Unilateral renal vein thrombosis and nephrotic syndrome.

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Mid-portion agenesis of corpus callosum in a presumed Baller-Gerold syndrome.

We report an association of trigonocephaly and thumb hypoplasia in a 6.5-year-old boy, diagnosed as Baller-Gerold syndrome. In addition to craniosynostosis and radial limb defect, which are constant in this syndrome, our patient presents two unusual features: the first is an epidermal nevus and the second is an agenesis of the middle portion of corpus callosum. This unique type of callosal agenesis in the context of a polymalformative disorder supports the hypothesis that partial agenesis of corpus callosum may be due to an event occurring before the 12th week gestation with continued development of the midline structures.Case ReportsJournal Articleinfo:eu-repo/semantics/publishe

Hysteresis identification using nonlinear state-space models

Most studies tackling hysteresis identification in the technical literature follow white-box approaches, i.e. they rely on the assumption that measured data obey a specific hysteretic model. Such an assumption may be a hard requirement to handle in real applications, since hysteresis is a highly individualistic nonlinear behaviour. The present paper adopts a black-box approach based on nonlinear state-space models to identify hysteresis dynamics. This approach is shown to provide a general framework to hysteresis identification, featuring flexibility and parsimony of representation. Nonlinear model terms are constructed as a multivariate polynomial in the state variables, and parameter estimation is performed by minimising weighted least-squares cost functions. Technical issues, including the selection of the model order and the polynomial degree, are discussed, and model validation is achieved in both broadband and sine conditions. The study is carried out numerically by exploiting synthetic data generated via the Bouc-Wen equations

Approaches to Fault Detection for Heating Systems Using CP Tensor Decompositions

Two new signal-based and one model-based fault detection methods using canonical polyadic (CP) tensor decomposition algorithms are presented, and application examples of heating systems are given for all methods. The first signal-based fault detection method uses the factor matrices of a data tensor directly, the second calculates expected values from the decomposed tensor and compares these with measured values to generate the residuals. The third fault detection method is based on multi-linear models represented by parameter tensors with elements computed by subspace parameter identification algorithms and data for different but structured operating regimes. In case of missing data or model parameters in tensor representation, an approximation method based on a special CP tensor decomposition algorithm for incomplete tensors is proposed, called the decompose-and-unfold method. As long as all relevant dynamics has been recorded, this method approximates – also from incomplete data – models for all operating regimes, which can be used for residual generation and fault detection, e.g. by parity equations

Nonlinear System Identification: An Overview of Common Approaches

International audienceNonlinear mathematical models are essential tools in various engineering and scientific domains, where more and more data are recorded by electronic devices. How to build nonlinear mathematical models essentially based on experimental data is the topic of this entry. Due to the large extent of the topic, this entry provides only a rough overview of some well-known results, from gray-box to black-box system identification