scholarly journals A Development of Real Time Artificial Intelligence Warning System Linked Discharge and Water Quality (II) Construction of Warning System

2005 ◽  
Vol 38 (7) ◽  
pp. 575-584
Author(s):  
In-Sung Yeon ◽  
Sang-Jin Ahn
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Real Time ◽  
Warning System

Research and Application of Real-Time Remote Monitoring and Early Warning System of Source Water Quality

2013 ◽  
Vol 779-780 ◽  
pp. 1408-1413
Author(s):  
Shu Yuan Li ◽  
Jian Hua Tao ◽  
Lei Yu
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Early Warning ◽  
Remote Monitoring ◽  
Early Warning System ◽  
Warning System ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
The Real ◽  
Monitoring And Early Warning

Drinking water sources play an important role in assurance of life safety, normal production and social stability. In this paper, a real-time remote water quality monitoring and early warning system has been developed. The paper concentrates on the system architecture and key techniques of the real-time water quality monitoring and early warning. The implementation of the system by advanced water quality sensor techniques, wireless transmission, databases and water quality modeling is retraced in detail. It can be applied to the real-time remote monitoring of water quality and decision support for water pollution incidents.

scholarly journals On the Development of a Real Time Water Monitoring System

2021 ◽  
Vol 327 ◽  
pp. 02011
Author(s):  
Sandel Zaharia ◽  
Gabriel Iana ◽  
Cristian Monea ◽  
Mihnea Sandu
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Real Time ◽  
Web Application ◽  
Quality Of Data ◽  
Sewer System ◽  
Hardware Platform ◽  
Running Water ◽  
Software Application

The goal of the paper starts from the need for real-time monitoring of both running water and its affluents and urban sewerage systems with a role in discharging wastewater. The idea is to assess water quality and to determine the sources of pollutants resulting from human activity. The data quality will be obtained by purchasing them with a high resolution, both spatial and temporal, using multi-parametric sensors on a hardware platform of its own multisensory acquisition. The acquired data is stored in CLOUD or local server for storage, analysis and interpretation. There will be a software application based on artificial intelligence technologies that serves to identify and classify different polluted areas, locate pollution sources, predict their extinction, degree of pollution and help make decisions based on real-time detection. A web application will provide all the data collected in the field and it can be accessed on a common online platform. This allows researchers or employees of relevant agencies as well as city sewer system operators to validate the quality of data purchased from sensors and end users to be sure of their correctness.

Implementation of an innovative sensor technology for effective online water quality monitoring in the distribution network

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
B. R. de Graaf ◽  
F. Williamson ◽  
Marcel Klein Koerkamp ◽  
J. W. Verhoef ◽  
R. Wuestman ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Real Time ◽  
Low Cost ◽  
Distribution Network ◽  
Warning System ◽  
High Sensitivity ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Sensor Technology

For safe supply of drinking water, water quality needs to be monitored online in real time. The consequence of inadequate monitoring can result in substantial health risks, and economic and reputational damages. Therefore, Vitens N.V., the largest drinking water company of the Netherlands, set a goal to explore and invest in the development of intelligent water supply. In order to do this Vitens N.V. has set up a demonstration network for online water quality monitoring, the Vitens Innovation Playground (VIP). With the recent innovative developments in the field of online sensoring Vitens kicked off, in 2011, its first major online sensoring program by implementing a sensor grid based on EventLab systems from Optiqua Technologies Pte Ltd in the distribution network. EventLab utilizes bulk refractive index as a generic parameter for continuous real time monitoring of changes in water quality. Key characteristics of this innovative optical sensor technology, high sensitivity generic sensors at low cost, make it ideal for deployment as an early warning system. This paper describes different components of the system, the technological challenges that were overcome, and presents performance data and conclusions from deployment of Optiqua's EventLab systems in the VIP.

A Real-Time Mathematical Model for Three-Dimensional Tidal Flow and Water Quality

1991 ◽  
Vol 24 (6) ◽  
pp. 171-177 ◽  
Author(s):  
Zeng Fantang ◽  
Xu Zhencheng ◽  
Chen Xiancheng
Keyword(s):  
Mathematical Model ◽  
Water Quality ◽  
Real Time ◽  
Control Volume ◽  
Tidal Flow ◽  
Three Dimensional ◽  
River Estuary ◽  
Pearl River Estuary ◽  
Practical Application ◽  
The Pearl River

A real-time mathematical model for three-dimensional tidal flow and water quality is presented in this paper. A control-volume-based difference method and a “power interpolation distribution” advocated by Patankar (1984) have been employed, and a concept of “separating the top-layer water” has been developed to solve the movable boundary problem. The model is unconditionally stable and convergent. Practical application of the model is illustrated by an example for the Pearl River Estuary.

Safety Monitoring and Warning System for Subway Construction Workers using Wearable Technology

2019 ◽  
Vol 13 ◽  
Author(s):  
Jun-hua Chen ◽  
Da-hu Wang ◽  
Cun-yuan Sun
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Early Warning ◽  
Warning System ◽  
Safety Monitoring ◽  
Wearable Technology ◽  
Construction Workers ◽  
Great Success ◽  
Subway Construction ◽  
Monitoring And Early Warning

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

Flat Flexible Thin Milli-electrode Array for Real-time in situ Water Quality Monitoring in Distribution Systems

2017 ◽  
Vol 2017 (4) ◽  
pp. 5598-5617
Author(s):  
Zhiheng Xu ◽  
Wangchi Zhou ◽  
Qiuchen Dong ◽  
Yan Li ◽  
Dingyi Cai ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Distribution Systems ◽  
Electrode Array ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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