Bayesian methods for predicting LAI and soil water content

peer reviewedLAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: Extended Kalman Filtering (EKF), Particle Filtering (PF), and Variational Filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error (RMSE) of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation.Filtering method-based state and parameter estimation for crop model

Förhandlingar om kulturföremål. Parters intressen och argument i processer om återförande av kulturföremål

Disputes over demands for a return of cultural objects, in many cases museum objects, are well known. But such conflicts can also be seen as negotiations, which can be analyzed as well. This thesis adds a negotiation perspective and by a close scrutiny points out certain factors and arguments which can facilitate a process, cause a blocking, or rescind a blocking. By referring to such a process as a form of negotiation, this might bring about possibilities for the parties involved, which they otherwise would not been considering. It may occur that behind a party's arguments some interests could have been hidden consciously, or been surpassed by something else, which can cause a blocking. The aim of this thesis is to highlight the actors' different perspectives in negotiations concerning return of cultural objects, how they argue in a negotiation position and how the process can affect the management of cultural objects. The negotiation perspective can generate knowledge for increased understanding of motives behind the parties' positions. The specific traits of negotiation processes and what arguments and interests that may be important during the passage of events are examined in two case studies. One case is about the process of the return of medieval ecclesiastical objects from a museum context to two rural churches on Gotland, Sweden. The other study examines the process of negotiating the return of a totem pole from the Museum of Ethnography in Stockholm to the people of Haisla First Nation, Canada. The material that has been analyzed in this thesis shows in which phase in the process and why the parties changed their opinion, thus making a constructive solution possible. The thesis identifies aspects that the parties considered important in the negotiation process, and the outcome indicates how essential factors are valued in cases where the return of cultural objects are negotiated. Values and arguments, present in the case studies, are identified and categorized, which then are compiled into tables in order to make them comparable. These tables show in what period turning points took place in the process, and which aspects made parties change their respective standpoint, as the situation shifted from disagreement to consensus. For instance, groups of arguments that associates to the categories are: place, cultural identity, conservation and economy, are strong indicators of what some people find important. This thesis shows why and how the parties were convinced of the benefits of a solution grounded in consensus. By using a negotiation perspective the analysis identifies incentives that created a progressive process. The findings are useful for better understanding of future processes of returning cultural objects and benefit the development of the management of cultural heritage

A novel leak detection approach in water distribution networks

© 2021 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 worksThis paper proposes a novel leak monitoring framework aims to improve the operation of water distribution network (WDN). To do that, an online statistical hypothesis test based leak detection is proposed. The main advantages of the developed method are first to deal with the higher required computational time for detecting leaks and then, to update the KPCA model according to the dynamic change of the process. Thus, this can be performed to massive and online datasets. Simulation results obtained from simulated WDN data demonstrate the effectiveness of the proposed technique.Peer ReviewedPostprint (author's final draft

Process Monitoring Using Data-Based Fault Detection Techniques: Comparative Studies

Data based monitoring methods are often utilized to carry out fault detection (FD) when process models may not necessarily be available. The partial least square (PLS) and principle component analysis (PCA) are two basic types of multivariate FD methods, however, both of them can only be used to monitor linear processes. Among these extended data based methods, the kernel PCA (KPCA) and kernel PLS (KPLS) are the most well-known and widely adopted. KPCA and KPLS models have several advantages, since, they do not require nonlinear optimization, and only the solution of an eigenvalue problem is required. Also, they provide a better understanding of what kind of nonlinear features are extracted: the number of the principal components (PCs) in a feature space is fixed a priori by selecting the appropriate kernel function. Therefore, the objective of this work is to use KPCA and KPLS techniques to monitor nonlinear data. The improved FD performance of KPCA and KPLS is illustrated through two simulated examples, one using synthetic data and the other using simulated continuously stirred tank reactor (CSTR) data. The results demonstrate that both KPCA and KPLS methods are able to provide better detection compared to the linear versions

Online statistical hypothesis test for leak detection in water distribution networks

This paper aims at improving the operation of the water distribution networks (WDN) by developing a leak monitoring framework. To do that, an online statistical hypothesis test based on leak detection is proposed. The developed technique, the so-called exponentially weighted online reduced kernel generalized likelihood ratio test (EW-ORKGLRT), is addressed so that the modeling phase is performed using the reduced kernel principal component analysis (KPCA) model, which is capable of dealing with the higher computational cost. Then the computed model is fed to EW-ORKGLRT chart for leak detection purposes. The proposed approach extends the ORKGLRT method to the one that uses exponential weights for the residuals in the moving window. It might be able to further enhance leak detection performance by detecting small and moderate leaks. The developed method’s main advantages are first dealing with the higher required computational time for detecting leaks and then updating the KPCA model according to the dynamic change of the process. The developed method’s performance is evaluated and compared to the conventional techniques using simulated WDN data. The selected performance criteria are the excellent detection rate, false alarm rate, and CPU time.Peer ReviewedPostprint (author's final draft

Factors that May Influence the Performance of Wireless Sensor Networks

International audienc

Nonlinear State and Parameter Estimation Using Iterated Sigma Point Kalman Filter: Comparative Studies

