The stochastic simulation algorithm （SSA） accurately depicts spatially homogeneous wellstirred chemically reacting systems with small populations of chemical species and properly represents noise, but it is often ab...The stochastic simulation algorithm （SSA） accurately depicts spatially homogeneous wellstirred chemically reacting systems with small populations of chemical species and properly represents noise, but it is often abandoned when modeling larger systems because of its computational complexity. In this work, a twin support vector regression based stochastic simulations algorithm （TS^3A） is proposed by combining the twin support vector regression and SSA, the former is a well-known robust regression method in machine learning. Numerical results indicate that this proposed algorithm can be applied to a wide range of chemically reacting systems and obtain significant improvements on efficiency and accuracy with fewer simulating runs over the existing methods.展开更多
The speed of a ship sailing in waves always slows down due to the decrease in efficiency of the propeller. So it is necessary and essential to analyze the unsteady hydrodynamic performance of propeller in waves. This ...The speed of a ship sailing in waves always slows down due to the decrease in efficiency of the propeller. So it is necessary and essential to analyze the unsteady hydrodynamic performance of propeller in waves. This paper is based on the numerical simulation and experimental research of hydrodynamics performance when the propeller is under wave conditions. Open-water propeller performance in calm water is calculated by commercial codes and the results are compared to experimental values to evaluate the accuracy of the numerical simulation method. The first-order Volume of Fluid(VOF) wave method in STAR CCM+ is utilized to simulate the three-dimensional numerical wave. According to the above prerequisite, the numerical calculation of hydrodynamic performance of the propeller under wave conditions is conducted, and the results reveal that both thrust and torque of the propeller under wave conditions reveal intense unsteady behavior. With the periodic variation of waves, ventilation, and even an effluent phenomenon appears on the propeller. Calculation results indicate, when ventilation or effluent appears, the numerical calculation model can capture the dynamic characteristics of the propeller accurately, thus providing a significant theory foundation forfurther studying the hydrodynamic performance of a propeller in waves.展开更多
A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decom...A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.展开更多
To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership functi...To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.展开更多
基金This work was supported by the National Natural Science Foundation of China （No.30871341）, the National High-Tech Research and Development Program of China （No.2006AA02-Z190）, the Shanghai Leading Academic Discipline Project （No.S30405）, and the Natural Science Foundation of Shanghai Normal University （No.SK200937）.
文摘The stochastic simulation algorithm （SSA） accurately depicts spatially homogeneous wellstirred chemically reacting systems with small populations of chemical species and properly represents noise, but it is often abandoned when modeling larger systems because of its computational complexity. In this work, a twin support vector regression based stochastic simulations algorithm （TS^3A） is proposed by combining the twin support vector regression and SSA, the former is a well-known robust regression method in machine learning. Numerical results indicate that this proposed algorithm can be applied to a wide range of chemically reacting systems and obtain significant improvements on efficiency and accuracy with fewer simulating runs over the existing methods.
基金Supported by the National Natural Science Foundation of China （51379043, 41176074, 51209048, 51409063）, High Tech Ship Research Project of Ministry of Industry and Technology （G014613002）, and the Support Plan for Youth Backbone Teachers of Harbin Engineering University （HEUCFQ 1408）
文摘The speed of a ship sailing in waves always slows down due to the decrease in efficiency of the propeller. So it is necessary and essential to analyze the unsteady hydrodynamic performance of propeller in waves. This paper is based on the numerical simulation and experimental research of hydrodynamics performance when the propeller is under wave conditions. Open-water propeller performance in calm water is calculated by commercial codes and the results are compared to experimental values to evaluate the accuracy of the numerical simulation method. The first-order Volume of Fluid(VOF) wave method in STAR CCM+ is utilized to simulate the three-dimensional numerical wave. According to the above prerequisite, the numerical calculation of hydrodynamic performance of the propeller under wave conditions is conducted, and the results reveal that both thrust and torque of the propeller under wave conditions reveal intense unsteady behavior. With the periodic variation of waves, ventilation, and even an effluent phenomenon appears on the propeller. Calculation results indicate, when ventilation or effluent appears, the numerical calculation model can capture the dynamic characteristics of the propeller accurately, thus providing a significant theory foundation forfurther studying the hydrodynamic performance of a propeller in waves.
文摘A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.
文摘To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.
