Multi‐step performance degradation prediction method for proton‐exchange membrane fuel cell stack using 1D convolution layer and CatBoost DOI
Zehui Zhang,

Tianhang Dong,

Xiaobin Xu

et al.

International Journal of Adaptive Control and Signal Processing, Journal Year: 2024, Volume and Issue: unknown

Published: June 14, 2024

Summary The increasing environmental issues such as climate change and air pollution require energy saving emission reduction in various fields, manufacturing, building, transportation. To address the above problem, proton‐exchange membrane fuel cells (PEMFC) gradually become promising green conversion device due to advantages of zero pollution, high efficiency, low operating noise. However, durability problem has extremely limited PEMFC large‐scale commercial application. prolong service life PEMFC, performance degradation prediction is an effective method. This paper proposes a multi‐step method for based on CatBoost feature selection, convolution computing, interactive learning mechanism. used evaluate importance monitor parameters degradation. evaluation results mechanism analyses are select construing model. Based 1D convolutional layer mechanism, model proposed extract deep features from data predict cell system. In particular, performed by configurable sliding window. effectiveness verified real experiment datasets, show that particularly decreases computation selection layer.

Language: Английский

Bidirectional gated recurrent unit with auto encoders for detecting arrhythmia using ECG data DOI

R. Sarankumar,

M. Ramkumar,

K. Vijaipriya

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 294, P. 111696 - 111696

Published: March 24, 2024

Language: Английский

Citations

11
,Object Tracking using Optimized Dual Interactive Wasserstein Generative Adversarial Network from Surveillance Video DOI
Karthik Srinivasan

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113084 - 113084

Published: Feb. 1, 2025

Language: Английский

Citations

0
Anti-Motion Interference Electrocardiograph Monitoring System: A Review DOI

Jiajun Shen,

Xiao Li, Yanhao Wang

et al.

IEEE Sensors Journal, Journal Year: 2024, Volume and Issue: 24(10), P. 15727 - 15747

Published: April 8, 2024

Language: Английский

Citations

1
Multi‐step performance degradation prediction method for proton‐exchange membrane fuel cell stack using 1D convolution layer and CatBoost DOI
Zehui Zhang,

Tianhang Dong,

Xiaobin Xu

et al.

International Journal of Adaptive Control and Signal Processing, Journal Year: 2024, Volume and Issue: unknown

Published: June 14, 2024

Summary The increasing environmental issues such as climate change and air pollution require energy saving emission reduction in various fields, manufacturing, building, transportation. To address the above problem, proton‐exchange membrane fuel cells (PEMFC) gradually become promising green conversion device due to advantages of zero pollution, high efficiency, low operating noise. However, durability problem has extremely limited PEMFC large‐scale commercial application. prolong service life PEMFC, performance degradation prediction is an effective method. This paper proposes a multi‐step method for based on CatBoost feature selection, convolution computing, interactive learning mechanism. used evaluate importance monitor parameters degradation. evaluation results mechanism analyses are select construing model. Based 1D convolutional layer mechanism, model proposed extract deep features from data predict cell system. In particular, performed by configurable sliding window. effectiveness verified real experiment datasets, show that particularly decreases computation selection layer.

Language: Английский

Citations

1