In this chapter, iterated sigma‐point Kalman filter (ISPKF) methods are used for nonlinear state variable and model parameter estimation. Different conventional state estimation methods, namely the unscented Kalman filter (UKF), the central difference Kalman filter (CDKF), the square‐root unscented Kalman filter (SRUKF), the square‐root central difference Kalman filter (SRCDKF), the iterated unscented Kalman filter (IUKF), the iterated central difference Kalman filter (ICDKF), the iterated square‐root unscented Kalman filter (ISRUKF) and the iterated square‐root central difference Kalman filter (ISRCDKF) are evaluated through a simulation example with two comparative studies in terms of state accuracies, estimation errors and convergence. The state variables are estimated in the first comparative study, from noisy measurements with the several estimation methods. Then, in the next comparative study, both of states and parameters are estimated, and are compared by calculating the estimation root mean square error (RMSE) with the noise‐free data. The impacts of the practical challenges (measurement noise and number of estimated states/parameters) on the performances of the estimation techniques are investigated. The results of both comparative studies reveal that the ISRCDKF method provides better estimation accuracy than the IUKF, ICDKF and ISRUKF. Also the previous methods provide better accuracy than the UKF, CDKF, SRUKF and SRCDKF techniques. The ISRCDKF method provides accuracy over the other different estimation techniques; by iterating maximum a posteriori estimate around the updated state, it re‐linearizes the measurement equation instead of depending on the predicted state. The results also represent that estimating more parameters impacts the estimation accuracy as well as the convergence of the estimated parameters and states. The ISRCDKF provides improved state accuracies than the other techniques even with abrupt changes in estimated states

Characterization of greater middle eastern genetic variation for enhanced disease gene discovery

The Greater Middle East (GME) has been a central hub of human migration and population admixture. The tradition of consanguinity, variably practiced in the Persian Gulf region, North Africa, and Central Asia1-3, has resulted in an elevated burden of recessive disease4. Here we generated a whole-exome GME variome from 1,111 unrelated subjects. We detected substantial diversity and admixture in continental and subregional populations, corresponding to several ancient founder populations with little evidence of bottlenecks. Measured consanguinity rates were an order of magnitude above those in other sampled populations, and the GME population exhibited an increased burden of runs of homozygosity (ROHs) but showed no evidence for reduced burden of deleterious variation due to classically theorized ‘genetic purging’. Applying this database to unsolved recessive conditions in the GME population reduced the number of potential disease-causing variants by four- to sevenfold. These results show variegated genetic architecture in GME populations and support future human genetic discoveries in Mendelian and population genetics

Predicting biomass and grain protein content using Bayesian methods

This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback–Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times

Traitement du signal collaboratif dans les réseaux de capteurs sans fils

The primary focus of the thesis is to study the Bayesian inference problem in distributed Wireless Sensors Networks with particular emphasis on the trade-off between estimation precision and energy-awareness. We have proposed to use a distributed statistical signal processing in Wireless Sensors Networks with quantized measurements. In particular, this thesis addresses the application of variational methods for solving localization and tracking problems under energy and power constraints in Wireless Sensors Networks. Our work addresses three issues in wireless sensors networks: smart quantization scheme, cluster management and application of multi-objective optimization under energy constraint. The thesis contributions can be summarized as follows: - Target position estimation with quantized measurements based on variational methods - Channel estimation between the candidates sensors and the cluster head for target tracking in Wireless Sensor Network. - Adaptive optimized quantization under fixed and variable transmission power for target tracking in Wireless Sensor Network. - Best sensors selection that participate in data collection for target tracking in Wireless Sensor Network. - Secure data aggregation in Wireless Sensor Network. - Optimal communication path selection between sensors. - Multi-objective optimization method in Wireless Sensor Network. - Application of the multi-criteria data aggregation for crisis management based on multi-agents system in Wireless Sensor Network.L'objectif principal de la thèse est d'étudier le problème d'inférence bayésienne dans les réseaux de capteurs distribués avec un accent particulier sur le compromis entre la précision de l'estimation et la consommation de l'énergie. Nous avons proposé des algorithmes de traitement distribué du signal avec des mesures de capteurs quantifiées. En particulier, cette thèse porte sur l'application des méthodes variationnelles pour résoudre les problèmes de suivi de cibles sous les contraintes d'énergie dans les RCSFs. Le travail a abouti à la résolution de trois problèmes en RCSFs: la quantification intelligente des données des capteurs, la gestion des clusters et l'application de l'optimisation multi-objectifs pour s'accommoder des contraintes énergétiques d'un réseau de capteurs. Les contributions de cette thèse concernent les points suivant: -Estimation des positions de cibles basée sur des mesures quantifiées utilisant des méthodes variationnelles. -Estimation de canal entre les capteurs candidats et le chef de cluster. -Un régime de quantification adaptative sous contraintes de puissance de transmission constante et variable. -Sélection de meilleurs capteurs qui peuvent participer à la collecte de données. -Agrégation sécurisée de données dans le RCSF. -Sélection de chemins de communication optimaux entre les capteurs. -Méthode d'optimisation multi-objectifs dans le RCSF. -Application de la méthode d'agrégation multicritères des données basée sur le systéme multi-agents pour la gestion de crise dans le RCSF