6 papers
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…
Unsupervised Waste Classification By Dual-Encoder Contrastive Learning and Multi-Clustering Voting (DECMCV)
Kui Huang, Mengke Song, Shuo Ba +6
Waste classification is crucial for improving processing efficiency and reducing environmental pollution. Supervised deep learning methods are commonly used for automated waste cla…
Renaissance of Literate Programming in the Era of LLMs: Enhancing LLM-Based Code Generation in Large-Scale Projects
Wuyang Zhang, Yansong Li, Zeyu Dong +6
Large Language Models (LLMs) have helped programmers increase efficiency through code generation, comprehension, and repair. However, their application to large-scale projects rema…
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…