Modelling uncertainty for leak localization in Water Networks

The performance and success of model-based leak localization methods applied to water distribution networks (WDN) highly depends on the uncertainty of the system considered. This work proposes an original method of modeling the effect of uncertainties in these networks. The proposed method is based on the collection of real data in the water network in the absence of leaks. The discrepancy (residual) between the measured data and the one provided by a simulator of the network in normal operation is used to extrapolate the possible residuals in the different leak scenarios. In addition, indicators for assessing the effect of uncertainty in the performance of leak localization methods based on residual correlation analysis are provided. The error in terms of correlation intervals and leak localzation assessment between the proposed approximation and the real one is studied by means a simplified model of the WDN of Hanoi (Vietnam).Postprint (published version

Comparison of demand pattern calibration in water distribution networks with geographic and non-geographic parameterization

Demands are one of the most uncertain parameters in water distribution network models. A good calibration of the model demands leads to better results when using the model for any purpose. A demand pattern calibration methodology that uses a priori information has been developed for calibrating the behavior of demand groups. In cities, similar demand behaviors are distributed all over the network, contrary to smaller villages where demands are clearly sectorised in residential neighborhoods, commercial zones and industrial sectors. In this work, demand pattern calibration has a final use for leakage detection and isolation. Detecting a leakage in a pattern that covers nodes spread all over the network makes the isolation unfeasible. Besides, demands in the same zone may be more similar due to the common pressure of the area rather than for the type of contract. A demand pattern calibration methodology is applied to a real network with synthetic non-geographic demands for calibrating geographic demand patterns. The results are compared with a previous work where the calibrated patterns were the original non-geographic onesPeer ReviewedPostprint (published version

Modelling uncertainty for leak localization in Water Networks

The performance and success of model-based leak localization methods applied to water distribution networks (WDN) highly depends on the uncertainty of the system considered. This work proposes an original method of modeling the effect of uncertainties in these networks. The proposed method is based on the collection of real data in the water network in the absence of leaks. The discrepancy (residual) between the measured data and the one provided by a simulator of the network in normal operation is used to extrapolate the possible residuals in the different leak scenarios. In addition, indicators for assessing the effect of uncertainty in the performance of leak localization methods based on residual correlation analysis are provided. The error in terms of correlation intervals and leak localzation assessment between the proposed approximation and the real one is studied by means a simplified model of the WDN of Hanoi (Vietnam).Peer ReviewedPostprint (published version

Real-Time Non-Intrusive Assessment of Viewing Distance During Computer Use

Purpose: To develop and test the sensitivity of an ultrasound-based sensor to assess the viewing distance of visual display terminals operators in real-time conditions. Methods: A modified ultrasound sensor was attached to a computer display to assess viewing distance in real time. Sensor functionality was tested on a sample of 20 healthy participants while they conducted four 10-minute randomly presented typical computer tasks (a match-three puzzle game, a video documentary, a task requiring participants to complete a series of sentences, and a predefined internet search). Results: The ultrasound sensor offered good measurement repeatability. Game, text completion, and web search tasks were conducted at shorter viewing distances (54.4 cm [95% CI 51.3-57.5 cm], 54.5 cm [95% CI 51.1-58.0 cm], and 54.5 cm [95% CI 51.4-57.7 cm], respectively) than the video task (62.3 cm [95% CI 58.9-65.7 cm]). Statistically significant differences were found between the video task and the other three tasks (all p < 0.05). Range of viewing distances (from 22 to 27 cm) was similar for all tasks (F = 0.996; p = 0.413). Conclusions: Real-time assessment of the viewing distance of computer users with a non-intrusive ultrasonic device disclosed a task-dependent pattern. (C) 2016 American Academy of OptometryPostprint (author's final draft

Disinfection by products estimation in a water distribution network

Even though disinfection is necessary to ensure water safety for human consumption, some disinfectants produce disinfection by-products (DBPs) that may be dangerous for human health. Current European legislation obligates water distributors to limit some DBPs concentration to final consumers. Then, water companies must control these compounds and are obligated to periodically monitor their network. DBPs modeling can be very useful for estimating online DBPs concentration throughout the network, increasing DBPs control and knowledge, but avoiding DBPs analytics time and resources consumption [1]. Trihalomethanes (THM), the first DBPs discovered, have long been the most studied and modeled. Previous studies have mostly used linear relations between variables and THM concentration, but also computational modelling, mechanistic and data driven models [2, 3]. Even though, there are still challenges to beat: most studies use a small database and laboratory-scale for model building, forgetting the impact of network pipelines and season. In addition, significant variables for DBPs’ formation such as retention time are most of the time neglected due to its difficulty to measure. Finally, THMs are not the only DBPs generated from disinfection or even the most toxic: other DBPs must be studied, and their formation pathways along the network investigated. In this study, data from a full-scale distribution network was used: online sensors and sampling campaigns. To include hydraulic conditions as retention time, EPANET software and R programming are used to simulate the network. Different models, mechanistic and data driven, have been used to estimate the chlorine decay and DBP formation within the network. Results of the calibration and validation of these models and the conclusions obtained are presented.Peer ReviewedPostprint (published version

