Q
Qiong Wei
Researcher at China Agricultural University
Publications - 8
Citations - 178
Qiong Wei is an academic researcher from China Agricultural University. The author has contributed to research in topics: Fault (power engineering) & Water environment. The author has an hindex of 3, co-authored 7 publications receiving 77 citations.
Papers
More filters
Journal ArticleDOI
Review of Dissolved Oxygen Detection Technology: From Laboratory Analysis to Online Intelligent Detection.
TL;DR: The application of intelligent technology in intelligent signal transfer processing, digital signal processing, and the real-time dynamic adaptive compensation and correction of dissolved oxygen sensors is studied.
Journal ArticleDOI
Equipment and Intelligent Control System in Aquaponics: A Review
TL;DR: Hydroponics as the main vegetable cultivation method in aquaponics and the main equipment of water treatment in recirculating aquaculture are introduced, and the traditional equipment and its development prospects are analyzed.
Journal ArticleDOI
Intelligent monitoring and control technologies of open sea cage culture: A review
Yaoguang Wei,Qiong Wei,Dong An +2 more
TL;DR: It is demonstrated that cage farming is the trend of aquaculture and in the future, applications will combine information monitoring technology, intelligent control technology, and intelligent equipment technology to realize intelligent, digital, automatic and unmanned operation of cage Aquaculture.
Journal ArticleDOI
LSTM-TCN: dissolved oxygen prediction in aquaculture, based on combined model of long short-term memory network and temporal convolutional network
TL;DR: The combined prediction method LSTM-TCN (long short-term memory network and temporal convolutional network) has high accuracy in dissolved oxygen prediction, and can capture better characteristics of historical data with increasing time window of the historical dissolved oxygen sequence.
Posted ContentDOI
An Improved CNN-LSTM Network Based On Hierarchical Attention Mechanism For Motor Bearing Fault Diagnosis
TL;DR: This paper presents an improved CNN-LSTM network based on hierarchical attention mechanism (CALSTM) for motor bearing fault diagnosis that does not need signal processing and adaptively weights the features of each sample learned by the neural network, which enhances the explanatory ability of the learning process of the Neural network.