential, specifically among governments, weather agencies, insurers, and emergency response services.

Use Case Idea

Deploy KAN-FIF on weather satellites for real-time monitoring and prediction of tropical cyclones, aiding governments and agencies in disaster preparedness and response planning.

Science

The study develops a Kolmogorov-Arnold Network-based Feature Interaction Framework (KAN-FIF) combining MLP, CNN, and spline-parameterized KAN layers to predict Maximum Sustained Wind (MSW) using less parameters and processing time than traditional methods, thus enabling edge device inference.

Method & Eval

The system was evaluated on meteorological data for its capability to accurately predict MSW and operational feasibility on satellite hardware, outperforming the baseline model Phy-CoCo in speed and parameter efficiency.

Caveats

Real-world satellite integration may face unforeseen compatibility issues; Additionally, any inaccuracies in predictions could have significant safety implications.

Author Intelligence

Jiakang Shen

Shandong University
202300171054@mail.sdu.edu.cn

Qinghui Chen

Shandong University
202420785@mail.sdu.edu.cn

Runtong Wang

Shandong University
202300171170@mail.sdu.edu.cn

Chenrui Xu

Shandong University
202300171055@mail.sdu.edu.cn

Jinglin Zhang

Shandong University
jinglin.zhang@sdu.edu.cn

Cong Bai

Zhejiang University of Technology
congbai@zjut.edu.cn

Feng Zhang

Fudan University
fengzhang@fudan.edu.cn