The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differenti...The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation （PDE） model, Kramer＇s PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer＇s PDE model. The study suggests that the Kramer＇s PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.展开更多
In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as...In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOlM where the polynomial renders to a unity coefficient or a special first-order internal model. By inserting the HOlM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.展开更多
The scheduling of gasoline-blending operations is an important problem in the oil refining industry. Thisproblem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but alsonon-convex ...The scheduling of gasoline-blending operations is an important problem in the oil refining industry. Thisproblem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but alsonon-convex nonlinear behavior, due to the blending of various materials with different quality properties.In this work, a global optimization algorithm is proposed to solve a previously published continuous-timemixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimi-zation, the distribution problem, and several important operational features and constraints. The algorithmemploys piecewise McCormick relaxation （PMCR） and normalized multiparametric disaggregation tech-nique （NMDT） to compute estimates of the global optimum. These techniques partition the domain of oneof the variables in a bilinear term and generate convex relaxations for each partition. By increasing the num-ber of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates ofthe global solution. The algorithm is compared to two commercial global solvers and two heuristic methodsby solving four examples from the literature. Results show that the proposed global optimization algorithmperforms on par with commercial solvers but is not as fast as heuristic approaches.展开更多
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...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.展开更多
Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to contr...Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization.This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control.To showcase this approach,five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system.This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges.These controllers also had reasonable per-iteration times of ca.0.1 s.This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which,in the face of process uncertainties or modelling limitations,allow rapid and stable control over wider operating ranges.展开更多
Modern times have shown that understanding infectious diseases is imperative to our survival.In this study,we gain an understanding of the SIR model for novel or emerging infectious diseases by deriving time-dependent...Modern times have shown that understanding infectious diseases is imperative to our survival.In this study,we gain an understanding of the SIR model for novel or emerging infectious diseases by deriving time-dependent solutions for the spreading profiles using a second-order approximation.We noticed that we got a solution that resembles the well-known soliton solution of the Korteweg-de Vries(KdV)equation.The KdV equation is a deterministic nonlinear partial differential equation that possesses a solitary wave solution known as a soliton.Using phase portrait analysis,we can show that the graphical time profile of the number of infected in the SIR model is qualitatively the same as the KdV wave profile.展开更多
Purpose–The purpose of this paper is to deal with the stabilization of the continuous-time TakagiSugeno(TS)fuzzy models by using their discretized models.Design/methodology/approach–In this case,a discrete model is...Purpose–The purpose of this paper is to deal with the stabilization of the continuous-time TakagiSugeno(TS)fuzzy models by using their discretized models.Design/methodology/approach–In this case,a discrete model is obtained from the discretization of the continuous TS fuzzy model.The gains obtained from a non-parallel distributed compensation controller ensuring the stabilization of the discrete model are used to check if the discrete control law used in the continuous time without any zero-order hold can stabilize the continuous TS model.Findings–This method is compared to another published method.Originality/value–Therefore,the originality of this paper consists in the fusion of the two continuous and discrete cases to obtain new stabilization conditions in the continuous case.Simulation examples show the interest of the proposed approach.展开更多
We develop a one-dimensional notion of affine processes under parameter uncertainty,which we call nonlinear affine processes.This is done as follows:given a setof parameters for the process,we construct a correspondin...We develop a one-dimensional notion of affine processes under parameter uncertainty,which we call nonlinear affine processes.This is done as follows:given a setof parameters for the process,we construct a corresponding nonlinear expectation on the path space of continuous processes.By a general dynamic programming principle,we link this nonlinear expectation to a variational form of the Kolmogorov equation,where the generator of a single affine process is replaced by the supremum over all corresponding generators of affine processes with parameters in.This nonlinear affine process yields a tractable model for Knightian uncertainty,especially for modelling interest rates under ambiguity.We then develop an appropriate Ito formula,the respective term-structure equations,and study the nonlinear versions of the Vasiˇcek and the Cox–Ingersoll–Ross(CIR)model.Thereafter,we introduce the nonlinear Vasicek–CIR model.This model is particularly suitable for modelling interest rates when one does not want to restrict the state space a priori and hence this approach solves the modelling issue arising with negative interest rates.展开更多
文摘The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation （PDE） model, Kramer＇s PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer＇s PDE model. The study suggests that the Kramer＇s PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.
