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.

Advanced real time control of a combined sewer system

1998 ◽  
Vol 37 (1) ◽  
pp. 319-326 ◽  
Author(s):  
Andreas Petruck ◽  
Andreas Cassar ◽  
Joachim Dettmar
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Real Time Control ◽  
Sewer System ◽  
Time Control ◽  
Combined Sewer ◽  
Combined Sewer System ◽  
Complex Monitoring ◽  
Set Up

The research project ‘Real Time Control of a Combined Sewer System by Radar estimates of Precipitation’ seeks to improve the water quality of a stream by reducing quantitative and qualitative discharges of combined sewage overflow (CSO). The complex monitoring and simulation system that has been set up for control purposes is described. Two advanced real time control (ARTC) strategies have been developed. First a pollution based real time control (PBRTC) strategy, and second a water quality based real time control (WQBRTC) strategy. The PBRTC strategy is already implemented, the WQBRTC strategy will be implemented during the course of the project. For the PBRTC an off-line analysis is presented to show the effectiveness of the strategy.

Sewers and the Quality of Canals and Ditches in Amsterdam

1993 ◽  
Vol 27 (5-6) ◽  
pp. 61-67 ◽  
Author(s):  
E. Jacobs ◽  
J. W. van Sluis
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Sediment Quality ◽  
Water System ◽  
Water Systems ◽  
Sewer System ◽  
Water And Sediment ◽  
Combined Sewer ◽  
Water And Sediment Quality

The surface water system of Amsterdam is very complicated. Of two characteristic types of water systems the influences on water and sediment quality are investigated. The importance of the sewer output to the total loads is different for both water systems. In a polder the load from the sewers is much more important than in the canal basin. Measures to reduce the emission from the sewers are much more effective in a polder. The effect of these measures on sediment quality is more than the effect on water quality. Some differences between a combined sewer system and a separate sewer system can be found in sediment quality.

scholarly journals QoS Management for Embedded Databases in Multicore-Based Embedded Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Woochul Kang ◽  
Jaeyong Chung
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Control Method ◽  
Network Connectivity ◽  
Time Data ◽  
Quality Of Data ◽  
Multiple Control ◽  
Data Intensive ◽  
Real Time Data

With ubiquitous deployment of sensors and network connectivity, amounts of real-time data for embedded systems are increasing rapidly and database capability is required for many embedded systems for systematic management of real-time data. In such embedded systems, supporting the timeliness of tasks accessing databases is an important problem. However, recent multicore-based embedded architectures pose a significant challenge for such data-intensive real-time tasks since the response time of accessing data can be significantly affected by potential intercore interferences. In this paper, we propose a novel feedback control scheme that supports the timeliness of data-intensive tasks against unpredictable intercore interferences. In particular, we use multiple inputs/multiple outputs (MIMO) control method that exploits multiple control knobs, for example, CPU frequency and the Quality-of-Data (QoD) to handle highly unpredictable workloads in multicore systems. Experimental results, using actual implementation, show that the proposed approach achieves the target Quality-of-Service (QoS) goals, such as task timeliness and Quality-of-Data (QoD) while consuming less energy compared to baseline approaches.

Enhancing data-driven elderly appointment services in domestic care communities under COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
H.Y. Lam ◽  
G.T.S. Ho ◽  
Daniel Y. Mo ◽  
Valerie Tang
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Care Service ◽  
The Elderly ◽  
Care Centre ◽  
Service Matching ◽  
Content Type ◽  
Care Services ◽  
Domestic Care

PurposeUnder the impact of Coronavirus disease 2019 (COVID-19), this paper contributes in the deployment of the Artificial Intelligence of Things (AIoT)-based system, namely AIoT-based Domestic Care Service Matching System (AIDCS), to the existing electronic health (eHealth) system so as to enhance the delivery of elderly-oriented domestic care services.Design/methodology/approachThe proposed AIDCS integrates IoT and Artificial Intelligence (AI) technologies to (1) capture real-time health data of the elderly at home and (2) provide the knowledge support for decision making in the domestic care appointment service in the community.FindingsA case study was conducted in a local domestic care centre which provided elderly oriented healthcare services to the elderly. By integrating IoT and AI into the service matching process of the mobile apps platform provided by the local domestic care centre, the results proved that customer satisfaction and the quality of the service delivery were improved by observing the key performance indicators of the transactions after the implementation of the AIDCS.Originality/valueFollowing the outbreak of COVID-19, this is a new attempt to overcome the limited research done on the integration of IoT and AI techniques in the domestic care service. This study not only inherits the ability of the existing eHealth system to automatically capture and monitor the health status of the elderly in real-time but also improves the overall quality of domestic care services in term of responsiveness, effectiveness and efficiency.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

scholarly journals Rapid Assessment of Water Quality in Teluk Lake by Using Macroinvertebrates (A Family Level Biotic Index)

2018 ◽  
Vol 68 ◽  
pp. 04006
Author(s):  
Siswanta Kaban ◽  
M. Edi Armanto ◽  
M. Rasyid Ridho ◽  
Poedji L. Hariani
Keyword(s):  
Water Quality ◽  
Anthropogenic Activities ◽  
Rapid Assessment ◽  
Working Party ◽  
Biotic Index ◽  
Average Score ◽  
Running Water ◽  
Three Stages ◽  
Polluted Waters

In this study the water quality of Teluk Lake, Jambi Province by biological indices using macroobenthic during December 2017 to March 2018 were evaluated. Macro obenthic have been the most commonly indicator of running water quality to rapidly detect water quality changes. The methodology consisted on three stages: 1) macro invertebrates sampling and and sorting of the organisms in laboratory, 2) taxonomic identification of organisms and 3) determination and scoring with the Biological Monitoring Working Party -Average Score per Taxon (BMWP-ASPT) index. Result of water quality of Teluk Lake based on the diversity of macroinvertebrates was degraded with BMWP-ASPT index below 4. Based on the BMWP-ASPT score of the macroinvertebrates, both of sampling 1 and 2 were polluted with the category of medium and heavily polluted. In general, the decreasing of water quality of Teluk Lake was due to anthropogenic activities, so that the intolerant macro invertebrates could not survive in polluted waters while the tolerant ones can adapt well to the polluted aquatic environment.

scholarly journals Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Prasad M. Pujar ◽  
Harish H. Kenchannavar ◽  
Raviraj M. Kulkarni ◽  
Umakant P. Kulkarni
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Real Time ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Quality Analysis ◽  
Water Quality Analysis

AbstractIn this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

scholarly journals Mapeamento de Problemas de Qualidade no Linked Data

2015 ◽  
Vol 1 (1) ◽  
pp. 38
Author(s):  
Jessica Oliveira De Souza ◽  
Jose Eduardo Santarem Segundo
Keyword(s):  
Semantic Web ◽  
User Experience ◽  
Web Application ◽  
Linked Data ◽  
Data Sets ◽  
Quality Of Data ◽  
Related Quality ◽  
Primary Means ◽  
Basic Semantic

Since the Semantic Web was created in order to improve the current web user experience, the Linked Data is the primary means in which semantic web application is theoretically full, respecting appropriate criteria and requirements. Therefore, the quality of data and information stored on the linked data sets is essential to meet the basic semantic web objectives. Hence, this article aims to describe and present specific dimensions and their related quality issues.

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