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The impedance modeling of passive device is mandatory for EMI prediction of power converter on printed board circuit (PCB) level. The black-box node-to-node impedance function (NIF) model, which extracts the connecting impedance matrix from measurement, can be used as the most general representation. However, the most recent development of this model is still based on the assumption of ideal shorting...
This paper proposes a neural network (NN) based noise prediction model for electric machines, applied to the case of synchronous reluctance motors (SynRMs). The natural frequencies of various vibration modes for a SynRM stator with generalized tooth geometry and slot numbers have been obtained using structural FEA based computations and then used to build a NN based surrogate model. The accuracy of...
Magnus rotor is essentially a rotating cylinder that generates lift due to Magnus effect. The effect has been generating curiosity in a wide variety of fields ranging from sports to alternate energy. The wind turbine under investigation replaces tradition blades that have an airfoil cross-section, with such Magnus rotors. In this paper, Magnus wind turbines with three and five rotors have been analyzed...
The Ensemble Kalman Filter (EnKF) is a Kalman based particle filter which was introduced to solve large scale data assimilation problems where the state space is of very large dimensionality. It also achieves good results when applied to a target tracking problem, however, due to its Gaussian assumption for the prior density, the performance can be improved by introducing Gaussian mixtures. In this...
The paper presents an informational technology for improving the reliability of nonlinear dynamic objects fault diagnosing using model-based diagnostics method of nonparametric identification. Diagnostic models build on the base of Volterra kernels moments. The efficiency of the proposed diagnostic models analyzes on an nonlinear dynamic objects simulation model.
This paper presents an activity detection system using dendrite threshold logic neuron models. This method generates a dendrite weight matrix from the background image and detect the changes in the subsequent images through the trained neuron outputs. Using only one layer of dendrite neuron cells with simplistic threshold logic cells, an accuracy of 98% is reported in realistic imaging conditions...
A measurement-based statistical verification approach is developed for systems with partly unknown dynamics. These grey-box systems are subject to identification experiments which, new in this contribution, enable accepting or rejecting system properties expressed in a linear-time logic. We employ a Bayesian framework for the computation of a confidence level on the properties and for the design of...
In the literature a lot of articles about so-called feedback-based trading strategies, i.e., strategies that compute investment exclusively from their own gain exist. Price is therein treated as a disturbance variable in the technical application. With these strategies, astonishing results may be shown. However, the so-called price taker property is always assumed, that means that one's own trading...
Procedural textures have been widely used as they can be easily generated from various mathematical models. However, the model parameters are not perceptually meaningful or uniform for non-expert users. In this paper, we proposed a system that can generate procedural textures interactively along certain perceptual dimensions. We built a procedural texture dataset and measured twelve perceptual properties...
The most common approach in Structural Health Monitoring (SHM) consists in performing accelerometric measures of the response of the monitored structures to natural or artificial stimuli (e.g. wind, urban traffic, seismic events etc.) and in modeling the dynamic behavior of the structure on the basis of these measures. The models can be used, in particular, to extract and compare the main modes i...
Inter-channel time-varying (TV) relationships of scalp neural recordings offer deep understanding of the brain sensory and cognitive functions. This paper develops a state space-based TV multivariate autoregressive (MVAR) model for estimating TV-information flow (IF) recruited by different motor imagery (MI) movements. TV model coefficients are computed through Kalman filter (KF) by incorporating...
It is difficult for least square method (LS) to deal with the ill-conditioned matrix of nonlinear polynomial model. In the case of the higher order of system, the matrix inversion is very complicated. A new approach based on LS is present which is combined with mirror-injection algorithm in order to obtain polynomial parameters identification of nonlinear system model. The columns of coefficient matrix...
Atrial fibrillation (Afib), the most common type of cardiac arrhythmia, arises from abnormal electrical activity of atrial membrane. Electrophysiological dynamics of human atrial tissue is represented by biophysically detailed models. Such models are really complex and hard to use for analysis purposes such as parameter estimation based on patient data. Hence, there is a need for simpler yet biophysically...
In this paper, we propose a prediction model for breathing pattern based on observations from CBCT raw projection images. From the raw CBCT projections the diaphragm apex position is measured, which in turn is used for the state estimation. We use a novel state space model followed by an Unscented Kalman Filter (UKF). Our method is compared with one of the successful models called Local Circular Motion...
The SONIC (Suppression Of underwater Noise Induced by Cavitation) and AQUO (Achieve QUieter Oceans by shipping noise footprint reduction) projects were awarded within the European Seventh Framework Program to develop tools to investigate and mitigate the effects of underwater sound generated by shipping activities on marine life. Model generated sound maps were identified by the European Commission...
Computational and experimental methods for the prediction of underwater-radiated noise due to propeller cavitation as in use at MARIN are discussed for three cases, viz. a cruise liner, a container vessel and a catamaran, all taken from EU project SONIC. The computational methods include the propeller flow panel method PROCAL, the tip vortex noise model ETV, and the sheet cavitation noise models due...
The performances of sixteen equation error methods for continuous-time system identification are compared through a simulation example with the CONTSID toolbox. The influence of the sampling period, the type of input signal (piece-wise constant or band-limited) and the noise (level and type: white/colored) is studied. The methods are then classed according to quantitative and qualitative criteria.
We address the problem of component fault detection and isolation in nonlinear dynamical systems. We consider parameterized nonlinear state-space models and, in the framework of the local approach we have developed, we investigate two possible solutions. The first one is based on state elimination and the use of an equivalent input-output model. The second one is based on state estimation and the...
Linear discrete time stochastic dynamical systems with switching parameters may represent a wide class of physical processes. Given the fact that, for non linear systems, the conditional density has no finite parametrization, optimal filters for this class of models are generally infinite dimensional. An exact recursive hybrid filter form has been recently found by Elliott and al [3]. The purpose...
This paper describes a method for additive abrupt fault detection and isolation. Parameter estimation is applied off line to obtain a discrete model. The contribution of sensor or actuator faults on the output error residual is analysed. Fault occurrence is detected using a Page-Hinkley algorithm. Then the parameters of several output error models of the residual are estimated. The analysis of these...
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