In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and ...In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and the fuzzy c-means methods are applied in cluster analysis to actual traffic data, which suggests that dividing the traffic flow into two or three clusters can best reflect intrinsic patterns of traffic flows. Such information is then taken as guidance in spline regression, thus significantly reducing the computational burden of estimating spline models. Spline regression is used to estimate the locations of knots and the coefficients of the model so that the global error can be minimized. Model analysis results demonstrate that the proposed spline models have better fitting and generalization capability than the conventional models. In addition, the new method is more flexible in terms of data fitting and can provide smoother traffic stream models.展开更多
A prototype model of the mean radius flow path of a four-stage, high speed 1 MWe axial steam turbine was optimized by using evolution algorithms, DE （differential evolution） algorithm in this case. Also the cost-ben...A prototype model of the mean radius flow path of a four-stage, high speed 1 MWe axial steam turbine was optimized by using evolution algorithms, DE （differential evolution） algorithm in this case. Also the cost-benefits of the optimization were inspected. The optimization was successfully performed but the accuracy of the optimization was slightly less than hoped when compared to the control modeling executed with the CFD （computational fluid dynamics）. The mentioned inaccuracy could have been hardly avoided because of problems with an initial presumption involving semi-empiric calculations and of the uncertainty concerning the absolute areas of qualification of the functions. This kind of algebraic modeling was essential for the success of the optimization because e.g. CFD-calculation could not have been done on each step of the optimization. During the optimization some problems occurred with the adequacy of the computer capacity and with finding a suitable solution that would keep the algorithms within mathematically allowable boundaries but would not restrict the progress of the opti- mization too much. The rest of the problems were due to the novelty of the application and problems with pre- ciseness when handling the areas of qualification of the functions. Although the accuracy of the optimization re- suits was not exactly in accordance with the objective, they did have a favorable effect on the designing of the turbine. The optimization executed with the help of the DE-algorithm got at least about 3.5 % more power out of the turbine which means about 150 000 ε cost-benefit per turbine in the form of additional electricity capacity.展开更多
基金The US National Science Foundation (No.BCS-0527508)
文摘In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and the fuzzy c-means methods are applied in cluster analysis to actual traffic data, which suggests that dividing the traffic flow into two or three clusters can best reflect intrinsic patterns of traffic flows. Such information is then taken as guidance in spline regression, thus significantly reducing the computational burden of estimating spline models. Spline regression is used to estimate the locations of knots and the coefficients of the model so that the global error can be minimized. Model analysis results demonstrate that the proposed spline models have better fitting and generalization capability than the conventional models. In addition, the new method is more flexible in terms of data fitting and can provide smoother traffic stream models.
基金Financially supported by the Finnish Funding Agency for Technology and Innovation (TEKES)
文摘A prototype model of the mean radius flow path of a four-stage, high speed 1 MWe axial steam turbine was optimized by using evolution algorithms, DE （differential evolution） algorithm in this case. Also the cost-benefits of the optimization were inspected. The optimization was successfully performed but the accuracy of the optimization was slightly less than hoped when compared to the control modeling executed with the CFD （computational fluid dynamics）. The mentioned inaccuracy could have been hardly avoided because of problems with an initial presumption involving semi-empiric calculations and of the uncertainty concerning the absolute areas of qualification of the functions. This kind of algebraic modeling was essential for the success of the optimization because e.g. CFD-calculation could not have been done on each step of the optimization. During the optimization some problems occurred with the adequacy of the computer capacity and with finding a suitable solution that would keep the algorithms within mathematically allowable boundaries but would not restrict the progress of the opti- mization too much. The rest of the problems were due to the novelty of the application and problems with pre- ciseness when handling the areas of qualification of the functions. Although the accuracy of the optimization re- suits was not exactly in accordance with the objective, they did have a favorable effect on the designing of the turbine. The optimization executed with the help of the DE-algorithm got at least about 3.5 % more power out of the turbine which means about 150 000 ε cost-benefit per turbine in the form of additional electricity capacity.
