51,594 research outputs found
On an application of extended kalman filtering to activated sludge processes: a benchmark study
The growing demand for performance improvements of urban wastewater system operation coupled with the lack of instrumentation in most wastewater treatment plants motivates the need for non-linear observers to be used as virtual sensors for estimation and control of effluent quality. This paper is focused on the development of a general procedure for on-line monitoring of activated sludge processes, using an extended Kalman filter (EKF) approach. The Activated Sludge Model no.1 (ASM1) is selected to describe the biological processes in the reactor. On-line measurements are corrupted by additive white noise and unknown inputs are modelled using fast Fourier transform (FFT) and spectrum analyses. The given procedure aims at reducing the original ASM1 model to an observable and identifiable model, which can be used for joint non-linear state and parameter estimations. Simulation results are presented to demonstrate the effectiveness of the proposed methods and show that on-line monitoring of SND and XND concentrations is achieved when dynamic input data are used tocharacterize the influent wastewater for the model
On Norm-Based Estimations for Domains of Attraction in Nonlinear Time-Delay Systems
For nonlinear time-delay systems, domains of attraction are rarely studied
despite their importance for technological applications. The present paper
provides methodological hints for the determination of an upper bound on the
radius of attraction by numerical means. Thereby, the respective Banach space
for initial functions has to be selected and primary initial functions have to
be chosen. The latter are used in time-forward simulations to determine a first
upper bound on the radius of attraction. Thereafter, this upper bound is
refined by secondary initial functions, which result a posteriori from the
preceding simulations. Additionally, a bifurcation analysis should be
undertaken. This analysis results in a possible improvement of the previous
estimation. An example of a time-delayed swing equation demonstrates the
various aspects.Comment: 33 pages, 8 figures, "This is a pre-print of an article published in
'Nonlinear Dynamics'. The final authenticated version is available online at
https://doi.org/10.1007/s11071-020-05620-8
COSMOGRAIL: the COSmological MOnitoring of GRAvItational Lenses XV. Assessing the achievability and precision of time-delay measurements
COSMOGRAIL is a long-term photometric monitoring of gravitationally lensed
QSOs aimed at implementing Refsdal's time-delay method to measure cosmological
parameters, in particular H0. Given long and well sampled light curves of
strongly lensed QSOs, time-delay measurements require numerical techniques
whose quality must be assessed. To this end, and also in view of future
monitoring programs or surveys such as the LSST, a blind signal processing
competition named Time Delay Challenge 1 (TDC1) was held in 2014. The aim of
the present paper, which is based on the simulated light curves from the TDC1,
is double. First, we test the performance of the time-delay measurement
techniques currently used in COSMOGRAIL. Second, we analyse the quantity and
quality of the harvest of time delays obtained from the TDC1 simulations. To
achieve these goals, we first discover time delays through a careful inspection
of the light curves via a dedicated visual interface. Our measurement
algorithms can then be applied to the data in an automated way. We show that
our techniques have no significant biases, and yield adequate uncertainty
estimates resulting in reduced chi2 values between 0.5 and 1.0. We provide
estimates for the number and precision of time-delay measurements that can be
expected from future time-delay monitoring campaigns as a function of the
photometric signal-to-noise ratio and of the true time delay. We make our blind
measurements on the TDC1 data publicly availableComment: 11 pages, 8 figures, published in Astronomy & Astrophysic
Fault detection, identification and accommodation techniques for unmanned airborne vehicles
Unmanned Airborne Vehicles (UAV) are assuming prominent roles in both the commercial and military aerospace industries. The promise of reduced costs and reduced risk to human life is one of their major attractions, however these low-cost systems are yet to gain acceptance as a safe alternate to manned solutions. The absence of a thinking, observing, reacting and decision making pilot reduces the UAVs capability of managing adverse situations such as faults and failures. This paper presents a review of techniques that can be used to track the system health onboard a UAV. The review is based on a year long literature review aimed at identifying approaches suitable for combating the low reliability and high attrition rates of today’s UAV. This research primarily focuses on real-time, onboard implementations for generating accurate estimations of aircraft health for fault accommodation and mission management (change of mission objectives due to deterioration in aircraft health). The major task of such systems is the process of detection, identification and accommodation of faults and failures (FDIA). A number of approaches exist, of which model-based techniques show particular promise. Model-based approaches use analytical redundancy to generate residuals for the aircraft parameters that can be used to indicate the occurrence of a fault or failure. Actions such as switching between redundant components or modifying control laws can then be taken to accommodate the fault. The paper further describes recent work in evaluating neural-network approaches to sensor failure detection and identification (SFDI). The results of simulations with a variety of sensor failures, based on a Matlab non-linear aircraft model are presented and discussed. Suggestions for improvements are made based on the limitations of this neural network approach with the aim of including a broader range of failures, while still maintaining an accurate model in the presence of these failures
Oscillatory regulation of Hes1: discrete stochastic delay modelling and simulation
Discrete stochastic simulations are a powerful tool for understanding the dynamics of chemical kinetics when there are small-to-moderate numbers of certain molecular species. In this paper we introduce delays into the stochastic simulation algorithm, thus mimicking delays associated with transcription and translation. We then show that this process may well explain more faithfully than continuous deterministic models the observed sustained oscillations in expression levels of hes1 mRNA and Hes1 protein
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