The paper presents formulation and implementation of Weighted Least Squares algorithm with moving measurement window for a grey box model parameters estimation purposes. The grey box of a biological reactor dynamics i...
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The paper presents formulation and implementation of Weighted Least Squares algorithm with moving measurement window for a grey box model parameters estimation purposes. The grey box of a biological reactor dynamics is used by Model Predictive controller to control integrated wastewater treatment system at medium time scale. The parameter estimation Weighted Least Squares algorithm is validated by simulation based on recently developed and calibrated model of integrated wastewater treatment system in Kartuzy, Poland.
This paper extends our previous work on constrained trajectory generation for UAVs (Unmanned Aerial Vehicles) with the task of fully covering an a priori known 3D structure. The novelty resides in the geometrical appr...
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This paper extends our previous work on constrained trajectory generation for UAVs (Unmanned Aerial Vehicles) with the task of fully covering an a priori known 3D structure. The novelty resides in the geometrical approach we adopt to describe sufficient coverage constraints in a mixed-integer formulation. The combined use of hyperplane arrangement, cell merging procedures and mixed-integer formulations provides feasible “viewpoint” regions through which the trajectory has to pass. The selected viewpoints with their corresponding feasible cells allow the vehicle to fully cover the 3D structure. The tools used and the results obtained are exemplified over a particular quadcopter system.
This paper presents recently introduced learning algorithm called extreme learning machine (ELM) for single-hidden layer feed-forward neural-networks (SLFNs) which randomly chooses hidden nodes and analytically determ...
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This paper presents recently introduced learning algorithm called extreme learning machine (ELM) for single-hidden layer feed-forward neural-networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. The ELM avoids problems like local minima, improper learning rate and over fitting commonly faced by iterative learning methods and completes the training very fast. We have evaluated the multicategory classification performance of ELM on five different data sets related to bioinformatics namely, the Breast Cancer Wisconsin data set, the Pima Diabetes data set, the Heart-Statlog data set, the Hepatitis data set and the Hypothyroid data set. A detailed analysis of different activation functions with varying number of neurons is also carried out which concludes that Algebraic Sigmoid function outperforms all other activation functions on these data sets. The evaluation results indicate that ELM produces better classification accuracy with reduced training time and implementation complexity compared to earlier implemented models.
3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolu...
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The aim of this work is to present an entropy-like Lyapunov function based dynamic feedback design technique for quasi-polynomial and Lotka-Volterra systems. It is shown, that the dynamic feedback design problem is eq...
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The aim of this work is to present an entropy-like Lyapunov function based dynamic feedback design technique for quasi-polynomial and Lotka-Volterra systems. It is shown, that the dynamic feedback design problem is equivalent to the feasibility of a bilinear matrix inequality. The problem is also formulated as a control Lyapunov function based feedback design when the Lyapunov function parameters are given, the solution of this problem can be obtained by solving a linear matrix inequality. The developed method is illustrated on a simple numerical example.
This paper describes the design of a modelling and simulation strategy for a laboratory-scale rolling mill in the G2 real-time-software. Also, it briefly presents factors necessary for improving the modelling and simu...
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This paper describes the design of a modelling and simulation strategy for a laboratory-scale rolling mill in the G2 real-time-software. Also, it briefly presents factors necessary for improving the modelling and simulation from a metallurgical view. The hybrid model base for the mill is designed from a combination of Object-Orientation, Rule-based Programming, GRAFCET paradigm, equations, and procedures.
Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number...
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A Markov-like weighted least squares (WLS) estimator is presented herein for harmonic sinusoidal parameter estimation. The estimator involves two distinct steps whereby it first obtains a set of initial parameter esti...
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A Markov-like weighted least squares (WLS) estimator is presented herein for harmonic sinusoidal parameter estimation. The estimator involves two distinct steps whereby it first obtains a set of initial parameter estimates that neglect the harmonic structure by some standard sinusoidal parameter estimation technique, and then the initial parameter estimates are refined via a WLS fit. It is found that the proposed estimator achieves similar performance to the optimal nonlinear least squares method when the signal-to-noise ratio (SNR) is moderate or high, but at a significantly reduced computational complexity. Furthermore, the former is shown to have a lower threshold SNR than the latter.
This paper deals with problems related to the computation of false alarm rate (FAR), the determination of thresholds and their integration in the design of observer-based fault detection systems. Different from the kn...
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ISBN:
(纸本)0780379241
This paper deals with problems related to the computation of false alarm rate (FAR), the determination of thresholds and their integration in the design of observer-based fault detection systems. Different from the known norm-based residual evaluation methods, the probabilistic robustness theory is applied for the purposes of calculating thresholds and FAR.
A controlsystemsengineering approach, employing a two-level overall system architecture and different but compatible formalisms for system representation on the upper and lower levels, has been investigated in detai...
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