基金supported by the General Program (No.60774022)the State Key Program of National Natural Science Foundation of China(No.60834001)the State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University (No.RCS2009ZT011)
文摘In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOlM where the polynomial renders to a unity coefficient or a special first-order internal model. By inserting the HOlM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.
基金Support by Ontario Research FoundationMc Master Advanced Control ConsortiumFundacao para a Ciência e Tecnologia(Investigador FCT 2013 program and project UID/MAT/04561/2013)
文摘The scheduling of gasoline-blending operations is an important problem in the oil refining industry. Thisproblem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but alsonon-convex nonlinear behavior, due to the blending of various materials with different quality properties.In this work, a global optimization algorithm is proposed to solve a previously published continuous-timemixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimi-zation, the distribution problem, and several important operational features and constraints. The algorithmemploys piecewise McCormick relaxation （PMCR） and normalized multiparametric disaggregation tech-nique （NMDT） to compute estimates of the global optimum. These techniques partition the domain of oneof the variables in a bilinear term and generate convex relaxations for each partition. By increasing the num-ber of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates ofthe global solution. The algorithm is compared to two commercial global solvers and two heuristic methodsby solving four examples from the literature. Results show that the proposed global optimization algorithmperforms on par with commercial solvers but is not as fast as heuristic approaches.
文摘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.
基金The authors thank the MOE AcRF Grant in Singapore for financial support of the projects on Precision Healthcare Development,Manufacturing and Supply Chain Optimization(Grant No.R-279-000-513-133)Advanced Process Control and Machine Learning Methods for Enhanced Continuous Manufacturing of Pharmaceutical Products(Grant No.R-279-000-541-114).
文摘Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization.This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control.To showcase this approach,five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system.This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges.These controllers also had reasonable per-iteration times of ca.0.1 s.This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which,in the face of process uncertainties or modelling limitations,allow rapid and stable control over wider operating ranges.
文摘Modern times have shown that understanding infectious diseases is imperative to our survival.In this study,we gain an understanding of the SIR model for novel or emerging infectious diseases by deriving time-dependent solutions for the spreading profiles using a second-order approximation.We noticed that we got a solution that resembles the well-known soliton solution of the Korteweg-de Vries(KdV)equation.The KdV equation is a deterministic nonlinear partial differential equation that possesses a solitary wave solution known as a soliton.Using phase portrait analysis,we can show that the graphical time profile of the number of infected in the SIR model is qualitatively the same as the KdV wave profile.
文摘Purpose–The purpose of this paper is to deal with the stabilization of the continuous-time TakagiSugeno(TS)fuzzy models by using their discretized models.Design/methodology/approach–In this case,a discrete model is obtained from the discretization of the continuous TS fuzzy model.The gains obtained from a non-parallel distributed compensation controller ensuring the stabilization of the discrete model are used to check if the discrete control law used in the continuous time without any zero-order hold can stabilize the continuous TS model.Findings–This method is compared to another published method.Originality/value–Therefore,the originality of this paper consists in the fusion of the two continuous and discrete cases to obtain new stabilization conditions in the continuous case.Simulation examples show the interest of the proposed approach.
文摘We develop a one-dimensional notion of affine processes under parameter uncertainty,which we call nonlinear affine processes.This is done as follows:given a setof parameters for the process,we construct a corresponding nonlinear expectation on the path space of continuous processes.By a general dynamic programming principle,we link this nonlinear expectation to a variational form of the Kolmogorov equation,where the generator of a single affine process is replaced by the supremum over all corresponding generators of affine processes with parameters in.This nonlinear affine process yields a tractable model for Knightian uncertainty,especially for modelling interest rates under ambiguity.We then develop an appropriate Ito formula,the respective term-structure equations,and study the nonlinear versions of the Vasiˇcek and the Cox–Ingersoll–Ross(CIR)model.Thereafter,we introduce the nonlinear Vasicek–CIR model.This model is particularly suitable for modelling interest rates when one does not want to restrict the state space a priori and hence this approach solves the modelling issue arising with negative interest rates.
