Volume 13, Number 4, 2016
The frame rate of conventional vision systems is restricted to the video signal formats (e.g., NTSC 30 fps and PAL 25fps) that are designed on the basis of the characteristics of the human eye, which implies that the processing speed of these systems is limited to the recognition speed of the human eye.However, there is a strong demand for real-time high-speed vision sensors in many application fields, such as factory automation, biomedicine, and robotics, where high-speed operations are carried out.These high-speed operations can be tracked and inspected by using high-speed vision systems with intelligent sensors that work at hundreds of Hertz or more, especially when the operation is difficult to observe with the human eye.This paper reviews advances in developing real-time high speed vision systems and their applications in various fields, such as intelligent logging systems, vibration dynamics sensing, vision-based mechanical control, three-dimensional measurement/automated visual inspection, vision-based human interface, and biomedical applications.
In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.
This paper presents a compact analytical model for the organic field-effect transistors (OFETs), which describes two main aspects, the first one is related to the behavior in above threshold regime, while the other corresponds to the below threshold regime. The total drain current in the OFET device is calculated as the sum of two components, with the inclusion of a smooth transition function in order to take into account both regions using a single expression. A genetic algorithm based approach (GA) is investigated as a parameter extraction tool in the case of the compact OFET model to find the parameters' values from experimental data such as: mobility enhancement factor γ, threshold voltage VTh, subthreshold swing S, channel length modulation γ, and knee region sharpness m. The comparison of the developed current model with the experimental data shows a good agreement in terms of the transfer and the output characteristics. Therefore, the GA based approach can be considered as a competitive candidate compared to the direct method.
The paper presents a robust parallel distributed compensation (PDC) fuzzy controller for a nonlinear and certain system in continuous time described by the Takagi-Sugeno (T-S) fuzzy model. This controller is based on a new type of time-varying fuzzy sets (TVFS). These fuzzy sets are characterized by displacement of the kernels to the right or left of the universe of discourse, and they are directed by a well-defined criterion. In this work, we only focused on the movement of midpoint of the universe. The movements of this midpoint are optimized by particle swarm optimization (PSO) approach.
In this paper, operator-based nonlinear water temperature control for a group of three connected microreactors actuated by Peltier devices is proposed. To control the water temperature of tube in the microreactor, the temperature change of aluminum effects is considered. Therefore, the temperature change of aluminum becomes the part of an input of the tube. First, nonlinear thermal models of aluminum plates and tubes that structure the microreactor are obtained. Then, an operator based nonlinear water temperature control system for the microreactor is designed. Finally, the effectiveness of the proposed models and methods is confirmed by simulation and experimental results.
This paper presents a solution to tracking control problem for a class of nonlinear systems with unknown parameters and uncertain time-varying delays. A new adaptive neural network (NN) dynamic surface controller (DSC) is developed. Some assumptions on uncertain time delays, which were required to be satisfied in previous works, are removed by introducing a novel indirect neural network algorithm into dynamic surface control framework. Also, the designed controller is independent of the time delays. Moreover, the dynamic compensation terms are introduced to facilitate the controller design. It is shown that the closed-loop tracking error converges to a small neighborhood of zero. Finally, a chaotic circuit system is initially bench tested to show the effectiveness of the proposed method.