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This paper addresses the problem of joint estimation of time series of gene expressions and identification of the coefficients of gene interactions defining the network. The proposed method exploits a state-space structure describing the system so that a bank of particle filters can be used to efficiently track each of the time series separately. Since each gene interacts with some of the other genes,...
In this paper, we propose to address the moving average (MA) parameters estimation issue based only on noisy observations and without any knowledge on the variance of the additive stationary white Gaussian measurement noise. For this purpose, the MA process is approximated by a high-order AR process and its parameters are estimated by using an errors-in-variables (EIV) approach, which also makes it...
This paper presents an improvement of the existing interval symmetric single-step method ISS1 which will be called the interval midpoint symmetric single-step method IMSS1. The term ‘midpoint’ is referred to the updated midpoints used in every step in the method. The idea of midpoint will potentially reduce the time and improve the effectiveness of the method. This method is tested numerically in...
In this paper we use ultra-wideband (UWB) signals for the localization of blade tips on wind turbines. Our approach is to acquire two separate distances to each tip via time-delay estimation, and each tip is then localized by triangulation. We derive an approximate maximum a posteriori (MAP) delay estimator exploiting i) contextual prior information and ii) a direct-path approximation. The resulting...
Texture analysis is central in many image processing problems. It can be conducted by studying the local regularity fluctuations of image amplitudes, and multifractal analysis provides a theoretical and practical framework for such a characterization. Yet, due to the non Gaussian nature and intricate dependence structure of multifractal models, accurate parameter estimation is challenging: standard...
This work treats multi-pitch estimation, and in particular the common misclassification issue wherein the pitch at half of the true fundamental frequency, here referred to as a suboctave, is chosen instead of the true pitch. Extending on current methods which use an extension of the Group LASSO for pitch estimation, this work introduces an adaptive total variation penalty, which both enforce group-...
In this paper we combine the quadratic approximation approach used in derivative-free optimization (DFO) with iterative gradient-modification optimization (IGMO) to formulate an efficient scheme for iterative real-time optimization (RTO) under model uncertainty. By combining the robustness of the DFO approach to noisy data with the convergence to the true optimum of the IGMO using empirical gradients,...
In dynamical systems context, a predictor is a method that provides an estimation of the future system output using past information of the system. An interval predictor provides an outer estimation of the future output. The center of this interval can be used as central or nominal prediction. A method to formulate interval predictors is to assume an unknown but bounded error in the system measurements...
Recently, many sparse approximation methods have been applied to solve spectral unmixing problems. These methods in contrast to traditional methods for spectral unmixing are designed to work with large a-prori given spectral dictionaries containing hundreds of labelled material spectra enabling to skip the expensive endmember extraction and labelling step. However, it has been shown that sparse approximation...
Source estimation is a fundamental ingredient of Full Waveform Inversion (FWI). In such seismic inversion methods wavelet intensity and phase spectra are usually estimated statistically although for the FWI formulation as a nonlinear least-squares optimization problem it can naturally be incorporated to the workflow. Modern approaches for source estimation consider robust misfit functions leading...
Parallelizability of an algorithm is nowadays a highly desirable property as computer hardware is becoming increasingly parallel. In this paper, a formulation of the particle filtering algorithm, suitable for parallel or distributed computing, is proposed. From the particle set, a series expansion is fitted to the posterior probability density function. The global information provided by the particles...
Discrete-time estimation of rigid body attitude and angular velocity without any knowledge of the attitude dynamics model, is treated using the discrete Lagrange-d'Alembert principle. Using body-fixed sensor measurements of direction vectors and angular velocity, a Lagrangian is obtained as the difference between a kinetic energy-like term that is quadratic in the angular velocity estimation error...
An optimal estimation approach for distributed Manning roughness coefficient in an overland flow based on the adjoint method is here proposed. Through some appropriate assumptions and simplifications, the governing equation describing the system is derived from the first continuity equation of the well-known one-dimensional Saint-Venant equations. In this equation, the empirical Manning parameter...
This paper presents a data-driven control scheme to iteratively achieve the desired objective criterion with significant improvement of the convergence performance for linear-time-invariant (LTI) single-input-single-output (SISO) systems. The internal iterative behavior between the current parameter and the optimal parameter is firstly analyzed with mathematic expression. And a novel iterative law...
Moving Horizon Estimation (MHE) is a general method employed in many dynamic systems to monitor unmeasurable states. MHE can handle unavoidable physical constraints on the system by a constrained nonlinear optimization problem. However, since this approach requires repeated solving of the optimization problem, it is usually limited to slow-evolving, quasi-linear, low-order systems. In this work, we...
In this paper, we examine data-driven aspects of consensus networks influenced by a stubborn agent. In particular we show that the judicious placement of the stubborn agent can be achieved based on snapshots of the data generated by the network through estimating the appropriate eigenvector of the perturbed Laplacian matrix. The exact dynamic mode decomposition algorithm is employed for estimating...
The aim of this paper is to present a method for estimation of parametric faults in closed-loop systems. The key technology applied in this paper is coprime factorization of both the dynamic system as well as the feedback controller. Using the Youla-Jabr-Bongiorno-Kucera (YJBK) parameterization, it is shown that a certain matrix transfer function, the fault signature matrix, is an LFT (linear fractional...
We propose a method for computing bounds on output functionals of a class of time-dependent PDEs. To this end, we introduce barrier functionals for PDE systems. By defining appropriate unsafe sets and optimization problems, we formulate an output functional bound estimation approach based on barrier functionals. In the case of polynomial data, sum of squares (SOS) programming is used to construct...
Reinforcement learning (RL) in problems with large markov decision processes (MDPs) can result in many action policies. Over time, it becomes non-trivial computationally to search for action policies that are effective to the states. Function approximation can be used to generalize the action policies to improve the search efficiency. Based on the Adaptive Resonance Theory, FASON is a type of self-organizing...
To prevent shortage of storage space in a service system, an administrator usually set per-user quota as an upper limit of usable space for each user. To avoid service failure caused by resource exhaustion, the administrator tends to set a conservative quota value such as the storage capacity divided by the expected maximum number of users. In this research, we analyzed long-term storage usage history...
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