This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A ne...This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.展开更多
A high contrast to noise ratio(CNR)is always desirable for contrast-enhanced computed tomography angiography(CTA).To ensure a high CNR of the vascular images in CTA and potentially reduce the radiation exposure and co...A high contrast to noise ratio(CNR)is always desirable for contrast-enhanced computed tomography angiography(CTA).To ensure a high CNR of the vascular images in CTA and potentially reduce the radiation exposure and contrast usage,an adaptive bolus chasing method is proposed and evaluated compared to the existing constant-speed method.The proposed method is based on a local time and space parameter varying model of the contrast bolus.Optimal scan time for the next segment of the vasculature is estimated and predicted in real time and guides the computed tomography(CT)scanner table movement that guarantees that each segment of the vasculature is scanned with the maximum possible enhancement.Simulations and experimental results show that the proposed bolus chasing method outperforms the conventional constant-speed method substantially.展开更多
We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functi...We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.展开更多
In this paper, an agent-based simulation about knowledge transition associated with social impact in market is introduced. In the simulation, the genetic algorithm is used to generate the next generation products and ...In this paper, an agent-based simulation about knowledge transition associated with social impact in market is introduced. In the simulation, the genetic algorithm is used to generate the next generation products and a dynamic social impact model is used to simulate how customers are influenced by other customers. The simulation and its results not only show some features and patterns of knowledge transition, but also explore and display some phenomena of business cultures. On the basis of the innovation model of knowledge-based economy, the transition between technical knowledge and products knowledge is discussed, and a fuzzy linear quantification model which can be used to simulate the transition is introduced.展开更多
文摘This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.
基金The work was supported partially by NSF ECS-0555394 and NIH/NIBIB EB004287.
文摘A high contrast to noise ratio(CNR)is always desirable for contrast-enhanced computed tomography angiography(CTA).To ensure a high CNR of the vascular images in CTA and potentially reduce the radiation exposure and contrast usage,an adaptive bolus chasing method is proposed and evaluated compared to the existing constant-speed method.The proposed method is based on a local time and space parameter varying model of the contrast bolus.Optimal scan time for the next segment of the vasculature is estimated and predicted in real time and guides the computed tomography(CT)scanner table movement that guarantees that each segment of the vasculature is scanned with the maximum possible enhancement.Simulations and experimental results show that the proposed bolus chasing method outperforms the conventional constant-speed method substantially.
基金supported by National Natural Science Foundation of China (Grant No. 11271080)
文摘We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.
文摘In this paper, an agent-based simulation about knowledge transition associated with social impact in market is introduced. In the simulation, the genetic algorithm is used to generate the next generation products and a dynamic social impact model is used to simulate how customers are influenced by other customers. The simulation and its results not only show some features and patterns of knowledge transition, but also explore and display some phenomena of business cultures. On the basis of the innovation model of knowledge-based economy, the transition between technical knowledge and products knowledge is discussed, and a fuzzy linear quantification model which can be used to simulate the transition is introduced.
