This work presents a novel parameter estimation approach for system modelling based on model decomposition. This approach uses Possible Conflicts to decompose the system model into minimal submodels that are used to o...
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ISBN:
(纸本)9781612848006
This work presents a novel parameter estimation approach for system modelling based on model decomposition. This approach uses Possible Conflicts to decompose the system model into minimal submodels that are used to obtain minimal parameter estimators for non-faulty situations. A laboratory plant was used to test the approach. The results obtained were compared against two classical parameter estimation techniques, the SQP optimization method and a curve-fitting approach using non-linear least squares. Both classical approaches use the global simulation model of the plant to carry out the optimization. The properties of the three techniques are presented and discussed. The developed parameter estimation approach improves the results obtained with the cited classical approaches.
Neutrophils must be removed from inflammatory sites for inflammation to resolve. Recent work in zebrafish has shown neutrophils can migrate away from inflammatory sites, as well as die in situ. The signals regulating ...
This work proposed an improvement for the visual odometry based on a new hardware design concept. Generally, a visual odometry system relies on one stereo camera to detect the 3D features which are used as input for t...
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This work proposed an improvement for the visual odometry based on a new hardware design concept. Generally, a visual odometry system relies on one stereo camera to detect the 3D features which are used as input for the ego motion estimation process. However, such design usually faced with the motion ambiguity problem resulting in poor estimation of the ego motion. The proposed multi-camera system solves this problem using an intuitive hardware design that enables a simple yet effective way to eliminate the motion ambiguity problems. Experiment results show that the proposed system yields better ego motion estimation compared to the conventional single camera system.
In this paper, we study the real controllability radius of higher-order linear time-invariant (LTI) systems, LTI descriptor systems, and time-delay LTI systems. The various radii are defined in terms of real parametri...
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A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-c...
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This paper investigates the problem of obtaining a state-space model of the disturbance evolution that precedes turbulent flow and the associated increase in skin-friction drag on aircraft surfaces. This problem is hi...
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A dynamical system can exhibit structure on multiple levels. Different system representations can capture different elements of a dynamical system's structure. We consider LTI input-output dynamical systems and pr...
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In proteomics 2-dimensional SDS-polyacrylamide gel electrophoresis (2D-PAGE) is the most widely used method for analyzing protein mixtures qualitatively. There are, however, a lot of noise and measurement biases which...
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ISBN:
(纸本)9789604742813
In proteomics 2-dimensional SDS-polyacrylamide gel electrophoresis (2D-PAGE) is the most widely used method for analyzing protein mixtures qualitatively. There are, however, a lot of noise and measurement biases which needs to be accounted for both in the localization of spots as well as in the quantitative measurement of protein expression. Previous techniques for denoising 2D gels are based on thresholding, smoothing and spot recognition. Wavelet transformations have also been applied to denoise 2D gels, however these techniques are typically in the frequency domain and they tend to shift spots slightly. In this paper, we improve the protein spot detection process by wavelet de-noising based on genetic algorithm.
Input variables selection plays a critical role in data-driven modelling, especially for complex systems with high dimensionality between the input/output space. In this paper, a new artificial neural network based fo...
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Input variables selection plays a critical role in data-driven modelling, especially for complex systems with high dimensionality between the input/output space. In this paper, a new artificial neural network based forward input selection scheme is proposed. The objective of the proposed scheme is to select the smallest number of important variables as model inputs, which will then be used for neural-fuzzy data modelling. The proposed input selection scheme is applied to a case study of Charpy impact energy prediction, with data extracted from an industrial database. Model performance has been compared with previous results where a much larger input set was used. Simulation results show that the number of inputs for the Charpy data model can be significantly reduced with little performance degradation. Also, the performance of the proposed scheme outperforms both the standard correlation analysis and fuzzy clustering based input selection schemes.
This paper deals with design of a synchronous frame control strategy for single-phase inverter-based islanded distributed generation (DG) systems. Although, implementation of these regulators requires a minimum of two...
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