1,619 research outputs found
Human-in-the-Loop Model Predictive Control of an Irrigation Canal
Until now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including industrial applications [1]-[3].Junta de AndalucĂa P11-TEP-812
On the modeling and real-time control of urban drainage systems: A survey
Trabajo presentado a la 11th International Conference on Hydroinformatics celebrada en New York (US) del 17 al 21 de agosto de 2014.Drainage networks are complex systems composed by several processes including recollection, transport, storing, treatment, and releasing the water to a receiving environment. The way Urban Drainage Systems (UDS) manage wastewater is through the convenient handling of active elements such as gates (redirection and/or retention), storing tanks, and pumping stations, when needed. Therefore, modeling and control of UDS basically consists in knowing and representing the (dynamical) behavior of these elements and managing them properly in order to achieve a given set of control objectives, such as minimization of flooding in streets or maximization of treated wastewater in the system. Given the large number of elements composing an UDS and the interaction between them, management and control strategies may depend on highly complex system models, which implies the explicit difficulty for designing real-time control (RTC) strategies. This paper makes a review of the models used to describe, simulate, and control UDS, proposes a revision of the techniques and strategies commonly used for the control UDS, and finally compares several control strategies based on a case study.This work has been partially supported by project N°548-2012 “Drenaje Urbano y Cambio Climático: Hacia los Sistemas de Alcantarillado del Futuro.”, Mexichem
Colombia S.A, the scholarships of Colciencias N°567-2012 and 647-2013, and the EU Project EFFINET (FP7-ICT-2011-8-31855) and the DGR of Generalitat de Catalunya (SAC group Ref. 2009/SGR/1491).Peer Reviewe
Modeling and real-time control of urban drainage systems: A review
Urban drainage systems (UDS) may be considered large-scale systems given their large number of associated states and decision actions, making challenging their real-time control (RTC) design. Moreover, the complexity of the dynamics of the UDS makes necessary the development of strategies for the control design. This paper reviews and discusses several techniques and strategies commonly used for the control of UDS. Moreover, the models to describe, simulate, and control the transport of wastewater in UDS are also reviewed.This work has been partially supported by Mexichem, Colombia through the project “Drenaje Urbano y Cambio Climático: Hacia los Sistemas de Alcantarillado del Futuro.” Fase II, with reference No. 548-2012, the scholarships of Colciencias No. 567-2012 and 647-2013, and the project ECOCIS (Ref. DPI2013-48243-C2-1-R).Peer Reviewe
On The Modeling And Real-Time Control Of Urban Drainage Systems: A Survey
Drainage network are complex systems composed by several processes including recollection, transport, storing, wastewater and/or rain treatment, and return of the water to a receiving environment. Urban drainage systems (UDS) involve most of these processes inside cities and can be either separate or combined systems, depending on how wastewater and rainwater are managed. The way UDS manage the wastewater is through the convenient handling of active elements such as gates (redirection and/or retention), storing tanks and pumping stations, when needed. Therefore, the modeling and control of UDS basically consists in knowing and representing the (dynamical) behavior of those elements and manage them properly in order to achieve a given set of control objectives, such as minimization of flooding in streets or maximization of treated wastewater in the system. Given the large number of elements composing a UDS and the interaction between them, management and control strategies may depend on highly complex system models, what implies the explicit difficulty for designing real-time control strategies. This paper makes a review on the huge world of models used to describe, simulate, and control UDS. Moreover, a revision of the techniques and strategies commonly used for the control of these systems is also presented and discussed. Mechanisms that ensure the correct operation of the UDS under presence of failures or communication flaws in the system are considered as well
On the modeling and real-time control of urban drainage systems: A survey
Drainage networks are complex systems composed by several processes including recollection, transport, storing, treatment, and releasing the water to a receiving environment. The way Urban Drainage Systems (UDS) manage wastewater is through the convenient handling of active elements such as gates (redirection and/or retention), storing tanks, and pumping stations, when needed. Therefore, modeling and control of UDS basically consists in knowing and representing the (dynamical) behavior of these elements and managing them properly in order to achieve a given set of control objectives, such as minimization of flooding in streets or maximization of treated wastewater in the system. Given the large number of elements composing an UDS and the interaction between them, management and control strategies may depend on highly complex system models, which implies the explicit difficulty for designing real-time control (RTC) strategies. This paper makes a review of the models used to describe, simulate, and control UDS, proposes a revision of the techniques and strategies commonly used for the control UDS, and finally compares several control strategies based on a case study.Peer ReviewedPostprint (author’s final draft
Smart Urban Water Networks
This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems
Secured Framework for Smart Farming in Hydroponics with Intelligent and Precise Management based on IoT with Blockchain Technology
Hydroponics is a type of soil-free farming that uses less water and other resources than conventional soil-based farming methods. However, due to the simultaneous supervision of multiple factors, nutrition advice, and plant diagnosis system, monitoring hydroponics farming is a difficult task. Hydroponic techniques utilizing the IoT show to deliver the finest outcomes, despite the usage of various artificial culture methods. Though, the usage of smart communication technologies and IoT exposes environments for smart farming to a wide range of cybersecurity risks and weaknesses. However, the adoption of intelligence-based controlling algorithms in the agricultural industry is a good use of current technical advancements to address these issues. This paper presented a secured framework for smart farming in hydroponics system. The proposed architecture is characterized into four-layer IoT based framework, sensor, communication, fog and cloud layer. Data analytics is performed using supervised machine learning techniques with intelligent and precise management and is applied at the fog layer for efficient computation over the cloud layer. The data security over channel is protected by using Blockchain Technology. The experimental results are evaluated and analyzed for several statistical parameters in order to improve the system efficacy
Hierarchical and Decentralised Federated Learning
Federated learning has shown enormous promise as a way of training ML models
in distributed environments while reducing communication costs and protecting
data privacy. However, the rise of complex cyber-physical systems, such as the
Internet-of-Things, presents new challenges that are not met with traditional
FL methods. Hierarchical Federated Learning extends the traditional FL process
to enable more efficient model aggregation based on application needs or
characteristics of the deployment environment (e.g., resource capabilities
and/or network connectivity). It illustrates the benefits of balancing
processing across the cloud-edge continuum. Hierarchical Federated Learning is
likely to be a key enabler for a wide range of applications, such as smart
farming and smart energy management, as it can improve performance and reduce
costs, whilst also enabling FL workflows to be deployed in environments that
are not well-suited to traditional FL. Model aggregation algorithms, software
frameworks, and infrastructures will need to be designed and implemented to
make such solutions accessible to researchers and engineers across a growing
set of domains.
H-FL also introduces a number of new challenges. For instance, there are
implicit infrastructural challenges. There is also a trade-off between having
generalised models and personalised models. If there exist geographical
patterns for data (e.g., soil conditions in a smart farm likely are related to
the geography of the region itself), then it is crucial that models used
locally can consider their own locality in addition to a globally-learned
model. H-FL will be crucial to future FL solutions as it can aggregate and
distribute models at multiple levels to optimally serve the trade-off between
locality dependence and global anomaly robustness.Comment: 11 pages, 6 figures, 25 reference
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