Self-tuning regulators with restricted inputs
Sensor network design for the estimation of spatially distributed processes
In a typical moving contaminating source identification problem, after some type of biological or chemical contamination has occurred, there is a developing cloud of dangerous or toxic material. In order to detect and localize the contamination source, a sensor network can be used. Up to now, however, approaches aiming at guaranteeing a dense region coverage or satisfactory network connectivity have dominated this line of research and abstracted away from the mathematical description of the physical...
Sensor network scheduling for identification of spatially distributed processes
The work treats the problem of fault detection for processes described by partial differential equations as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A simple node activation strategy is discussed for the design of a sensor network deployed in a spatial domain that is supposed to be used while detecting changes in the underlying parameters which govern the process evolution. The setting considered relates to a...
Sequential identification algorithm and controller choice for a certain class of distributed systems
Significance tests to identify regulated proteins based on a large number of small samples
Modern biology is interested in better understanding mechanisms within cells. For this purpose, products of cells like metabolites, peptides, proteins or mRNA are measured and compared under different conditions, for instance healthy cells vs. infected cells. Such experiments usually yield regulation or expression values – the abundance or absence of a cell product in one condition compared to another one – for a large number of cell products, but with only a few replicates. In order to distinguish...
Some algorithmic aspects of subspace identificationwith inputs
It has been experimentally verified that most commonly used subspace methods for identification of linear state-space systems with exogenous inputs may, in certain experimental conditions, run into ill-conditioning and lead to ambiguous results. An analysis of the critical situations has lead us to propose a new algorithmic structure which could be used either to test difficult cases andor to implement a suitable combination of new and old algorithms presented in the literature to help fixing the...
Some problems of system identification
Spectral analysis of ARMA processes by Prony's method
State-space realization of nonlinear control systems: unification and extension via pseudo-linear algebra
In this paper the tools of pseudo-linear algebra are applied to the realization problem, allowing to unify the study of the continuous- and discrete-time nonlinear control systems under a single algebraic framework. The realization of nonlinear input-output equation, defined in terms of the pseudo-linear operator, in the classical state-space form is addressed by the polynomial approach in which the system is described by two polynomials from the non-commutative ring of skew polynomials. This allows...
Stochastic Inverse Problem with Noisy Simulator. Application to aeronautical model
Inverse problem is a current practice in engineering where the goal is to identify parameters from observed data through numerical models. These numerical models, also called Simulators, are built to represent the phenomenon making possible the inference. However, such representation can include some part of variability or commonly called uncertainty (see [4]), arising from some variables of the model. The phenomenon we study is the fuel mass needed to link two given countries with a commercial...
Superposition of diffusions with linear generator and its multifractal limit process
In this paper a new multifractal stochastic process called Limit of the Integrated Superposition of Diffusion processes with Linear differencial Generator (LISDLG) is presented which realistically characterizes the network traffic multifractality. Several properties of the LISDLG model are presented including long range dependence, cumulants, logarithm of the characteristic function, dilative stability, spectrum and bispectrum. The model captures higher-order statistics by the cumulants. The relevance...
Superposition of Diffusions with Linear Generator and its Multifractal Limit Process
In this paper a new multifractal stochastic process called Limit of the Integrated Superposition of Diffusion processes with Linear differencial Generator (LISDLG) is presented which realistically characterizes the network traffic multifractality. Several properties of the LISDLG model are presented including long range dependence, cumulants, logarithm of the characteristic function, dilative stability, spectrum and bispectrum. The model captures higher-order statistics by the cumulants. The relevance...
Sweep coverage of discrete time multi-robot networks with general topologies
This paper addresses a sweep coverage problem of multi-robot networks with general topologies. To deal with environmental uncertainties, we present discrete time sweep coverage algorithms to guarantee the complete coverage of the given region by sweeping in parallel with workload partition. Moreover, the error between actual coverage time and the optimal time is estimated with the aid of continuous time results. Finally, numerical simulation is conducted to verify the theoretical results.