Water quality real-time monitoring system via biological detection based on video analysis

2017 ◽  
Author(s):  
Chen Xin ◽  
Yuan Fei
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Monitoring System ◽  
Video Analysis ◽  
Real Time Monitoring ◽  
Biological Detection

Developing the Wastewater Monitoring System Based on Ontology

2012 ◽  
Vol 452-453 ◽  
pp. 1301-1306
Author(s):  
Jian Jun Yi ◽  
Jian Gang Fan ◽  
Hui Jiang ◽  
Ying Cheng ◽  
Shao Li Chen
Keyword(s):  
Water Quality ◽  
Remote Control ◽  
Dissolved Oxygen ◽  
Real Time ◽  
Monitoring System ◽  
Data Transmission ◽  
Real Time Monitoring ◽  
Water Conductivity ◽  
Original Water ◽  
Control Water

Real time Monitoring system is an important gurantee for the water quality. SDI-12 water sensors are used to monitor the water conductivity, temperature, dissolved oxygen, Ph, turbidity and other parameters in this paper. The signal of the wastewater is collected to the ARM chip, and then it is processed by the chip. The preliminary result will be displayed on a handheld terminal in real time. The handheld terminal also integrates the function of remote data transmission, which can transmit the original water data to a central server for storage via GPRS. Central server analyse the wastewater data based on ontology. Meanwhile, the system can also control water detector via remote control, which realizes the unattended effect.

River Water Quality Robot Embedded with Real-Time Monitoring System: Design and Implementation

2021 ◽  
Author(s):  
Mohd Amirul Aizad M. Shahrani ◽  
Safaa Najah Saud Al-Humairi ◽  
Nurul Shahira Mohammad Puad ◽  
Muhammad Asyraf Zulkipli
Keyword(s):  
Water Quality ◽  
System Design ◽  
Real Time ◽  
River Water ◽  
Monitoring System ◽  
River Water Quality ◽  
Real Time Monitoring ◽  
Design And Implementation

INVESTIGATION OF HAND KINEMATICS TOWARDS FRONT CRAWL SWIMMING USING NON-INVASIVE REAL-TIME MONITORING SYSTEM

2014 ◽  
Author(s):  
Rozaimi Ghazali ◽  
◽  
Asiah Mohd Pilus ◽  
Wan Mohd Bukhari Wan Daud ◽  
Mohd Juzaila Abd Latif ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Real Time Monitoring ◽  
Front Crawl ◽  
Non Invasive ◽  
Hand Kinematics

Study on Slope Slide Real-Time Monitoring System in Guangdong Using GPS/CORS Integration Technique

2011 ◽  
Vol 13 (4) ◽  
pp. 562-570 ◽  
Author(s):  
Pingxin WEI ◽  
Chenggang LI ◽  
Feng XU ◽  
Xiaochun SHI
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Integration Technique ◽  
Real Time Monitoring

Selection and Evaluation of a Real Time Monitoring System for the Bigeye Bomb Fill/Close Production Facility. Phase 2

1989 ◽  
Author(s):  
Foy E. Ferguson ◽  
Michael W. Ellzy ◽  
Joseph W. Lovrich ◽  
Theodore L. Strozyk ◽  
Jr
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Phase 2 ◽  
Production Facility ◽  
Real Time Monitoring

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

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