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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...
This paper proposes a new algorithm for object recognition with high precision and an efficient execution time using the Kinect sensor. The proposed algorithm is based on geometric hashing and provides a more detailed geometric description of objects that can be further used to identify object parts or other information localized on the object's surface. The algorithm was tested on a dataset that...
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...
We study the problem of building a sensor model for the purpose of simulation. Our work is motivated by the potential impact of realistic simulators on the development cycle of software for real robots. The case is made for building models from approximate state information, relieving the burden of ground truth. Unlike calibration, where the goal is to identify and remove error from a signal, our...
It has always been difficult to accurately estimate the moment of inertia of an object, e.g. an unmanned aerial vehicle (UAV). Whilst various offline estimation methods exist to allow accurate parametric estimation by minimizing an error cost function, they require large memory consumption, high computational effort, and a long convergence time. The initial estimate's accuracy is also vital in attaining...
The common-mode voltage induced by an external plane-wave field in the terminal loads of a bundle of twisted-wire pairs (TWPs) is investigated. In particular, a deterministic bundle is firstly considered (i.e., a bundle with TWPs running parallel each other), and the different factors determining the variability of the induced voltage with the position of the TWP in the bundle cross section are explained...
Underwater noise is a form of pollution causing significant concern in terms of environmental status. Shipping is considered the main contributor to the total noise at the global scale, since ship radiated noise can propagate up to tens or hundreds of kilometers. This paper reports on a shipping noise prediction tool based on Automatic Identification System (AIS) data and a normal-mode acoustic propagation...
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...
Abstract submitted for the special session Oceans of Tomorrow: AQUO and SONIC activities towards the ship noise characterisation, invited by Ruggero Dambra (ruggero.dambra@cetena.it). The potential impact that all types of vessel emissions, including underwater noise, could have on marine fauna has become an important issue during past decade. Surface vessels radiate underwater noise mainly due to...
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...
Conducting hardware experiment is often expensive in various aspects such as potential damage to the robot and the number of people required to operate the robot safely. Computer simulation is used in place of hardware in such cases, but it suffers from so-called simulation bias in which policies tuned in simulation do not work on hardware due to differences in the two systems. Model-free methods...
Dynamic Movement Primitives (DMPs) are a common method for learning a control policy for a task from demonstration. This control policy consists of differential equations that can create a smooth trajectory to a new goal point. However, DMPs only have a limited ability to generalize the demonstration to new environments and solve problems such as obstacle avoidance. Moreover, standard DMP learning...
A time series is usually decomposed as trend and irregular parts. Generally the trend part is treated by regression or filtering methods. There are some shortcomings associated with these methods, either the form of trend is too simple to represent some complex trend patterns, or no concrete trend formula is available. Considering the trend part as the inherent dynamics conveyed by the time series,...
This paper demonstrated how a 2D planar oversampled HF receive array manifold can be modeled by NEC and utilized to asses the performance of conventional and optimal beamforming techniques. It is shown that the full EM array model behaves similar to a planewave manifold model in the presence of strong non-isotropic external noise. Furthermore it is demonstrated that the optimal beamformer solutions...
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...
The paper deals with a new identification approach, based on a prediction error method, for multivariable errors-in-variables models (EIV). Starting from the ARMAX decomposition of MIMO EIV processes and congruence conditions between noisy sequences and the constraints of EIV representations, the simultaneous estimate of the model parameters and of the noise covariance matrices is obtained. Numerical...
Model predictive control (MPC) is the dominant control technology for constrained, multivariable chemical processes. Performance assessment of MPC controllers is an important problem that has received little attention. Most multivariable controller assessment techniques are based on comparing the actual performance to that achievable under minimum variance control (MVC). MVC measures not well suited...
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...
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