34 citations · 82 across the 30 of their papers we have counts for
7 papers · 1 filter
Time-EAPCR-T: A Universal Deep Learning Approach for Anomaly Detection in Industrial Equipment
Huajie Liang, Di Wang, Yuchao Lu +7
With the advancement of Industry 4.0, intelligent manufacturing extensively employs sensors for real-time multidimensional data collection, playing a crucial role in equipment moni…
Time-EAPCR: A Deep Learning-Based Novel Approach for Anomaly Detection Applied to the Environmental Field
Lei Liu, Yuchao Lu, Ling An +3
As human activities intensify, environmental systems such as aquatic ecosystems and water treatment systems face increasingly complex pressures, impacting ecological balance, publi…
Inorganic Catalyst Efficiency Prediction Based on EAPCR Model: A Deep Learning Solution for Multi-Source Heterogeneous Data
Zhangdi Liu, Ling An, Mengke Song +5
The design of inorganic catalysts and the prediction of their catalytic efficiency are fundamental challenges in chemistry and materials science. Traditional catalyst evaluation me…
EAPCR: A Universal Feature Extractor for Scientific Data without Explicit Feature Relation Patterns
Zhuohang Yu, Ling An, Yansong Li +6
Conventional methods, including Decision Tree (DT)-based methods, have been effective in scientific tasks, such as non-image medical diagnostics, system anomaly detection, and inor…
Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms
Zhen-Yu Zhang, Guo-Xiang Shao, Chun-Ming Qiu +3
Abnormalities of the sewage pipe network will affect the normal operation of the whole city. Therefore, it is important to detect the abnormalities early. This paper propose an ear…
Efficiently Solving High-Order and Nonlinear ODEs with Rational Fraction Polynomial: the Ratio Net
Chenxin Qin, Ruhao Liu, Maocai Li +3
Recent advances in solving ordinary differential equations (ODEs) with neural networks have been remarkable. Neural networks excel at serving as trial functions and approximating s…
Activation functions are not needed: the ratio net
Chi-Chun Zhou, Hai-Long Tu, Yue-Jie Hou +3
A deep neural network for classification tasks is essentially consist of two components: feature extractors and function approximators. They usually work as an integrated whole, ho…