Author Page for Kuan Fu

Southeast University, Southeast University, PipeChina, Southeast University, No.4 Construction Company of China Petroleum Pipeline Engineering Co., Ltd., China University of Petroleum (Beijing) and Southeast University

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Citation 2

Abstract:

4.

A Latent Diffusion Enhanced Cnn with Attention Mechanism for Magnetic Flux Leakage Defect Quantification

Downloads 33 (1,304,549)

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A Latent Diffusion Enhanced Cnn with Attention Mechanism for Magnetic Flux Leakage Defect Quantification

Number of pages: 28 Posted: 01 Jul 2025

Xiaochen Gao, Jie Yang, Yuhan Zhang, Chang Su, Kuan Fu, Hongqun Chen, Jianjun Zhu and Jianli Wang

affiliation not provided to SSRN, Southeast University, affiliation not provided to SSRN, affiliation not provided to SSRN, PipeChina, affiliation not provided to SSRN, China University of Petroleum (Beijing) and Southeast University

Downloads 24 (1,459,262)

Abstract:

A Latent Diffusion Enhanced Cnn with Attention Mechanism for Magnetic Flux Leakage Defect Quantification

Number of pages: 28 Posted: 01 Jul 2025

Xiaochen Gao, Jie Yang, Yuhan Zhang, Chang Su, Kuan Fu, Hongqun Chen, Jianjun Zhu and Jianli Wang

affiliation not provided to SSRN, Southeast University, affiliation not provided to SSRN, affiliation not provided to SSRN, PipeChina, affiliation not provided to SSRN, China University of Petroleum (Beijing) and Southeast University

Downloads 9 (1,621,812)

Abstract:

5.

Quantitative Assessment of Pipeline Defects Utilizing a Dual-Stage Deep Learning Framework: Integration of Pretrained Yolov8 and Cnn Architectures on Magnetic Flux Leakage Data

Number of pages: 19 Posted: 13 May 2024

Jialiang Xie, Jie Yang, Kuan Fu, Linxi Tai, Xia Wang, Jianjun Zhu and Jianli Wang

Southeast University, Southeast University, PipeChina, Southeast University, No.4 Construction Company of China Petroleum Pipeline Engineering Co., Ltd., China University of Petroleum (Beijing) and Southeast University

Downloads 31 (1,304,549)

Abstract:

6.

DC-biased permeability modulation for ID/OD defects discrimination in thick-walled pipelines

Number of pages: 23 Posted: 13 Jan 2026

Jie Huang, Pengchao Chen, Rui Li, Kuan Fu and Zhenlin Li

China University of Petroleum (Beijing), PipeChina, PipeChina, PipeChina and affiliation not provided to SSRN

Downloads 18 (1,509,872)

Kuan Fu

PipeChina

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260

4

Scholarly Papers (6)

A Cascaded Deep Learning Approach for Detecting Pipeline Defects Via Pretrained Yolov5 and Vit Models Based on Mfl Data

Abstract:

Development and Simulation Analysis of a Novel Flexible Deformation Inspection Units (FDIU) for Oil and Gas Pipelines

Abstract:

Quantitative Assessment of Pipeline Defects Utilizing a Dual-Stage Deep Learning Framework: Integration of Pretrained Yolo Network and Multi-Input Parallel Convolution Architectures on Magnetic Flux Leakage Data

Abstract:

A Latent Diffusion Enhanced Cnn with Attention Mechanism for Magnetic Flux Leakage Defect Quantification

A Latent Diffusion Enhanced Cnn with Attention Mechanism for Magnetic Flux Leakage Defect Quantification

Abstract:

A Latent Diffusion Enhanced Cnn with Attention Mechanism for Magnetic Flux Leakage Defect Quantification

Abstract:

Quantitative Assessment of Pipeline Defects Utilizing a Dual-Stage Deep Learning Framework: Integration of Pretrained Yolov8 and Cnn Architectures on Magnetic Flux Leakage Data

Abstract:

DC-biased permeability modulation for ID/OD defects discrimination in thick-walled pipelines

Abstract: