Camera calibration process is very sensitive to noise. Errors' coming from noisy measurements of the points' coordinates, their situation in the world and the calibration algorithm itself creates a deviated so...
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Camera calibration process is very sensitive to noise. Errors' coming from noisy measurements of the points' coordinates, their situation in the world and the calibration algorithm itself creates a deviated solution from the real one. Therefore, it is more interesting to obtain the standard deviation and the mean value of a parameter, than only a value. This value is always false because of the input data is corrupted with noise. This standard deviation represents the quality of the computed parameter. In this paper a method to compute this standard deviation of the projection matrix elements is presented. In this case, the projection matrix is computed with the non homogeneous solution based on the least squares technique.
The paper at hand describes a type of fedbatch experiment for parameter estimation of non-structured microbial growth models during growth of biomass in stirred-tank bioreactors. The solution is based on the stabiliza...
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The paper at hand describes a type of fedbatch experiment for parameter estimation of non-structured microbial growth models during growth of biomass in stirred-tank bioreactors. The solution is based on the stabilization of substrate concentration by partial state feedback.
In the present communication a modelling methodology for non-conventional sampled-data systems based on the external representation is proposed. This technique permits the consideration of any cyclic sampling pattern ...
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In the present communication a modelling methodology for non-conventional sampled-data systems based on the external representation is proposed. This technique permits the consideration of any cyclic sampling pattern on the sampled process. Non-conventional modelling is required when, due to several aspects (resource sharing, task priorities, etc), delays appear in a real-time control system. Then, these delays can be considered in the modelling step, and, in a later one, a non-conventional controller can be designed, which generates correct control actions according to the time instant in which they arrive to the process.
The synthesis of controllers with robust performance is addressed. Tracking specifications are defined by a family of reference models describing the desired frequency response. The synthesis problem is then formulate...
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The synthesis of controllers with robust performance is addressed. Tracking specifications are defined by a family of reference models describing the desired frequency response. The synthesis problem is then formulated as a set inclusion problem in the frequency domain. It is shown that the problem can be cast as a constraint satisfaction problem and solved by branch-and-prune algorithms. Different branching and pruning methodologies are explored and compared
This work presents a multi-rate fusion model, which exploits the complimentary properties of visual and inertial sensors for egomotion estimation in applications such as robot navigation and augmented reality. The sam...
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This work presents a multi-rate fusion model, which exploits the complimentary properties of visual and inertial sensors for egomotion estimation in applications such as robot navigation and augmented reality. The sampling of these two sensors is described with size-varying input and output equations without assumed synchronicity and periodicity of measurements. Data fusion is performed with two different multi-rate (MR) filter models, an extended (EKF) and an unscented Kalman filter (UKF). A complete dynamic model for the 6D-tracking task is given together with a method to calculate the dependencies of the covariance matrices. It is further shown that a centripetal acceleration model and the precise description of quaternion prediction for a constant velocity model highly improve the estimation error for rotary motions. The comparison demonstrates that the MR-UKF provides better estimation results at higher computational costs.
Distributed processing of measurement results poses a number of challenges. Among important requirements are security, performance, robustness and ability to withstand network failures, need for flexibility and elegan...
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ISBN:
(纸本)9781634391887
Distributed processing of measurement results poses a number of challenges. Among important requirements are security, performance, robustness and ability to withstand network failures, need for flexibility and elegance of a proposed solution. We propose a novel approach to distributed processing, which capitalises on locality of data access. We propose a distributed software architecture that leverages a flexible consistency checking engine and takes advantage of software agency features in order to meet the mentioned distributed processing requirements.
During last years, the time-triggered architecture (TTA) has been gaining acceptance as a generic architecture for highly dependable real-time systems. It is now being used to implement the "x-by-wire " conc...
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ISBN:
(纸本)9780769520858
During last years, the time-triggered architecture (TTA) has been gaining acceptance as a generic architecture for highly dependable real-time systems. It is now being used to implement the "x-by-wire " concept. A problem for this kind of systems is their validation. Fault injection has achieved a great acceptance among designers for the experimental validation of systems. This work describes the results and experiences obtained during the validation of the TTP/C controller, a communication controller based on the TTA. Two different fault-injection techniques have been used: VHDL-based fault injection and physical fault injection at pin level. Due to the access that each technique has to different parts of the system, they can complement each other, but moreover, some experiments can be reproduced using both techniques, being very helpful for the analysis of the results.
A comparison of two methods for selecting inputs in nonlinear models with mixed discrete (categorical) and continuous variables is presented. Both methods assume that an initial superset of potential regressors is giv...
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A comparison of two methods for selecting inputs in nonlinear models with mixed discrete (categorical) and continuous variables is presented. Both methods assume that an initial superset of potential regressors is given along with a set of data. In the first approach, the relevant inputs are selected by a model-free search algorithm using fuzzy clustering to quantize continuous data into subsets. The second approach employs regression trees as an induction algorithm 'wrapped' within a search method. The results obtained for two simulation examples and one real-world data set show that the fuzzy clustering-based method performs more consistently in selecting the model structure. Moreover, this method is much faster then the wrapper approach.
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