Adaptive nonlinear guidance law using neural networks applied to a quadrotor

© 2019IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The NonLinear Guidance Law (NLGL) is a geometric algorithm commonly employed to solve the path following problem on different unmanned vehicles. NLGL is simple (does no depend on the model of the vehicle), effective and has only one tunning parameter. Its control parameter (L) depends on various factors, such as the velocity of the vehicle, the shape of the reference path and the dynamics of the vehicle. This paper analyses the effect of parameter L on the performance of NLGL when it is applied to a quadrotor vehicle. An Adaptive NLGL, which includes a velocity reduction term, is proposed. Stability proofs are given. Simulation results show that the proposed algorithm enhances the performance of the standard NLGL. Furthermore, it has no parameters to tune.Peer ReviewedPostprint (author's final draft

A deep reinforcement learning approach for path following on a quadrotor

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper proposes the Deep Deterministic Policy Grandient (DDPG) reinforcement learning algorithm to solve the path following problem in a quadrotor vehicle. This agent is implemented using a separated control and guidance structure with an autopilot tracking the attitude and velocity commands. The DDPG agent is implemented in python and it is trained and tested in the RotorS-Gazebo environment, a realistic multirotor simulator integrated in ROS. Performance is compared with Adaptive NLGL, a geometric algorithm that implements an equivalent control structure. Results show how the DDPG agent is able to outperform the Adaptive NLGL approach while reducing its complexity.This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERDF) through the SCAV project (ref. MINECO DPI2017-88403-R), and by SMART project (ref. EFA 153/16 Interreg Cooperation Program POCTEFA 2014- 2020). Bartomeu Rubí is also supported by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya, the European Social Fund (ESF) and AGAUR under a FI grant (ref. 2017FI B 00212).Peer ReviewedPostprint (author's final draft

A Survey of path following control strategies for UAVs focused on quadrotors

The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.Peer ReviewedPostprint (author's final draft

Quadrotor path following and reactive obstacle avoidance with deep reinforcement learning

A deep reinforcement learning approach for solving the quadrotor path following and obstacle avoidance problem is proposed in this paper. The problem is solved with two agents: one for the path following task and another one for the obstacle avoidance task. A novel structure is proposed, where the action computed by the obstacle avoidance agent becomes the state of the path following agent. Compared to traditional deep reinforcement learning approaches, the proposed method allows to interpret the training process outcomes, is faster and can be safely trained on the real quadrotor. Both agents implement the Deep Deterministic Policy Gradient algorithm. The path following agent was developed in a previous work. The obstacle avoidance agent uses the information provided by a low-cost LIDAR to detect obstacles around the vehicle. Since LIDAR has a narrow field-of-view, an approach for providing the agent with a memory of the previously seen obstacles is developed. A detailed description of the process of defining the state vector, the reward function and the action of this agent is given. The agents are programmed in python/tensorflow and are trained and tested in the RotorS/gazebo platform. Simulations results prove the validity of the proposed approach.This work has been partially funded by the Spanish Government (MINECO) through the project CICYT (ref. DPI2017-88403-R).Peer ReviewedPostprint (published version

Uncertainty effect on leak localisation in a DMA

The leak localisation methodologies based on data and models are affected by both uncertainties in the model and in the measurements. This uncertainty should be quantified so that its effect on the localisation methods performance can be estimated. In this paper, a model-based leak localisation methodology is applied to a real District Metered Area using synthetic data. In the generation process of the data, uncertainty in demands is taken into account. This uncertainty was estimated so that it can justify the uncertainty observed in the real measurements. The leak localisation methodology consists, first, in generating the set of possible measurements, obtained by Monte Carlo Simulation under a certain leak assumption and considering uncertainty, and second, in falsifying sets of nodes using the correlation with a leak residual model in order to signal a set of possible leaky nodes. The assessment is done by means of generating the confusion matrix with a Monte Carlo approach.Peer ReviewedPostprint (author's final draft