This paper deals with modelling and adaptive output tracking of a Transverse Flux Permanent Magnet Machine (TFPM) as a non-linear system with unknown nonlinearities by utilizing High Gain Observer (HGO) and Radial Bas...
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This paper deals with modelling and adaptive output tracking of a Transverse Flux Permanent Magnet Machine (TFPM) as a non-linear system with unknown nonlinearities by utilizing High Gain Observer (HGO) and Radial Basis Function (RBF) networks. The technique of feedback linearization and H ∞ control are used to design an adaptive control law for compensating the unknown nonlinearity parts, such the effect of cogging torque, as a disturbance is decreased onto the rotor angle and angular velocity tracking performances. Finally, the capability of the proposed method is shown in the simulation results.
In many business process modelling situations using Petri nets, the resulting model does not have a single input place and a single output place. Therefore, the correctness of the model cannot be assessed within the e...
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In many business process modelling situations using Petri nets, the resulting model does not have a single input place and a single output place. Therefore, the correctness of the model cannot be assessed within the existing frameworks, which are devised for workflow nets - a particular class of Petri nets with a single input place and a single output place. Moreover, the existing approaches for tackling this problem are rather simplistic and they do not work even for simple examples. This paper shows that, by an appropriate reduction of a multiple input/multiple output Petri net, it is possible to use the existing techniques to check the correctness of the original process. The approach is demonstrated with an appropriate example.
This paper describes B-spline interpolation and compares it with other reconstruction methods, especially in three-dimensional space. We first consider the B-spline bases in the terms of convolution in signal processi...
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This paper describes B-spline interpolation and compares it with other reconstruction methods, especially in three-dimensional space. We first consider the B-spline bases in the terms of convolution in signal processing. The presented analysis requires careful usage of continuous and discrete representation of B-spline. Emphasis is given to the important difference between B-spline interpolation and approximation. The difference is shown through frequency domain analysis, so we derive frequency responses of the B-spline interpolation and approximation. We conclude by demonstrating the use of several reconstruction filters and appropriate gradient estimators in volume rendering. Exact reconstruction in volume visualization is very important in many industrial applications, such as material cavity control.
The volume data is generally in the form of the large array of numbers. In order to render the object hidden in the volumetric data, we need to reconstruct or interpolate data values between the samples. The novelty p...
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The volume data is generally in the form of the large array of numbers. In order to render the object hidden in the volumetric data, we need to reconstruct or interpolate data values between the samples. The novelty presented in this paper is B-spline interpolation in the volumetric space. We show that this approach is better than currently used methods. We also present a hybrid approach, analyze this approach in frequency domain and compare it to B-spline interpolation. To enhance the quality during the volume visualization process it is important to enhance the quality of the reconstruction. It is of crucial importance to explore different undesired effects. If better reconstruction is performed the more accurate result of volume visualization process is achieved.
In this paper we show a method of time series prediction with neural networks. In the first section we present the main elements of the problem and two standard methods that can be used for predictions. In the second ...
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Size modification of binary pictures can be mapped onto the CNN array using space variant linear templates. However, if all the parameters have to be set for each cell individually, then one of the CNN's main adva...
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Size modification of binary pictures can be mapped onto the CNN array using space variant linear templates. However, if all the parameters have to be set for each cell individually, then one of the CNN's main advantages will be lost in practice, the simple and quick parallel reprogrammability. In this paper, a general methodology is presented to derive the space variant templates of the complete weighting matrix from control pictures applying a simple nonlinear space invariant template. The straightforward design method presumes a modified CNN architecture (multiple input and specific nonlinear voltage-controlled current sources in every cell) and can be extended for a large class of sparse weighting matrices. Following this strategy the diminishment and enlargement process has been investigated using constant cell current and various bias maps in the transformations.
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