Objective To investigate impulsive noise suppression of medical volume data. Methods The volume data is represented as level sets and a special set operator is defined and applied to filtering it. The small connected ...Objective To investigate impulsive noise suppression of medical volume data. Methods The volume data is represented as level sets and a special set operator is defined and applied to filtering it. The small connected components, which are likely to be produced by impulsive noise, are eliminated after the filtering process. A fast algorithm that uses a heap data structure is also designed. Results Compared with traditional linear filters such as a Gaussian filter, this method preserves the fine structure features of the medical volume data while removing noise, and the fast algorithm developed by us reduces memory consumption and improves computing efficiency. The experimental results given illustrate the efficiency of the method and the fast algorithm. Conclusion The set operator-based method shows outstanding denoising properties in our experiment, especially for impulsive noise. The method has a wide variety of applications in the areas of volume visualization and high dimensional data processing.展开更多
In this paper, a robust fractional order fuzzy P ＋ fuzzy I ＋ fuzzy D （FOFP ＋ FOFI ＋ FOFD） controller is presented for a nonlinear and uncertain 2-1ink planar rigid manipulator. It is a nonlinear fuzzy controller...In this paper, a robust fractional order fuzzy P ＋ fuzzy I ＋ fuzzy D （FOFP ＋ FOFI ＋ FOFD） controller is presented for a nonlinear and uncertain 2-1ink planar rigid manipulator. It is a nonlinear fuzzy controller with variable gains that makes it self- adjustable or adaptive in nature. The fractional order operators further make it more robust by providing additional degrees of freedom to the design engineer. The integer order counterpart, fuzzy P ＋ fuzzy I ＋ fuzzy D （FP ＋ FI ＋ FD） controller, for a comparative study, was realized by taking the integer value for the fractional order operators in FOFP ＋ FOFI ＋ FOFD controller. The performances of both the fuzzy controllers are evaluated for reference trajectory tracking and disturbance rejection with and without model uncertainty and measurement noise. Genetic algorithm was used to optimize the parameters of controller under study for minimum integral of absolute error. Simulation results demonstrated that FOFP ＋ FOFI ＋ FOFD controller show much better performance as compared to its counterpart FP ＋ FI ＋ FD controller in servo as well as the regulatory problem and in model uncertainty and noisy environment FOFP ＋ FOFI ＋ FOFD controller demonstrated more robust behavior as compared to the FP ＋ FI ＋ FD controller. For the developed controller bounded-input and bounded-output stability conditions are also developed using Small Gain Theorem.展开更多
文摘Objective To investigate impulsive noise suppression of medical volume data. Methods The volume data is represented as level sets and a special set operator is defined and applied to filtering it. The small connected components, which are likely to be produced by impulsive noise, are eliminated after the filtering process. A fast algorithm that uses a heap data structure is also designed. Results Compared with traditional linear filters such as a Gaussian filter, this method preserves the fine structure features of the medical volume data while removing noise, and the fast algorithm developed by us reduces memory consumption and improves computing efficiency. The experimental results given illustrate the efficiency of the method and the fast algorithm. Conclusion The set operator-based method shows outstanding denoising properties in our experiment, especially for impulsive noise. The method has a wide variety of applications in the areas of volume visualization and high dimensional data processing.
文摘In this paper, a robust fractional order fuzzy P ＋ fuzzy I ＋ fuzzy D （FOFP ＋ FOFI ＋ FOFD） controller is presented for a nonlinear and uncertain 2-1ink planar rigid manipulator. It is a nonlinear fuzzy controller with variable gains that makes it self- adjustable or adaptive in nature. The fractional order operators further make it more robust by providing additional degrees of freedom to the design engineer. The integer order counterpart, fuzzy P ＋ fuzzy I ＋ fuzzy D （FP ＋ FI ＋ FD） controller, for a comparative study, was realized by taking the integer value for the fractional order operators in FOFP ＋ FOFI ＋ FOFD controller. The performances of both the fuzzy controllers are evaluated for reference trajectory tracking and disturbance rejection with and without model uncertainty and measurement noise. Genetic algorithm was used to optimize the parameters of controller under study for minimum integral of absolute error. Simulation results demonstrated that FOFP ＋ FOFI ＋ FOFD controller show much better performance as compared to its counterpart FP ＋ FI ＋ FD controller in servo as well as the regulatory problem and in model uncertainty and noisy environment FOFP ＋ FOFI ＋ FOFD controller demonstrated more robust behavior as compared to the FP ＋ FI ＋ FD controller. For the developed controller bounded-input and bounded-output stability conditions are also developed using Small Gain Theorem.
