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An integrated approach is presented for the on-line control and monitoring of chemical and biochemical processes. Predictive control and nonlinear estimation techniques are employed to achieve the required control performances while providing on-line estimates of the process key parameters. The features of the proposed approach are illustrated in simulation through two examples dealing with biological...
An audio signal can be represented by a Time-Varying AutoRegressive (TVAR) model, whose parameters can be estimated by a particle filter. Since the original parameters are unavailable for real signals, an evaluation of the estimation may be traditionally performed through indirect criteria such as the SNR of the signal denoised by a Kalman filter based on the TVAR estimated model or through a statistical...
The paper deals with the problem of reconstruction of nonlinearities in a certain class of nonlinear dynamical systems of composite structure from their input-output observations when a prior information about the system is poor, thus excluding the standard parametric approach to the problem. The multiresolution idea, being the fundamental concept of modern wavelet theory, is adopted and multiscale...
The aim of this paper is to present some contributions of a nonparametric statistical approach to the identification of uncertain dynamical systems. This method is particularly well adapted to biotechnological processes. We developed a semi-parametric filtering algorithm, able to estimate both non measured state variables and functional kinetic parameters without any a priori assumption on the modelling...
This paper proposes a new optimization strategy to estimate nitrifiable nitrogen concentration in wastewater, nitrification rate, denitrification rate and/or COD available for denitrification of an activated sludge process submitted to intermittent aeration. The approach uses the oxydo-reduction potential (ORP) and dissolved oxygen (DO) measurements only. The parameter identification is based on a...
In this paper the well-known problem of optimal input design is considered. The problem is formulated as follows: given a dynamic non-linear model structure which is assumed to be affine in the input, and a specific parameter of interest θk, find a feedback law that maximizes the sensitivity of the model output to the parameter. Analytical solutions to this specific problem for a general single state...
Human tracking across multiple cameras is highly demanded for large scale video surveillance. To successfully track human across multiple uncalibrated cameras that have no overlapping field of views, a system to train more reliable camera link models is proposed in this paper. We employ a novel approach of combining multiple camera links and building bidirectional transition time distribution in the...
In Frequency Modulated Continuous Wave (FMCW) radar, the Fast Fourier Transform (FFT) is very efficient in separating reflections from various range cells. For the subsequent determination of the Doppler spectrum, the FFT is less suitable. The raw FFT is very erratic, while the smoothed FFT is limited in the spectral shapes it can accurately describe. For determination of the Doppler spectrum, ARMAsel...
A method is presented that provides the exact equivalent representation of the periodogram by means of an MA(w-1) model. This method will be compared to inferring an MA(q) model directly from the data by estimating the parameters and selecting the optimal model order. Representing the periodogram by means of an MA(n-1) model, enables the use of The Prediction Error to make an exact quantitative comparison...
Mathematical modelling of cell culture processes is particularly useful for simulation, optimisation and control. Building models for such bioprocesses presents challenges at all stages of model development. In order to achieve model simplicity, the system of mass balances for the macroscopic species involved in a reaction scheme is generally used. However, the underlying reaction scheme is usually...
Simultaneous perturbation stochastic approximation (SPSA) is an optimization method which requires only a few objective function evaluations to obtain gradient information. In this paper, a first-order SPSA algorithm is described, which makes use of several numerical artifices, including adaptive gain sequences, gradient smoothing and a step rejection procedure, to enhance convergence and stability...
A new technique for nonlinear state and parameter estimation of the discrete time stochastic volatility models in which the logarithm of the asset return conditional variance follows an autoregressive model has been developed. The Gibbs sampling algorithm is used to construct a Markov-chain simulation tool that reflects both inherent model variability and parameter uncertainty. The proposed chain...
Closed loop output error identification algorithms and recently developed algorithm for direct closed loop estimation of reduced order controllers [3, 5] despite their diversity have in fact a unifying basic ground which will be enhanced. The paper also will explore the interaction between closed loop plant model identification and direct estimation in closed loop of reduced order controllers. The...
Location Based Social Networks (LBSNs) integrate location-based facilities with social connectivity for delivering a variety of services, enhancing user experience, emergency/ disaster management, and streamlining business processes. A number of recent research efforts have studied relationships between geolocation and social connectivity, social connectivity and preferences, and node attributes and...
This paper presents a new approach for shortterm load forecasting using the participatory learning paradigm. Participatory learning paradigm is a new training procedure that follows the human learning mechanism adopting an acceptance mechanism to determine which observation is used based upon its compatibility with the current beliefs. Here, participatory learning is used to train a class of hybrid...
The present study deals with the reconstruction of the continuous-time state space parameters proper of human quiet standing. The reconstruction utilized a hybrid non-linear extended Kalman filter to combine a biomechanical model with the discrete-time position measurements provided by two web-cameras via a General Linear Camera model. After camera calibration and validating the filter in simulation,...
Several methods have been proposed over the past few decades as a solution to the brain sources localization problem using EEG signals. In this paper the performances of different brain source localization techniques, including the Minimum Norm Estimates (MNE), Low Resolution Electrical Tomography (LORETA) and Multiple Sparse Priors (MSP), are assessed and compared. Due to the lack of the baseline,...
Conventional iris recognition using a full frontal iris image has reached a very high accuracy rate. In this paper, we focus on processing off-angle iris images. Previous research has shown that it is possible to correct off-angle iris images, but knowledge of the angle was needed. Very little work has focused on iris angle estimation which can be used for angle correction. In this paper, we describe...
Most phylogeny estimation systems such as SATe2 or DACTAL use fixed configurations and tools that make them suitable only for solving specific problems. Out of that scope, a hand-made combination of individual tools and methods has to be composed in order to get the desired phylogeny estimation. PhyloFlow is a new framework based on a workflow extendable to a wide range of tasks in phylogenetic analysis...
Intelligent Transportation Systems (ITS) have come a long way targeting problems such as increasing emissions and growing vehicle numbers. Current approaches address a variety of objectives including congestion management, collision avoidance, energy-efficiency and emission reduction. However, respective solutions typically are designed for and tailored to a predefined set of objectives. Consequently,...
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