The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, d...The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.展开更多
A method for simulation of cutting virtual soft tissue objects made of tetrahedron elements is developed. A linear isotropic elastic model is used for the soft tissue material properties and a tensor-mass model chosen...A method for simulation of cutting virtual soft tissue objects made of tetrahedron elements is developed. A linear isotropic elastic model is used for the soft tissue material properties and a tensor-mass model chosen for the physical deformation. The Verlet leapfrog method is used to perform time integration in solving the dynamic equations. Cutting is simulated by simply removing the tetrahedron elements that are intersected with the virtual scalpel. By making use of the spatial coherence, collision detection between soft tissue objects and the virtual scalpel is sped up. To facilitate the simulation, the soft tissue object is represented by linked lists of vertices, edges and tetra elements with pointers to the related neighboring features. The established software framework can serve as a base for the future development. Results of virtual experiments are shown and discussed. Possible future directions are also given.展开更多
Upper and lower bounds on peak-to-mean envelope power ratio (PMEPR) in OFDM systems are derived in this paper. The derivation results show that the upper bound on PMEPR only depends on the aperiodic autocorrdation fun...Upper and lower bounds on peak-to-mean envelope power ratio (PMEPR) in OFDM systems are derived in this paper. The derivation results show that the upper bound on PMEPR only depends on the aperiodic autocorrdation functions of the data sequences and is quite useful for rapid elimination of sequences that have PMEPR exceeding a given threshold. According to the lower bound on PMEPR,it has a great change as N (number of subcarriers) varies only for a very small N. For a 16-subcarrier BPSK-OFDM system, a selective mapping (SLM) way to reduce PMEPR is investigated with the upper bound on PMEPR and the distribution of PMEPR for all possible message sequences is given. The analytic results show that the maximal PMEPR is about 6.5 dB which is 5.5 dB less than that in worst situation.展开更多
The problem of direct adaptive neural network control for a class of uncertain nonlinear systems with unknown constant control gain is studied in this paper. Based on the supervisory control strategy and the approxima...The problem of direct adaptive neural network control for a class of uncertain nonlinear systems with unknown constant control gain is studied in this paper. Based on the supervisory control strategy and the approximation capability of multilayer neural networks (MNNs), a novel design scheme of direct adaptive neural network controller is proposed. The adaptive law of the adjustable parameter vector and the matrix of weights in the neural networks and the gain of sliding mode control term to adaptively compensate for the residual and the approximation error of MNNs is determined by using a Lyapunov method. The approach does not require the optimal approximation error to be square-integrable or the supremum of the optimal approximation error to be known. By theoretical analysis, the closed-loop control system is proven to be globally stable in the sense that all signals involved are bounded, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.展开更多
文摘The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.
文摘A method for simulation of cutting virtual soft tissue objects made of tetrahedron elements is developed. A linear isotropic elastic model is used for the soft tissue material properties and a tensor-mass model chosen for the physical deformation. The Verlet leapfrog method is used to perform time integration in solving the dynamic equations. Cutting is simulated by simply removing the tetrahedron elements that are intersected with the virtual scalpel. By making use of the spatial coherence, collision detection between soft tissue objects and the virtual scalpel is sped up. To facilitate the simulation, the soft tissue object is represented by linked lists of vertices, edges and tetra elements with pointers to the related neighboring features. The established software framework can serve as a base for the future development. Results of virtual experiments are shown and discussed. Possible future directions are also given.
文摘Upper and lower bounds on peak-to-mean envelope power ratio (PMEPR) in OFDM systems are derived in this paper. The derivation results show that the upper bound on PMEPR only depends on the aperiodic autocorrdation functions of the data sequences and is quite useful for rapid elimination of sequences that have PMEPR exceeding a given threshold. According to the lower bound on PMEPR,it has a great change as N (number of subcarriers) varies only for a very small N. For a 16-subcarrier BPSK-OFDM system, a selective mapping (SLM) way to reduce PMEPR is investigated with the upper bound on PMEPR and the distribution of PMEPR for all possible message sequences is given. The analytic results show that the maximal PMEPR is about 6.5 dB which is 5.5 dB less than that in worst situation.
文摘The problem of direct adaptive neural network control for a class of uncertain nonlinear systems with unknown constant control gain is studied in this paper. Based on the supervisory control strategy and the approximation capability of multilayer neural networks (MNNs), a novel design scheme of direct adaptive neural network controller is proposed. The adaptive law of the adjustable parameter vector and the matrix of weights in the neural networks and the gain of sliding mode control term to adaptively compensate for the residual and the approximation error of MNNs is determined by using a Lyapunov method. The approach does not require the optimal approximation error to be square-integrable or the supremum of the optimal approximation error to be known. By theoretical analysis, the closed-loop control system is proven to be globally stable in the sense that all signals involved are bounded, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.
